1. Evolutionary Biology
  2. Structural Biology and Molecular Biophysics

Structurally distributed surface sites tune allosteric regulation

  1. James W McCormick
  2. Marielle AX Russo
  3. Samuel Thompson
  4. Aubrie Blevins
  5. Kimberly A Reynolds  Is a corresponding author
  1. The Green Center for Systems Biology, University of Texas Southwestern Medical Center, United States
  2. Department of Biophysics, University of Texas Southwestern Medical Center, United States
  3. Department of Bioengineering, Stanford University, United States
https://doi.org/10.7554/eLife.68346
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Cite this article
  1. James W McCormick
  2. Marielle AX Russo
  3. Samuel Thompson
  4. Aubrie Blevins
  5. Kimberly A Reynolds
(2021)
Structurally distributed surface sites tune allosteric regulation
eLife 10:e68346.
https://doi.org/10.7554/eLife.68346
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Abstract

Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation mutagenesis of a synthetic allosteric switch in which Dihydrofolate reductase (DHFR) is regulated by a blue-light sensitive LOV2 domain. Using a high-throughput assay wherein DHFR catalytic activity is coupled to E. coli growth, we assessed the impact of 1548 viable DHFR single mutations on allostery. Despite most mutations being deleterious to activity, fewer than 5% of mutations had a statistically significant influence on allostery. Most allostery disrupting mutations were proximal to the LOV2 insertion site. In contrast, allostery enhancing mutations were structurally distributed and enriched on the protein surface. Combining several allostery enhancing mutations yielded near-additive improvements to dynamic range. Our results indicate a path toward optimizing allosteric function through variation at surface sites.

eLife digest

Many proteins exhibit a property called ‘allostery’. In allostery, an input signal at a specific site of a protein – such as a molecule binding, or the protein absorbing a photon of light – leads to a change in output at another site far away. For example, the protein might catalyze a chemical reaction faster or bind to another molecule more tightly in the presence of the input signal. This protein ‘remote control’ allows cells to sense and respond to changes in their environment. An ability to rapidly engineer new allosteric mechanisms into proteins is much sought after because this would provide an approach for building biosensors and other useful tools. One common approach to engineering new allosteric regulation is to combine a ‘sensor’ or input region from one protein with an ‘output’ region or domain from another.

When researchers engineer allostery using this approach of combining input and output domains from different proteins, the difference in the output when the input is ‘on’ versus ‘off’ is often small, a situation called ‘modest allostery’. McCormick et al. wanted to know how to optimize this domain combination approach to increase the difference in output between the ‘on’ and ‘off’ states.

More specifically, McCormick et al. wanted to find out whether swapping out or mutating specific amino acids (each of the individual building blocks that make up a protein) enhances or disrupts allostery. They also wanted to know if there are many possible mutations that change the effectiveness of allostery, or if this property is controlled by just a few amino acids. Finally, McCormick et al. questioned where in a protein most of these allostery-tuning mutations were located.

To answer these questions, McCormick et al. engineered a new allosteric protein by inserting a light-sensing domain (input) into a protein involved in metabolism (a metabolic enzyme that produces a biomolecule called a tetrahydrofolate) to yield a light-controlled enzyme. Next, they introduced mutations into both the ‘input’ and ‘output’ domains to see where they had a greater effect on allostery.

After filtering out mutations that destroyed the function of the output domain, McCormick et al. found that only about 5% of mutations to the ‘output’ domain altered the allosteric response of their engineered enzyme. In fact, most mutations that disrupted allostery were found near the site where the ‘input’ domain was inserted, while mutations that enhanced allostery were sprinkled throughout the enzyme, often on its protein surface. This was surprising in light of the commonly-held assumption that mutations on protein surfaces have little impact on the activity of the ‘output’ domain. Overall, the effect of individual mutations on allostery was small, but McCormick et al. found that these mutations can sometimes be combined to yield larger effects.

McCormick et al.’s results suggest a new approach for optimizing engineered allosteric proteins: by introducing mutations on the protein surface. It also opens up new questions: mechanically, how do surface sites affect allostery? In the future, it will be important to characterize how combinations of mutations can optimize allosteric regulation, and to determine what evolutionary trajectories to high performance allosteric ‘switches’ look like.

Introduction

In allosteric regulation, protein activity is modulated by an input effector signal spatially removed from the active site. Allostery is a desirable engineering target because it can yield sensitive, reversible, and rapid control of protein activity in response to diverse inputs (Dagliyan et al., 2019; Pincus et al., 2017; Raman et al., 2014). One common approach for achieving allosteric regulation in both engineered and evolved systems is through domain insertion: the transposition, recombination, or otherwise fusion of an ‘input’ domain into an ‘output’ domain of interest (Aroul-Selvam et al., 2004; Dagliyan et al., 2016; Ostermeier and Benkovic, 2000; Nadler et al., 2016). In natural proteins, domain insertions and rearrangements play a key role in generating regulatory diversity, with kinases serving as a prototypical example (Fan et al., 2018; Huse and Kuriyan, 2002; Peisajovich et al., 2010; Shah et al., 2018). In engineered proteins, domain insertions have been used to generate fluorescent metabolite biosensors (Nadler et al., 2016), sugar-regulated TEM-1 β-lactamase variants (Guntas et al., 2005), and a myriad of light-controlled proteins including kinases, ion channels, guanosine triphosphatases, guanine exchange factors, and Cas9 variants (Dagliyan et al., 2016; Wang et al., 2016; Karginov et al., 2011; Toettcher et al., 2013; Shaaya et al., 2020; Coyote-Maestas et al., 2019; Richter et al., 2016). In all cases, domain insertion provides a powerful means to confer new regulation in a modular fashion.

However, naively created domain insertion chimeras sometimes exhibit relatively modest allosteric dynamic range, with small observed differences in activity between the constitutive and activated states (Lee et al., 2008). These fusions then require further optimization by either evolution or empirical mutagenesis, but general principles to guide this process are largely absent. Which mutations tune or improve an allosteric system? Because we lack comprehensive studies of allosteric mutational effects in either engineered or natural systems, it remains unclear whether such mutations are common or rare, and what magnitude of allosteric effect we might typically expect for single mutations. Additionally, it is not obvious if such mutations are structurally distributed or localized (for example, to the insertion site). Answers to these questions would inform practical strategies for optimizing engineered systems and provide insight into the evolution of natural multi-domain regulation in proteins.

To address these questions, we performed a deep mutational scan of a synthetic allosteric switch: a fusion between the E. coli metabolic enzyme Dihydrofolate Reductase (DHFR) and the blue-light sensing LOV2 domain from A. sativa (Lee et al., 2008; Reynolds et al., 2011). This modestly allosteric chimera shows a 30% increase in DHFR velocity in response to light. Focusing on mutations to the DHFR residues, we found that only a small fraction (4.4%) of the mutations that retained DHFR activity had a statistically significant impact on allostery. Individual mutations exhibited generally modest effect sizes; the most allosteric single mutant characterized (H124Q) yielded a twofold increase in velocity in response to light relative to the starting construct. Structurally, allostery disrupting mutations tended to cluster near the LOV2 insertion site and were modestly enriched at both conserved and co-evolving amino acid positions. In contrast, allostery enhancing mutations were distributed across the protein, and strongly associated with the protein surface. We observed that combining a few of these mutations yielded near-additive enhancements to allosteric dynamic range. Collectively, our data elucidates practical strategies for optimizing engineered systems, and shows that weakly conserved, structurally distributed surface sites can contribute to allosteric tuning.

Results

Characterization of an unoptimized allosteric fusion of DHFR-LOV2

To begin our study of allostery tuning mutations, we selected a previously characterized synthetic allosteric fusion between DHFR and LOV2 generated in earlier work (Lee et al., 2008; Reynolds et al., 2011). In this fusion, the LOV2 domain of A. sativa is inserted between residues 120 and 121 of the E. coli DHFR βF-βG loop; we refer to this construct as DL121 (Figure 1A,B). The choice of LOV2 insertion site was guided by Statistical Coupling Analysis (SCA), an approach for analyzing coevolution between pairs of amino acids across a homologous protein family (Rivoire et al., 2016; Lockless and Ranganathan, 1999; Halabi et al., 2009). A central finding of SCA is that co-evolving groups of amino acids, termed sectors, often form physically contiguous networks in the tertiary structure that link allosteric sites to active sites (Halabi et al., 2009; Süel et al., 2003; Pincus et al., 2018). To create the DL121 fusion, Lee et al. followed the guiding principle that sector connected surface sites in DHFR might serve as preferred sites (or ‘hot spots’) for the introduction of allosteric regulation (Lee et al., 2008). The resulting DL121 fusion covalently attaches the N- and C-termini of LOV2 into a sector connected surface on DHFR, and displays a twofold increase in DHFR hydride transfer rate (khyd) in response to blue light (Lee et al., 2008). Under steady-state conditions, we measured a 28% increase in the turnover number (kcat) in response to light and a statistically insignificant change in the Michaelis constant (Km) (Figure 1C). Thus, the DL121 fusion is modestly allosteric in vitro. As DHFR has no known natural allosteric regulation, the LOV2 insertion confers a new, evolutionarily unoptimized regulatory input.

Figure 1
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The DL121 DHFR/LOV2 fusion.

(A) Composite structures of the individual DHFR and LOV2 domains (PDB ID: 1R × 2 and 2V0U), indicating the LOV2 insertion site between positions 120 and 121 of DHFR (Sawaya and Kraut, 1997; Halavaty and Moffat, 2007). DHFR is in gray cartoon, NADP co-factor in green sticks, and folate substrate in yellow sticks. In LOV2 signaling, blue light triggers the formation of a covalent adduct between a cysteine residue (C450) and a flavin mononucleotide (FMN, yellow sticks) (Salomon et al., 2001; Crosson and Moffat, 2002; Swartz et al., 2002) and associated unfolding of the C-terminal Jα-helix (red cartoon); this order-to-disorder transition is used for regulation in several synthetic and natural systems (Pudasaini et al., 2015; Glantz et al., 2016). (B) DHFR loop conformational changes near the LOV2 insertion site. While the mechanism of DHFR regulation by LOV2 is currently unknown, inspecting the native DHFR structure provides some insight. The substrate-bound Michaelis complex of native DHFR is in the ‘closed’ conformation (gray cartoon), while the product ternary complex is in the ‘occluded’ state (purple cartoon). The βF-βG loop, where LOV2 is inserted, is highlighted in cyan. In native DHFR, hydrogen bonds between this loop (Asp122) and the Met20 loop (Gly15, Glu17) are thought to stabilize the closed conformation (Sawaya and Kraut, 1997; Schnell et al., 2004). Mutations to positions 121 and 122 reduce activity and cause the enzyme to prefer the occluded conformation (Cameron and Benkovic, 1997; Mhashal et al., 2018; Miller and Benkovic, 1998). (C) Steady state Michaelis Menten kinetics for the DL121 fusion under lit (blue) and dark (gray) conditions. The kcat of DHFR increases 28% in response to light; the difference in Km is statistically insignificant (Supplementary file 1a). Error bars represent standard deviation for three replicates. (D) Quantifying the allosteric effect of mutation. Allostery for the DL121 fusion is reported as the ratio between lit and dark velocity. The effect of a mutation on allostery is then computed as the ratio of mutant allostery to wt-DL121 allostery (bottom blue box).

But can this relatively small allosteric effect generate measurable physiological differences that could provide the basis for evolutionary selection? DHFR catalyzes the reduction of 7,8-dihydrofolate (DHF) to 5,6,7,8-tetrahydrofolate (THF) using NADPH as a co-factor. THF then serves as a one-carbon donor and acceptor in the synthesis of thymidine, purine nucleotides, serine, glycine, and methionine. Because of these critical metabolic functions, DHFR activity is strongly linked to growth rate, and under appropriate conditions, E. coli growth rate can be used as a proxy for DHFR activity (Reynolds et al., 2011; Thompson et al., 2020). Prior work found that the modest in vitro allosteric effect of DL121 conferred a selectable growth rate advantage in vivo: when an E. coli DHFR deletion strain (ER2566 ΔfolAΔthyA) was complemented with DL121, the resulting strain grew 17% faster in the light than in the dark (Reynolds et al., 2011). Thus, DL121 is a system where: (1) allosteric control is rapidly and reversibly applied, (2) the allosteric effects on activity can be readily quantified both in vitro and in vivo, and (3) there remains potential for large improvements in regulatory dynamic range through mutation.

A high-throughput assay to resolve small changes in DHFR catalytic activity

Our goal was to measure the effect of every single amino acid mutation in DHFR on the allosteric regulation of DL121. To do this, we aimed to follow a strategy loosely akin to a double mutant cycle (Figure 1D). The starting DL121 construct shows so-called V-type allostery, in which the effector (light) regulates the catalytic turnover number (kcat) (Carlson and Fenton, 2016). Thus, allostery can be quantified as the ratio of kcat between lit and dark states. More generally, allostery might be considered as a ratio of velocities (v = kcat [S]/(Km + [S])) between the lit and dark states, as the allosteric effector could regulate turnover, substrate affinity, or both. In either case, we defined the allosteric effect of mutation as the fold change in allosteric regulation upon mutation (Figure 1D, blue box). We sought to infer this quantity for every mutation in a saturation mutagenesis library of DHFR by using growth rate as a proxy for catalytic activity.

As in prior work, we measured the growth rate of many E. coli strains in parallel by using next generation sequencing (NGS) to monitor the frequency of individual DHFR mutants over time in a mixed culture (Figure 2; Reynolds et al., 2011; Thompson et al., 2020). Allele frequencies (fa) at each time point (t) were normalized as follows: fa=ln⁡NaNWTt-lnNaNWTt=0 where Na and NWT are the number of mutant and wildtype (WT) counts at a given time point. By performing a linear fit of the log normalized allele frequencies vs. time we calculated a slope corresponding to relative growth rate: this value is the difference in growth rate for the mutant relative to a reference ('WT') construct.

Figure 2
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A high-throughput, high-resolution assay for DHFR activity.

(A) The turbidostat. The instrument has 15 individual growth chambers (vials), positioned on a stir plate inside an incubator. Illumination was provided by blue LEDs in each vial holder. (B) Log-normalized relative allele frequency over time for 11 DHFR point mutations of known catalytic activity and the DL121 fusion. Allele frequency (colored circles) was determined by next-generation sequencing of mixed-population culture samples at each time point. All frequencies were normalized to t = 0 and WT DHFR (no LOV2 insertion). Error bars reflect standard error across four measurements, they are sometimes obscured by the marker. The slope for each line of best fit provides the growth rate of each mutant allele relative to WT DHFR. (C) Relative growth rate vs. log10(velocity) for the 11 DHFR mutants and DL121 as characterized in panel B. Color coding of mutations is matched to panel B. Error bars reflect standard error of the mean over four replicates. The dashed line was fit by linear regression to all mutants in the linear regime (M42F excluded).

As individual mutations tend to exhibit modest effects on allosteric regulation, we optimized the linear regime and resolution of the growth rate assay in two ways (Reynolds et al., 2011). First, we grew the E. coli populations in a turbidostat outfitted with blue LEDs to activate LOV2 (Figure 2A). The turbidostat maintains each culture in exponential growth by dynamically sensing optical density and adjusting media dilution rate accordingly Toprak et al., 2013; this ensures near-constant media conditions and eliminates the need for manual serial dilutions. Second, we selected media conditions – M9 minimal media with 0.4% glucose and 1 µg/ml thymidine supplementation – in which growth rate can resolve subtle differences in catalytic activity near the DL121 fusion. We evaluated the resolution of our assay using a ‘standard curve’ of 11 point mutations of known catalytic activity in non-chimeric DHFR (Figure 2B). Under these conditions, we observed a log-linear relationship between relative growth rate and DHFR velocity over nearly four orders of magnitude; this relationship saturates (plateaus) for the most active mutants (WT and M42F, Figure 2C). Importantly, the relative growth rate and velocity of DL121 were near the center of the linear regime of our assay.

In using velocity to describe our data, we have incorporated two assumptions: (1) we presume minimal variation in protein abundance between mutants (enzyme concentration is equal to one) and (2) we fix the substrate concentration at 25 µM, which was previously reported as the endogenous concentration for WT E. coli (Kwon et al., 2008). Individual mutations may cause variation in protein abundance, but because allostery concerns a relative change in activity, light-independent differences in abundance can be removed by appropriate normalization (as discussed further below).

As previously observed, the exponential divergence of mutants with different growth rates in a population makes it possible to detect even small biochemical effects (Breslow et al., 2008). More specifically, we can discriminate a change of ±0.02 µM−1 s-1 in catalytic power (kcat/Km) under these conditions. This level of precision is on par with – and in some cases better than – literature-reported errors for in vitro steady state kinetics measurements of DHFR (Reynolds et al., 2011; Wagner et al., 1992; Huang et al., 1994). Consequently, we can resolve small catalytic and allosteric effects of mutations on DL121 through this high-throughput growth-based assay.

Deleterious mutations are enriched at conserved, coevolving positions in DHFR

In order to map the coupling of individual DHFR positions to light, we constructed a deep mutational scanning library over all DHFR positions in the DL121 fusion (Figure 3—figure supplements 1–2). Then, we measured the growth rate effect of each mutation in triplicate under both lit and dark conditions using the above-described assay (Figure 3A–C, Figure 3—figure supplements 3–4, Figure 3—source data 1). In this experiment, all growth rates were calculated relative to the unmutated DL121 fusion, which itself exhibits reduced activity (and growth rate) compared to WT DHFR. Mutations fell into four broad categories in terms of growth rate effects: neutral, uniformly deleterious (Figure 3A), uniformly beneficial (Figure 3B), or light dependent (and thus allosteric, Figure 3C). We were unable to measure growth rate for 891 of the 3021 possible missense mutations (19 substitutions over 159 positions): 226 (7.5%) were missing at the start of the experiment (t = 0) for one or more replicates (referred to as ‘no data’), and an additional 665 (22%) were depleted from the library before reaching the minimum of three time points required for growth rate estimation (we refer to these as null mutants, see also Materials and methods, Figure 3—figure supplement 4). We interpreted these 665 rapidly depleting null mutants as highly deleterious to growth rate and thus DHFR activity. The relative growth rates for the remaining 2130 mutations (70.5%) were highly reproducible, with a correlation coefficient between replicate pairs above 0.9 (Figure 3—figure supplement 3).

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The effect of DL121 DHFR mutations on growth rate.

(A-C) Representative relative growth rate trajectories for three mutations. (A) DL121 D27N was deleterious in both lit and dark conditions. (B) DL121 D122W was advantageous under both lit and dark conditions. (C) DL121 E154R was deleterious in the dark, and near neutral in the light. Solid lines were obtained by linear regression; the slope of these provides the difference in growth rate relative to the unmutated DL121 construct. Relative growth rates were measured in triplicate for each mutant under lit (blue) and dark (gray) conditions. (D) Distribution of relative growth rates under dark conditions. The distribution for all mutations with measurable growth rate effects is in gray (‘null data’ and ‘no data’ excluded); the distribution for sector mutations is in navy. The relative growth rate of DL121 D27N, a mutation that severely disrupts catalytic activity, is indicated with a cyan dashed line. (E) The fraction of DL121 mutations with measurable growth rates that can be categorized as: DHFR surface, core, sector, and evolutionarily conserved (see Materials and methods for definitions). The fraction is shown for both the complete library (gray bars), and the library after removing mutations with low growth (growth rate <= DL121 D27N). The absolute number of mutations is shown above each bar. A contingency table summarizes the overlap between mutations in the sector (at a p-value cutoff of 0.010), and the mutations that yield low growth (growth rate <= DL121 D27N). (F) Structural distribution of positions enriched for mutations with growth rates as low as or lower than DL121 D27N (red spheres). The DHFR backbone is in gray cartoon, the folate substrate in yellow sticks, and the NADP co-factor in green sticks. (G) Relationship of the sector (navy blue surface) to positions enriched for growth-rate disrupting mutations (red spheres, same as in F).

Figure 3—source data 1

Relative growth rates under lit and dark conditions for DL121 point mutations as determined by next-generation sequencing.

Column 1 is the mutation name, columns 2–4 are relative growth rates in the light (three replicates), column 5 is the average lit relative growth rate, and column 6 is the standard deviation across lit replicates. Columns 7–9 are relative growth rates in the dark (three replicates), column 10 is the average dark relative growth rate, and column 11 is the standard deviation across dark replicates. Relative growth rate values of −999 indicate mutations with insufficient counts to fit a reliable growth rate (‘null data’), values of −1000 indicate mutations missing from the library at t = 0 (‘no data’), respectively.

https://cdn.elifesciences.org/articles/68346/elife-68346-fig3-data1-v2.csv

Before examining the allosteric effects of mutations, we first considered the effects of mutations on growth rate (and thus DHFR activity) in a single growth condition (dark). Prior work has found that deleterious mutations are enriched at evolutionarily conserved positions and within the protein sector (McLaughlin et al., 2012). The DHFR sector was defined by analyzing coevolution in a multiple sequence alignment of native DHFR domains, so we wished to examine if sector positions were indeed critical to function in the chimeric DL121 fusion. Good correspondence between the DHFR sector, evolutionary conservation, and deleterious mutations in DL121 would provide confidence that the core functional elements of native DHFR remain intact in the chimera. The vast majority of mutations were at least modestly deleterious to growth, with a median relative growth rate of −0.084 in the dark and −0.083 in the light (Figure 3D). A cluster of beneficial mutations was observed just before the LOV2 insertion site at position 121 in both conditions, suggesting some potential to compensate for the inserted LOV2 (Figure 3—figure supplement 4). The overall distribution of fitness effects shows some differences relative to prior DMS studies of natural proteins including native E. coli DHFR (Thompson et al., 2020; Garst et al., 2017). First, the distribution of fitness effects for mutations in natural proteins is often centered around neutral, implying a certain degree of mutational robustness (McLaughlin et al., 2012; Stiffler et al., 2015). Secondly, DMS of native DHFR – under experimental conditions designed to resolve mutational effects near WT – revealed many beneficial (activating) mutations (Thompson et al., 2020). There are two explanations for the relative paucity of beneficial and neutral mutations in the present dataset. First, the DL121 fusion is comparably less robust because the unoptimized LOV2 insertion introduces an initial compromise to DHFR function. Secondly, the conditions of our assay (both expression and media) differ from prior work (Thompson et al., 2020) and were selected to resolve mutational effects near DL121; consequently, mutations with native-like (or better) activity are in the saturating, non-linear regime of our assay.

To identify the slowest growing – and presumably near, or entirely, inactivating – mutations, we applied an empirical growth rate cutoff of −0.13 to the lit and dark growth rates. This corresponds to the growth rate for DL121 D27N; D27N is an active site mutation that strongly reduces the activity of WT DHFR (Figure 2B,C). The DL121 D27N mutant grows very slowly in the conditions of our assay and is inviable in the absence of thymidine supplementation (Figure 3—figure supplement 5). We found that mutations with growth rates at or below this cutoff (including the null mutants) were significantly enriched in both the sector (p=7.9×10−8, Figure 3E, Supplementary file 1b) and at evolutionarily conserved positions (p=8.7×10−20, Figure 3—figure supplement 6, Supplementary file 1c). When mapped to the WT DHFR structure, positions enriched for deleterious mutations surround the active site and co-factor binding pocket (Figure 3F), structurally overlap with the sector (Figure 3G), and include a number of positions known to play a critical role in WT DHFR catalysis (e.g. W22, D27, M42, and L54) (Howell et al., 1986; Fierke et al., 1987). These data are consistent with the view that sector positions continue to play a key role in conferring DHFR catalytic activity in the DL121 fusion.

Following the thinking that (near) inactive DHFR variants are both inherently non-allosteric and associated with the least reproducible growth rate measurements (Figure 3—figure supplement 3), we removed the set of 1247 slow-growing (growth rate <−0.13) and null mutations prior to the analysis of allostery. The retained 1548 mutations – representing 51% of the growth assay data – remain well-distributed between the DL121 surface, core, sector, and evolutionarily conserved positions (Figure 3E). These present a high-confidence and representative subset of the data for evaluating mutational effects on DL121 allosteric regulation.

Allostery tuning mutations are sparse

To compute the allosteric effect of mutation, we considered the triplicate measurements of lit and dark relative growth rate for each mutant (Figure 3A–C). Given the log-linear relationship between growth rate and DHFR velocity (Figure 2C), subtracting growth rates approximates log-ratios of velocities. Thus, we estimated the allosteric effect of mutation by taking the difference in the average relative growth rates between lit and dark conditions:

In the above equations, rgr is relative growth rate (which is directly measured in our sequencing-based assay) and gr refers to absolute growth rate. Accordingly, positive values indicate allostery enhancing mutations and negative values indicate allostery disrupting mutations (Figures 1D and 4A). Of the 1548 mutations evaluated, the allosteric effect is normally distributed with a mean near zero (µ = 0.0017, Figure 4—figure supplement 1). To assess the statistical significance of allosteric effects, we computed a p-value for each mutation by unequal variance t-test under the null hypothesis that the lit and dark replicate measurements have equal means. These p-values were compared to a multiple-hypothesis testing adjusted p-value of p=0.016 determined by Sequential Goodness of Fit (SGoF, Figure 4B; Carvajal-Rodriguez and de Uña-Alvarez, 2011). Under these criteria, only 69 mutations (4.5% of all viable mutants) significantly influenced allostery: 56 mutations enhanced allostery while 13 disrupted allostery. We did not observe a strong association between the magnitude of growth rate effect and the allosteric effect size. Allostery-influencing mutations spanned a wide range of growth rates and exhibited comparatively modest effects on light regulation (Figure 4C).

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The effect of DL121 DHFR mutations on allostery.

(A) Heatmap of mutational effects on allostery. Blue indicates allostery disrupting mutations, and red indicates allostery enhancing mutations. White squares with black outlines mark the WT residue at each position. Mutations missing from the library (‘no data’) are colored gray, and mutations that did not have sufficient sequencing counts for at least three time points (‘null data’) are colored navy. The LOV2 domain insertion site is indicated with a red star. (B) Volcano plot indicating the statistical significance of the light-dark growth rate difference (y-axis) as a function of relative growth rate difference (x-axis). p-Values were computed using a t-test across triplicate light and dark measurements. Individual points correspond to mutations; mutations on the left (yellow) side of the graph are allostery disrupting, while mutations on the right (blue) are allostery enhancing. Two cutoffs for statistical significance are indicated with dashed gray lines – both a standard value of p=0.05, and an adjusted p-value of 0.016, obtained by using Sequential Goodness of Fit (SGoF) to account for multiple hypothesis testing. Mutations selected for further in vitro experimental characterization are colored red and labeled. S148C and E154R did not yield sufficient quantities of active protein for further in vitro characterization. (C) Triplicate relative growth rate measurements under lit (blue) and dark (gray) conditions for all mutations with statistically significant allostery at the adjusted p-value (p<=0.016). The mutations are sorted by dark growth rate; mutations selected for in vitro characterization are marked with red asterisks. (D) Relationship between the allosteric effect as measured in vivo and in vitro. As we expect a log-linear relationship, we compare the ratio of velocity at 25 µM DHF (along x) to the exponent of the relative growth rate difference (along y). The relative growth rate difference under lit and dark conditions is the mean of triplicate measurements, error bars indicate SEM. All mutant effects on growth rate were measured in the same experiment (corresponding to a subset of the data in panel B) with the exception of DL121 C450S. The relative growth rate for this light-insensitive LOV2 mutant was measured in the ‘calibration curve’ experiment shown in Figure 2 (see also Materials and methods). The ratio between velocity in the light and velocity in the dark reflects the mean of triplicate measurements; error bars indicate SEM. The green line was fit by linear regression.

To further examine the ability of the growth-based sequencing assay to quantitatively resolve mutation-associated changes in allosteric regulation, we selected 10 mutations spanning a range of allosteric and growth rate effects for in vitro characterization (Figure 4B red dots, Figure 4—figure supplements 2–4). As a control, we included the light insensitive variant DL121-C450S: the C450S mutation of LOV2 abrogates light-based signaling by blocking formation of a light-induced covalent bond between position 450 and the FMN chromophore (Christie et al., 2002). We expressed and purified the selected DL121 mutants to near homogeneity; S148C and E154R did not yield sufficient quantities of active protein for in vitro studies. We find it noteworthy that E154R—one of the strongest allostery-enhancing mutations in vivo—was unstable in multiple purification strategies. For the remaining eight mutations we measured the kcat and Km of DHFR under lit and dark conditions (Figure 4—figure supplement 2). To confirm function of the fused LOV2 domain, we also measured relaxation of the FMN chromophore following light stimulation and collected absorbance spectra before and after the application of light (Figure 4—figure supplements 3–4). As expected, all the characterized DL121 mutations (with the exception of DL121-C450S) retained LOV2 domains with light-responsive absorbance spectra and chromophore relaxation constants similar to the unmutated DL121 construct. Evaluating the light dependence of DHFR activity, the change in Km value between lit and dark conditions was neither significant for any point mutation nor correlated to allosteric effect size (R2 = 0.003) (Supplementary file 1a, Figure 4—figure supplements 5–6). The Km values for all characterized mutants (0.15–1.9 µM) were similar to that of unmutated DL121 (~1 µM). Instead, we observed that light predominantly modulated catalytic turnover (kcat).The ratio of kcat in the light relative to the dark ranged from 1.1 (for the non-allosteric DL121-C450S construct) to 2.0 (for the most allosteric point mutation, H124Q) (Supplementary file 1a, Figure 4—figure supplements 5–6). For reference, the starting DL121 construct has a lit:dark kcat ratio of 1.3. So why might the characterized allosteric mutations predominantly effect kcat? One plausible explanation is that the conditions of our in vivo experiments fall within a pseudo-zero-order kinetics regime ([DHF]>>Km). In this scenario, light-associated changes in Km would have little impact on enzyme velocity (and accordingly growth rate) and go undetected in our assay. Consistent with this, the in vivo concentration of DHF for wildtype E. coli (25 µM) is well above the Km for all the characterized DL121 mutations. Alternatively, it could be that the biophysical mechanism of the DL121 fusion somehow makes it more energetically feasible for light to modulate kcat than Km. In any case, the 1.3- to 2-fold changes in kcat translate to similar fold changes in enzyme velocity. A comparison of the in vitro allosteric effect on velocity to the in vivo growth rate effect yields a near-linear relationship with a correlation coefficient of 0.83 (Figure 4D). Taken together, these data show that our growth-based assay is quantitatively reporting on changes in allostery, and that the allosteric mutations identified here modulate DHFR activity through changes in catalytic turnover number.

The structural pattern of allostery tuning mutations

Next, we examined the distribution of allostery-tuning mutations on the WT DHFR tertiary structure. The 13 allostery disrupting mutations localized to six DHFR positions concentrated near the LOV2 insertion site (Figure 5A). More specifically, 90% of the allostery disrupting mutations occurred within 10 Å of the DHFR 121 cα atom (Figure 5B). These mutations were modestly enriched in the protein sector (Supplementary file 1d). Overall, the observed spatial distribution suggests these mutations may disrupt allostery by altering local structural contacts needed to ensure communication between DHFR and LOV2.

Figure 5
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Structural distribution of allosteric mutations.

(A) Sites of allostery disrupting mutations (orange spheres). DHFR backbone is in gray cartoon, folate substrate in yellow sticks, and NADP co-factor in green sticks. (B) Fraction of mutations that enhance (blue), disrupt (orange), or do not significantly influence allostery (gray) as a function of distance to the LOV2 insertion site at DHFR position 121. Solid and dashed lines indicate mutations at either the p=0.016 and p=0.05 significance cutoffs for allostery, respectively. (C) Sites of allostery enhancing mutations (light blue spheres). (D) Contingency table summarizing the overlap between allostery enhancing mutations and mutations on the DHFR solvent accessible surface (considered as >25% relative solvent accessibility in the 1R × 2 PDB). (E) Sites of allostery enhancing (light blue spheres) and disrupting mutations (orange spheres) in the context of the sector (dark blue surface). (F) Contingency table summarizing the relationship between allostery enhancing mutations and sector mutations (sector defined at a p-value cutoff of 0.010). No allostery enhancing mutations occur within the sector.

In contrast to this localized pattern, the 56 allostery enhancing mutations were observed at 25 positions distributed across the DHFR structure (Figure 5C) and enriched on the protein surface (Figure 5D, Supplementary file 1e). These enhancing mutations were never found in the protein sector and were thus statistically significantly depleted from the protein sector (Figure 5E,F). This relationship – wherein allostery disrupting mutations were modestly enriched and allostery enhancing mutations were strongly depleted from the sector – also holds when defining the set of allosteric mutations at a relaxed cutoff of p=0.05 (Supplementary file 1d). Given the prior finding that sector connected surface sites were hotspots for introducing allostery in DHFR (Reynolds et al., 2011), we also examined the association between allostery-influencing mutations and two other groups of DHFR positions: (1) surface sites that are either within or contacting the sector and (2) surface sites that are only contacting the sector (but not within-sector). As for the analysis of sector positions only, we observed a statistically significant depletion of allostery enhancing mutations and enrichment of allostery disrupting mutations when considering the set of surface sites within or contacting the sector. This finding holds true over a range of significance thresholds for defining sector and allosteric mutations (Supplementary file 1f). When considering the set of positions that contact (but are not within) the sector, we did not observe a statistically significant association at nearly all cutoffs (Supplementary file 1g). Indeed, a number of allostery enhancing mutations do not contact the sector at all and occur in surface exposed loops (e.g. from residues 84 to 89, and from 116 to 119). So, counter to our expectations, the optimization of allostery did not occur at sector connected sites or even proximal to the LOV2 insertion site. Instead, structurally distributed and weakly conserved surface sites provided a basis for tuning and enhancing allosteric regulation regardless of sector connectivity.

Taken together, our data show that many distributed surface sites can make modest contributions to allosteric regulation. Can these mutants be combined to further improve allosteric dynamic range? To test this, we created two mutant constructs by combining the most potent allostery enhancing mutations as characterized in vitro: the double mutant DL121-M16A,H124Q, and the triple mutant DL121-M16A,D87A,H124Q (Figure 6A). For both constructs, we measured steady-state catalytic parameters (Supplementary file 1a) and verified LOV2 function through absorbance spectra and chromophore relaxation kinetics experiments (Figure 6—figure supplement 1). Interestingly, all three mutations exhibited near-log-additive improvements in allostery (Figure 6B). The DL121-M16A,H124Q fusion exhibits a 2.74 fold increase in velocity upon light activation while the triple mutant shows a 3.87-fold increase in velocity. For both mutant combinations, the improvement in allostery is realized by reducing the dark state (constitutive) activity (Figure 6—figure supplement 1, Supplementary file 1a). The serial addition of allostery enhancing mutations also reduced the overall catalytic activity of DHFR, suggesting that further improvement could be obtained by combining these mutations with a non-allosteric but activity-enhancing mutation. Overall, these data suggest that a naïve sector connected fusion can be gradually evolved toward increased allosteric dynamic range through the stepwise accumulation of single mutations at structurally distributed surface sites (Figure 6C).

Figure 6 with 1 supplement see all
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Combinatorial effect of allostery-enhancing mutations.

(A) Location of M16, D87, and H124 (blue spheres). The LOV2 insertion site, G121, is shown in red spheres. The DHFR backbone is in gray cartoon, the folate substrate in yellow sticks, and the NADP co-factor in green sticks. (B) The in vitro allosteric effect of the single, double and triple mutants. Included are the log-additive expectations (Expected) for the double and triple mutants given only the single mutation effects, and the experimentally measured effects (Observed). The ratio between velocity in the light and dark reflects the mean of triplicate measurements; error bars indicate SEM. There is not a statistically significant difference between the expected and observed allosteric effects (p=0.07 for M16A,H124Q, p=0.48 for M16A,D87A,H124Q; as computed by unpaired t-test). (C) Schematic whereby a novel domain insertion is iteratively optimized by surface residue variation.

Discussion

We used deep mutational scanning to study the frequency and structural pattern of allostery tuning mutations in a synthetic allosteric system, with the goal of understanding how regulation between domains can be optimized. Overall, allostery-influencing mutations were rare – just under 5% of viable mutations had statistically distinguishable effects on the lit and dark states of the DL121 fusion. We found that mutations at conserved and co-evolving (sector) positions were often deleterious to DHFR function and infrequently influenced allosteric regulation. In a few cases, sector mutations served to disrupt allostery; nearly all allostery disrupting mutations were localized to the LOV2 insertion site on DHFR. Counter to our expectations, allostery enhancing mutations were distributed across the DHFR structure, depleted from the sector, and enriched on the protein surface. When considered individually, the allostery-enhancing mutations had modest effects (up to twofold) on regulation, but (at least in some cases) they can be combined to yield near-additive improvements in dynamic range. A triple mutant (DL121-M16A,D87A,H124Q) rationally designed using our point mutant data produces a 3.87-fold increase in velocity upon light stimulation, up from the 1.3-fold allosteric effect of our starting construct.

These results should be considered in the context of our experiment: the DL121 fusion begins with sharply reduced DHFR activity, and our experiment intentionally used relatively stringent DHFR selection conditions to better resolve small differences in kinetic parameters. Thus, it is unsurprising that a large fraction of DHFR mutations in our library were deleterious, with an appreciable fraction near-inactive. This result echoes prior studies showing that the fraction of deleterious mutations (and mutational robustness) is strongly modulated by a variety of factors, including purifying selection strength and expression level (Stiffler et al., 2015; Jiang et al., 2013; Lundin et al., 2018). Given the finding that stabilizing mutations can often improve protein evolvability (Lundin et al., 2018; Bloom et al., 2006; Zheng et al., 2020), it would be interesting to examine how the distribution of mutational effects on both DL121 function and allostery would change in the background of a stability (and/or activity) enhancing mutation to DL121. While we observed that the number of allosteric mutations is few and the effect sizes are generally small in our model system, a previous study of allostery tuning mutations in pyruvate kinase indicated that up to 30% of mutations can tune allostery, with the maximum observed effect size approaching 22-fold (Tang and Fenton, 2017). Nevertheless, our data serve to illuminate the pattern of mutational effects on a newly established (and unoptimized) domain fusion – the presumptive first step toward regulation in a number of both natural and synthetic systems.

Interestingly, we observe a seeming disparity between the sites where we were able to introduce new allosteric regulation by domain fusion (in our earlier work), and the sites where allosteric tuning takes place (in this work). Previously, Reynolds et al. found that sector connected surface sites served as hotspots for the introduction of new light-based regulation in DHFR (Reynolds et al., 2011). Indeed, allosteric regulation was never obtained when the LOV2 domain was inserted at a non-sector connected site. In contrast, in this work, we observed that allostery enhancing mutations were depleted both within the sector and at sector connected sites. For example, we observed a number of allostery enhancing mutations at positions 83–89 of the DHFR αD-βE loop, while LOV2 insertions in this region location did not initiate allostery as quantified either in vitro or in vivo (Lee et al., 2008; Reynolds et al., 2011). This suggests different structural requirements for establishing and tuning allostery in this system (and possibly others): here allostery seems to be more easily introduced at evolutionarily conserved and co-evolving sites, but once established, can be optimized through less conserved sector-peripheral residues.

Although our work focuses on a synthetic allosteric fusion, our results are broadly consistent with an emerging body of work characterizing allostery-influencing mutations in natural proteins. Together, these data point to a model in which mutations at evolutionarily conserved positions exert large (and often disruptive) effects on function while allostery is tuned at less conserved surface sites. For example, Leander et al. recently used deep mutational scanning to map the pattern of compensatory mutations that rescued allosteric function for non-allosteric tetracycline repressor (TetR) variants (Leander et al., 2020). In that study a ‘disrupt-and-restore’ strategy was used: an already-allosteric system was inactivated and deep mutational scanning was then used to identify compensatory mutations. While there are significant differences between rescuing a deficient variant and the optimization of a novel allosteric construct, they likewise found that the mutations at highly conserved sites were often disruptive to stability and function, while allostery-rescuing mutations occurred at weakly conserved and structurally distributed sites (Leander et al., 2020). Similarly, mutations at ‘rheostat’ sites – weakly conserved positions distal to the site of regulation – were found to modulate allosteric control in human liver pyruvate kinase and the lactose repressor protein (lacI) (Campitelli et al., 2020; Wu et al., 2019). Intriguingly, the association of allostery enhancing mutations with the protein surface hints at a possible role for solvent – and more specifically the protein hydration layer – in tuning regulation.

The finding that the allostery initiated upon naïve fusion of the DHFR and LOV2 domains can be further enhanced by single mutations implies a path to improved allosteric dynamic range by stepwise mutagenesis and selection. Three of the most allostery enhancing mutations could be combined to yield a near-additive improvement in regulatory dynamic range. This has interesting implications for both evolved and engineered allosteric systems. In evolved systems, standing mutational variation is more likely at weakly conserved surface sites (particularly under less stringent selection conditions), and this could provide a means for generating variation in allosteric regulation upon a domain fusion event. Moreover, while engineering studies sometimes use mutations near the domain insertion site to optimize regulation, our results suggest that diffuse surface site mutations could present an effective alternative. Whether by engineering or evolution, it seems that mutations at weakly conserved and structurally distributed residues can provide a path to the optimization of regulation.

Materials and methods

Key resources table
Reagent type
(species) or resource		DesignationSource or referenceIdentifiersAdditional information
Gene (Escherichia coli)DHFR-LOV2 121Reynolds et al. Cell 2011 [20]Fusion of Escherichia coli DHFR and Avena sativa LOV2
Strain, strain background (Escherichia coli)BL21(DE3)New England BiolabsNEB #: C2527HCompetent cells
Strain, strain background (Escherichia coli)ER2566 ΔfolA ΔthyADr. Steven Benkovic, described in [20, 26]Competent cells
Strain, strain background (Escherichia coli)XL1-BlueAgilent TechnologiesCat. #:
200249		Competent cells
Recombinant DNA reagentpACYC-Duet_DL121_WTTS(plasmid)Reynolds et al. Cell 2011 [20]Addgene ID 171954Contains chimeric DL121 with TYMS (selection vector)
Recombinant DNA reagentpHIS8-3_DL121(plasmid)Reynolds et al. Cell 2011 [20]Addgene ID 171953Contains chimeric DL121 (expression vector)
Sequence-based reagentDL121_pos1_fwdThis PaperMutagenic PCR primerNNSATCAGTCTGATTGCGGCG
Sequence-based reagentDL121_pos2_fwdThis PaperMutagenic PCR primerNNSAGTCTGATTGCGGCGTTAG
Sequence-based reagentDL121_pos3_fwdThis PaperMutagenic PCR primerNNSCTGATTGCGGCGTTAGCG
Sequence-based reagentDL121_pos4_fwdThis PaperMutagenic PCR primerNNSATTGCGGCGTTAGCGGTA
Sequence-based reagentDL121_pos5_fwdThis PaperMutagenic PCR primerNNSGCGGCGTTAGCGGTAGAT
Sequence-based reagentDL121_pos6_fwdThis PaperMutagenic PCR primerNNSGCGTTAGCGGTAGATCGC
Sequence-based reagentDL121_pos7_fwdThis PaperMutagenic PCR primerNNSTTAGCGGTAGATCGCGTTATC
Sequence-based reagentDL121_pos8_fwdThis PaperMutagenic PCR primerNNSGCGGTAGATCGCGTTATCG
Sequence-based reagentDL121_pos9_fwdThis PaperMutagenic PCR primerNNSGTAGATCGCGTTATCGGCATG
Sequence-based reagentDL121_pos10_fwdThis PaperMutagenic PCR primerNNSGATCGCGTTATCGGCATGG
Sequence-based reagentDL121_pos11_fwdThis PaperMutagenic PCR primerNNSCGCGTTATCGGCATGGAAAA
Sequence-based reagentDL121_pos12_fwdThis PaperMutagenic PCR primerNNSGTTATCGGCATGGAAAACGC
Sequence-based reagentDL121_pos13_fwdThis PaperMutagenic PCR primerNNSATCGGCATGGAAAACGCC
Sequence-based reagentDL121_pos14_fwdThis PaperMutagenic PCR primerNNSGGCATGGAAAACGCCATG
Sequence-based reagentDL121_pos15_fwdThis PaperMutagenic PCR primerNNSATGGAAAACGCCATGCCG
Sequence-based reagentDL121_pos16_fwdThis PaperMutagenic PCR primerNNSGAAAACGCCATGCCGTGG
Sequence-based reagentDL121_pos17_fwdThis PaperMutagenic PCR primerNNSAACGCCATGCCGTGGAAC
Sequence-based reagentDL121_pos18_fwdThis PaperMutagenic PCR primerNNSGCCATGCCGTGGAACCTG
Sequence-based reagentDL121_pos19_fwdThis PaperMutagenic PCR primerNNSATGCCGTGGAACCTGCCT
Sequence-based reagentDL121_pos20_fwdThis PaperMutagenic PCR primerNNSCCGTGGAACCTGCCTGCC
Sequence-based reagentDL121_pos21_fwdThis PaperMutagenic PCR primerNNSTGGAACCTGCCTGCCGAT
Sequence-based reagentDL121_pos22_fwdThis PaperMutagenic PCR primerNNSAACCTGCCTGCCGATCTC
Sequence-based reagentDL121_pos23_fwdThis PaperMutagenic PCR primerNNSCTGCCTGCCGATCTCGCC
Sequence-based reagentDL121_pos24_fwdThis PaperMutagenic PCR primerNNSCCTGCCGATCTCGCCTGG
Sequence-based reagentDL121_pos25_fwdThis PaperMutagenic PCR primerNNSGCCGATCTCGCCTGGTTT
Sequence-based reagentDL121_pos26_fwdThis PaperMutagenic PCR primerNNSGATCTCGCCTGGTTTAAACGC
Sequence-based reagentDL121_pos27_fwdThis PaperMutagenic PCR primerNNSCTCGCCTGGTTTAAACGCAACA
Sequence-based reagentDL121_pos28_fwdThis PaperMutagenic PCR primerNNSGCCTGGTTTAAACGCAACAC
Sequence-based reagentDL121_pos29_fwdThis PaperMutagenic PCR primerNNSTGGTTTAAACGCAACACCTTAAATAAAC
Sequence-based reagentDL121_pos30_fwdThis PaperMutagenic PCR primerNNSTTTAAACGCAACACCTTAAATAAACCCG
Sequence-based reagentDL121_pos31_fwdThis PaperMutagenic PCR primerNNSAAACGCAACACCTTAAATAAACCCGTG
Sequence-based reagentDL121_pos32_fwdThis PaperMutagenic PCR primerNNSCGCAACACCTTAAATAAACCCGT
Sequence-based reagentDL121_pos33_fwdThis PaperMutagenic PCR primerNNSAACACCTTAAATAAACCCGTGATTATGG
Sequence-based reagentDL121_pos34_fwdThis PaperMutagenic PCR primerNNSACCTTAAATAAACCCGTGATTATGGG
Sequence-based reagentDL121_pos35_fwdThis PaperMutagenic PCR primerNNSTTAAATAAACCCGTGATTATGGGCC
Sequence-based reagentDL121_pos36_fwdThis PaperMutagenic PCR primerNNSAATAAACCCGTGATTATGGGCC
Sequence-based reagentDL121_pos37_fwdThis PaperMutagenic PCR primerNNSAAACCCGTGATTATGGGCC
Sequence-based reagentDL121_pos38_fwdThis PaperMutagenic PCR primerNNSCCCGTGATTATGGGCCGC
Sequence-based reagentDL121_pos39_fwdThis PaperMutagenic PCR primerNNSGTGATTATGGGCCGCCATAC
Sequence-based reagentDL121_pos40_fwdThis PaperMutagenic PCR primerNNSATTATGGGCCGCCATACCT
Sequence-based reagentDL121_pos41_fwdThis PaperMutagenic PCR primerNNSATGGGCCGCCATACCTGG
Sequence-based reagentDL121_pos42_fwdThis PaperMutagenic PCR primerNNSGGCCGCCATACCTGGGAA
Sequence-based reagentDL121_pos43_fwdThis PaperMutagenic PCR primerNNSCGCCATACCTGGGAATCG
Sequence-based reagentDL121_pos44_fwdThis PaperMutagenic PCR primerNNSCATACCTGGGAATCGATCGGT
Sequence-based reagentDL121_pos45_fwdThis PaperMutagenic PCR primerNNSACCTGGGAATCGATCGGT
Sequence-based reagentDL121_pos46_fwdThis PaperMutagenic PCR primerNNSTGGGAATCGATCGGTCGT
Sequence-based reagentDL121_pos47_fwdThis PaperMutagenic PCR primerNNSGAATCGATCGGTCGTCCG
Sequence-based reagentDL121_pos48_fwdThis PaperMutagenic PCR primerNNSTCGATCGGTCGTCCGTTG
Sequence-based reagentDL121_pos49_fwdThis PaperMutagenic PCR primerNNSATCGGTCGTCCGTTGCCA
Sequence-based reagentDL121_pos50_fwdThis PaperMutagenic PCR primerNNSGGTCGTCCGTTGCCAGGA
Sequence-based reagentDL121_pos51_fwdThis PaperMutagenic PCR primerNNSCGTCCGTTGCCAGGACGC
Sequence-based reagentDL121_pos52_fwdThis PaperMutagenic PCR primerNNSCCGTTGCCAGGACGCAAA
Sequence-based reagentDL121_pos53_fwdThis PaperMutagenic PCR primerNNSTTGCCAGGACGCAAAAATATTATCC
Sequence-based reagentDL121_pos54_fwdThis PaperMutagenic PCR primerNNSCCAGGACGCAAAAATATTATCCTGAG
Sequence-based reagentDL121_pos55_fwdThis PaperMutagenic PCR primerNNSGGACGCAAAAATATTATCCTGAGCTC
Sequence-based reagentDL121_pos56_fwdThis PaperMutagenic PCR primerNNSCGCAAAAATATTATCCTGAGCTCACAA
Sequence-based reagentDL121_pos57_fwdThis PaperMutagenic PCR primerNNSAAAAATATTATCCTGAGCTCACAACCGG
Sequence-based reagentDL121_pos58_fwdThis PaperMutagenic PCR primerNNSAATATTATCCTGAGCTCACAACCGGGTA
Sequence-based reagentDL121_pos59_fwdThis PaperMutagenic PCR primerNNSATTATCCTGAGCTCACAACCG
Sequence-based reagentDL121_pos60_fwdThis PaperMutagenic PCR primerNNSATCCTGAGCTCACAACCG
Sequence-based reagentDL121_pos61_fwdThis PaperMutagenic PCR primerNNSCTGAGCTCACAACCGGGT
Sequence-based reagentDL121_pos62_fwdThis PaperMutagenic PCR primerNNSAGCTCACAACCGGGTACG
Sequence-based reagentDL121_pos63_fwdThis PaperMutagenic PCR primerNNSTCACAACCGGGTACGGAC
Sequence-based reagentDL121_pos64_fwdThis PaperMutagenic PCR primerNNSCAACCGGGTACGGACGAT
Sequence-based reagentDL121_pos65_fwdThis PaperMutagenic PCR primerNNSCCGGGTACGGACGATCGC
Sequence-based reagentDL121_pos66_fwdThis PaperMutagenic PCR primerNNSGGTACGGACGATCGCGTA
Sequence-based reagentDL121_pos67_fwdThis PaperMutagenic PCR primerNNSACGGACGATCGCGTAACG
Sequence-based reagentDL121_pos68_fwdThis PaperMutagenic PCR primerNNSGACGATCGCGTAACGTGG
Sequence-based reagentDL121_pos69_fwdThis PaperMutagenic PCR primerNNSGATCGCGTAACGTGGGTG
Sequence-based reagentDL121_pos70_fwdThis PaperMutagenic PCR primerNNSCGCGTAACGTGGGTGAAG
Sequence-based reagentDL121_pos71_fwdThis PaperMutagenic PCR primerNNSGTAACGTGGGTGAAGTCGG
Sequence-based reagentDL121_pos72_fwdThis PaperMutagenic PCR primerNNSACGTGGGTGAAGTCGGTG
Sequence-based reagentDL121_pos73_fwdThis PaperMutagenic PCR primerNNSTGGGTGAAGTCGGTGGAT
Sequence-based reagentDL121_pos74_fwd2This PaperMutagenic PCR primerNNSGTGAAGTCGGTGGATGAAG
Sequence-based reagentDL121_pos75_fwdThis PaperMutagenic PCR primerNNSAAGTCGGTGGATGAAGCAATTG
Sequence-based reagentDL121_pos76_fwdThis PaperMutagenic PCR primerNNSTCGGTGGATGAAGCAATTGC
Sequence-based reagentDL121_pos77_fwdThis PaperMutagenic PCR primerNNSGTGGATGAAGCAATTGCGG
Sequence-based reagentDL121_pos78_fwdThis PaperMutagenic PCR primerNNSGATGAAGCAATTGCGGCG
Sequence-based reagentDL121_pos79_fwdThis PaperMutagenic PCR primerNNSGAAGCAATTGCGGCGTGT
Sequence-based reagentDL121_pos80_fwdThis PaperMutagenic PCR primerNNSGCAATTGCGGCGTGTGGT
Sequence-based reagentDL121_pos81_fwdThis PaperMutagenic PCR primerNNSATTGCGGCGTGTGGTGAC
Sequence-based reagentDL121_pos82_fwdThis PaperMutagenic PCR primerNNSGCGGCGTGTGGTGACGTAC
Sequence-based reagentDL121_pos83_fwdThis PaperMutagenic PCR primerNNSGCGTGTGGTGACGTACCA
Sequence-based reagentDL121_pos84_fwdThis PaperMutagenic PCR primerNNSTGTGGTGACGTACCAGAAATCAT
Sequence-based reagentDL121_pos85_fwdThis PaperMutagenic PCR primerNNSGGTGACGTACCAGAAATCATGG
Sequence-based reagentDL121_pos86_fwdThis PaperMutagenic PCR primerNNSGACGTACCAGAAATCATGGTGATTG
Sequence-based reagentDL121_pos87_fwdThis PaperMutagenic PCR primerNNSGTACCAGAAATCATGGTGATTGGC
Sequence-based reagentDL121_pos88_fwdThis PaperMutagenic PCR primerNNSCCAGAAATCATGGTGATTGGC
Sequence-based reagentDL121_pos89_fwdThis PaperMutagenic PCR primerNNSGAAATCATGGTGATTGGCGG
Sequence-based reagentDL121_pos90_fwdThis PaperMutagenic PCR primerNNSATCATGGTGATTGGCGGC
Sequence-based reagentDL121_pos91_fwdThis PaperMutagenic PCR primerNNSATGGTGATTGGCGGCGGC
Sequence-based reagentDL121_pos92_fwdThis PaperMutagenic PCR primerNNSGTGATTGGCGGCGGCCGC
Sequence-based reagentDL121_pos93_fwdThis PaperMutagenic PCR primerNNSATTGGCGGCGGCCGCGTT
Sequence-based reagentDL121_pos94_fwdThis PaperMutagenic PCR primerNNSGGCGGCGGCCGCGTTTAT
Sequence-based reagentDL121_pos95_fwdThis PaperMutagenic PCR primerNNSGGCGGCCGCGTTTATGAA
Sequence-based reagentDL121_pos96_fwdThis PaperMutagenic PCR primerNNSGGCCGCGTTTATGAACAGTT
Sequence-based reagentDL121_pos97_fwdThis PaperMutagenic PCR primerNNSCGCGTTTATGAACAGTTCTTGC
Sequence-based reagentDL121_pos98_fwdThis PaperMutagenic PCR primerNNSGTTTATGAACAGTTCTTGCCAAAAGCGC
Sequence-based reagentDL121_pos99_fwdThis PaperMutagenic PCR primerNNSTATGAACAGTTCTTGCCAAAAGCGCAAA
Sequence-based reagentDL121_pos100_fwdThis PaperMutagenic PCR primerNNSGAACAGTTCTTGCCAAAAGCGCAAAAGC
Sequence-based reagentDL121_pos101_fwdThis PaperMutagenic PCR primerNNSCAGTTCTTGCCAAAAGCGCAAAAGCTTT
Sequence-based reagentDL121_pos102_fwdThis PaperMutagenic PCR primerNNSTTCTTGCCAAAAGCGCAAAAG
Sequence-based reagentDL121_pos103_fwdThis PaperMutagenic PCR primerNNSTTGCCAAAAGCGCAAAAGC
Sequence-based reagentDL121_pos104_fwdThis PaperMutagenic PCR primerNNSCCAAAAGCGCAAAAGCTTTATCTG
Sequence-based reagentDL121_pos105_fwdThis PaperMutagenic PCR primerNNSAAAGCGCAAAAGCTTTATCTGACG
Sequence-based reagentDL121_pos106_fwdThis PaperMutagenic PCR primerNNSGCGCAAAAGCTTTATCTGACG
Sequence-based reagentDL121_pos107_fwdThis PaperMutagenic PCR primerNNSCAAAAGCTTTATCTGACGCATATCGAC
Sequence-based reagentDL121_pos108_fwdThis PaperMutagenic PCR primerNNSAAGCTTTATCTGACGCATATCGAC
Sequence-based reagentDL121_pos109_fwdThis PaperMutagenic PCR primerNNSCTTTATCTGACGCATATCGACGC
Sequence-based reagentDL121_pos110_fwdThis PaperMutagenic PCR primerNNSTATCTGACGCATATCGACGCA
Sequence-based reagentDL121_pos111_fwdThis PaperMutagenic PCR primerNNSCTGACGCATATCGACGCAG
Sequence-based reagentDL121_pos112_fwdThis PaperMutagenic PCR primerNNSACGCATATCGACGCAGAAGT
Sequence-based reagentDL121_pos113_fwdThis PaperMutagenic PCR primerNNSCATATCGACGCAGAAGTGGAAC
Sequence-based reagentDL121_pos114_fwdThis PaperMutagenic PCR primerNNSATCGACGCAGAAGTGGAACT
Sequence-based reagentDL121_pos115_fwdThis PaperMutagenic PCR primerNNSGACGCAGAAGTGGAACTGG
Sequence-based reagentDL121_pos116_fwdThis PaperMutagenic PCR primerNNSGCAGAAGTGGAACTGGCC
Sequence-based reagentDL121_pos117_fwdThis PaperMutagenic PCR primerNNSGAAGTGGAACTGGCCACC
Sequence-based reagentDL121_pos118_fwdThis PaperMutagenic PCR primerNNSGTGGAACTGGCCACCACT
Sequence-based reagentDL121_pos119_fwdThis PaperMutagenic PCR primerNNSGAACTGGCCACCACTCTAGA
Sequence-based reagentDL121_pos120_fwdThis PaperMutagenic PCR primerNNSCTGGCCACCACTCTAGAG
Sequence-based reagentDL121_pos121_fwdThis PaperMutagenic PCR primerNNSGACACCCATTTCCCGGATTAC
Sequence-based reagentDL121_pos122_fwdThis PaperMutagenic PCR primerNNSACCCATTTCCCGGATTACGA
Sequence-based reagentDL121_pos123_fwdThis PaperMutagenic PCR primerNNSCATTTCCCGGATTACGAGCC
Sequence-based reagentDL121_pos124_fwdThis PaperMutagenic PCR primerNNSTTCCCGGATTACGAGCCG
Sequence-based reagentDL121_pos125_fwdThis PaperMutagenic PCR primerNNSCCGGATTACGAGCCGGAT
Sequence-based reagentDL121_pos126_fwdThis PaperMutagenic PCR primerNNSGATTACGAGCCGGATGACTG
Sequence-based reagentDL121_pos127_fwdThis PaperMutagenic PCR primerNNSTACGAGCCGGATGACTGG
Sequence-based reagentDL121_pos128_fwdThis PaperMutagenic PCR primerNNSGAGCCGGATGACTGGGAA
Sequence-based reagentDL121_pos129_fwdThis PaperMutagenic PCR primerNNSCCGGATGACTGGGAATCG
Sequence-based reagentDL121_pos130_fwdThis PaperMutagenic PCR primerNNSGATGACTGGGAATCGGTATTCAG
Sequence-based reagentDL121_pos131_fwdThis PaperMutagenic PCR primerNNSGACTGGGAATCGGTATTCAGC
Sequence-based reagentDL121_pos132_fwdThis PaperMutagenic PCR primerNNSTGGGAATCGGTATTCAGCGAATT
Sequence-based reagentDL121_pos133_fwdThis PaperMutagenic PCR primerNNSGAATCGGTATTCAGCGAATTCCAC
Sequence-based reagentDL121_pos134_fwdThis PaperMutagenic PCR primerNNSTCGGTATTCAGCGAATTCCAC
Sequence-based reagentDL121_pos135_fwdThis PaperMutagenic PCR primerNNSGTATTCAGCGAATTCCACGATG
Sequence-based reagentDL121_pos136_fwdThis PaperMutagenic PCR primerNNSTTCAGCGAATTCCACGATGC
Sequence-based reagentDL121_pos137_fwdThis PaperMutagenic PCR primerNNSAGCGAATTCCACGATGCTG
Sequence-based reagentDL121_pos138_fwdThis PaperMutagenic PCR primerNNSGAATTCCACGATGCTGATGC
Sequence-based reagentDL121_pos139_fwdThis PaperMutagenic PCR primerNNSTTCCACGATGCTGATGCG
Sequence-based reagentDL121_pos140_fwdThis PaperMutagenic PCR primerNNSCACGATGCTGATGCGCAG
Sequence-based reagentDL121_pos141_fwdThis PaperMutagenic PCR primerNNSGATGCTGATGCGCAGAACT
Sequence-based reagentDL121_pos142_fwdThis PaperMutagenic PCR primerNNSGCTGATGCGCAGAACTCTC
Sequence-based reagentDL121_pos143_fwdThis PaperMutagenic PCR primerNNSGATGCGCAGAACTCTCACAG
Sequence-based reagentDL121_pos144_fwdThis PaperMutagenic PCR primerNNSGCGCAGAACTCTCACAGC
Sequence-based reagentDL121_pos145_fwdThis PaperMutagenic PCR primerNNSCAGAACTCTCACAGCTATTGCTTTG
Sequence-based reagentDL121_pos146_fwdThis PaperMutagenic PCR primerNNSAACTCTCACAGCTATTGCTTTGAGATT
Sequence-based reagentDL121_pos147_fwdThis PaperMutagenic PCR primerNNSTCTCACAGCTATTGCTTTGAGATTCT
Sequence-based reagentDL121_pos148_fwdThis PaperMutagenic PCR primerNNSCACAGCTATTGCTTTGAGATTCTGG
Sequence-based reagentDL121_pos149_fwdThis PaperMutagenic PCR primerNNSAGCTATTGCTTTGAGATTCTGGAG
Sequence-based reagentDL121_pos150_fwdThis PaperMutagenic PCR primerNNSTATTGCTTTGAGATTCTGGAGCG
Sequence-based reagentDL121_pos151_fwdThis PaperMutagenic PCR primerNNSTGCTTTGAGATTCTGGAGCG
Sequence-based reagentDL121_pos152_fwdThis PaperMutagenic PCR primerNNSTTTGAGATTCTGGAGCGGC
Sequence-based reagentDL121_pos153_fwdThis PaperMutagenic PCR primerNNSGAGATTCTGGAGCGGCGG
Sequence-based reagentDL121_pos154_fwdThis PaperMutagenic PCR primerNNSATTCTGGAGCGGCGGTAA
Sequence-based reagentDL121_pos155_fwdThis PaperMutagenic PCR primerNNSCTGGAGCGGCGGTAACAT
Sequence-based reagentDL121_pos156_fwdThis PaperMutagenic PCR primerNNSGAGCGGCGGTAACATCCG
Sequence-based reagentDL121_pos157_fwdThis PaperMutagenic PCR primerNNSCGGCGGTAACATCCGTCG
Sequence-based reagentDL121_pos158_fwdThis PaperMutagenic PCR primerNNSCGGTAACATCCGTCGACAAG
Sequence-based reagentDL121_pos159_fwdThis PaperMutagenic PCR primerNNSTAACATCCGTCGACAAGCTTG
Sequence-based reagentDL121_pos1_revThis PaperMutagenic PCR primerCGGATCCTGGCTGTGGTG
Sequence-based reagentDL121_pos2_revThis PaperMutagenic PCR primerCATCGGATCCTGGCTGTG
Sequence-based reagentDL121_pos3_revThis PaperMutagenic PCR primerGATCATCGGATCCTGGCTG
Sequence-based reagentDL121_pos4_revThis PaperMutagenic PCR primerACTGATCATCGGATCCTGG
Sequence-based reagentDL121_pos5_revThis PaperMutagenic PCR primerCAGACTGATCATCGGATCCTG
Sequence-based reagentDL121_pos6_revThis PaperMutagenic PCR primerAATCAGACTGATCATCGGATCCTG
Sequence-based reagentDL121_pos7_revThis PaperMutagenic PCR primerCGCAATCAGACTGATCATCGG
Sequence-based reagentDL121_pos8_revThis PaperMutagenic PCR primerCGCCGCAATCAGACTGATC
Sequence-based reagentDL121_pos9_revThis PaperMutagenic PCR primerTAACGCCGCAATCAGACTGA
Sequence-based reagentDL121_pos10_revThis PaperMutagenic PCR primerCGCTAACGCCGCAATCAG
Sequence-based reagentDL121_pos11_revThis PaperMutagenic PCR primerTACCGCTAACGCCGCAAT
Sequence-based reagentDL121_pos12_revThis PaperMutagenic PCR primerATCTACCGCTAACGCCGC
Sequence-based reagentDL121_pos13_revThis PaperMutagenic PCR primerGCGATCTACCGCTAACGC
Sequence-based reagentDL121_pos14_revThis PaperMutagenic PCR primerAACGCGATCTACCGCTAAC
Sequence-based reagentDL121_pos15_revThis PaperMutagenic PCR primerGATAACGCGATCTACCGCTAAC
Sequence-based reagentDL121_pos16_revThis PaperMutagenic PCR primerGCCGATAACGCGATCTACC
Sequence-based reagentDL121_pos17_revThis PaperMutagenic PCR primerCATGCCGATAACGCGATCTAC
Sequence-based reagentDL121_pos18_revThis PaperMutagenic PCR primerTTCCATGCCGATAACGCG
Sequence-based reagentDL121_pos19_revThis PaperMutagenic PCR primerGTTTTCCATGCCGATAACGC
Sequence-based reagentDL121_pos20_revThis PaperMutagenic PCR primerGGCGTTTTCCATGCCGATAACG
Sequence-based reagentDL121_pos21_revThis PaperMutagenic PCR primerCATGGCGTTTTCCATGCC
Sequence-based reagentDL121_pos22_revThis PaperMutagenic PCR primerCGGCATGGCGTTTTCCAT
Sequence-based reagentDL121_pos23_revThis PaperMutagenic PCR primerCCACGGCATGGCGTTTTC
Sequence-based reagentDL121_pos24_revThis PaperMutagenic PCR primerGTTCCACGGCATGGCGTT
Sequence-based reagentDL121_pos25_revThis PaperMutagenic PCR primerCAGGTTCCACGGCATGGC
Sequence-based reagentDL121_pos26_revThis PaperMutagenic PCR primerAGGCAGGTTCCACGGCAT
Sequence-based reagentDL121_pos27_revThis PaperMutagenic PCR primerGGCAGGCAGGTTCCACGG
Sequence-based reagentDL121_pos28_revThis PaperMutagenic PCR primerATCGGCAGGCAGGTTCCA
Sequence-based reagentDL121_pos29_revThis PaperMutagenic PCR primerGAGATCGGCAGGCAGGTT
Sequence-based reagentDL121_pos30_revThis PaperMutagenic PCR primerGGCGAGATCGGCAGGCAG
Sequence-based reagentDL121_pos31_revThis PaperMutagenic PCR primerCCAGGCGAGATCGGCAGG
Sequence-based reagentDL121_pos32_revThis PaperMutagenic PCR primerAAACCAGGCGAGATCGGC
Sequence-based reagentDL121_pos33_revThis PaperMutagenic PCR primerTTTAAACCAGGCGAGATCGG
Sequence-based reagentDL121_pos34_revThis PaperMutagenic PCR primerGCGTTTAAACCAGGCGAGAT
Sequence-based reagentDL121_pos35_revThis PaperMutagenic PCR primerGTTGCGTTTAAACCAGGCGA
Sequence-based reagentDL121_pos36_revThis PaperMutagenic PCR primerGGTGTTGCGTTTAAACCAGG
Sequence-based reagentDL121_pos37_revThis PaperMutagenic PCR primerTAAGGTGTTGCGTTTAAACCAGG
Sequence-based reagentDL121_pos38_revThis PaperMutagenic PCR primerATTTAAGGTGTTGCGTTTAAACCAGG
Sequence-based reagentDL121_pos39_revThis PaperMutagenic PCR primerTTTATTTAAGGTGTTGCGTTTAAACCAG
Sequence-based reagentDL121_pos40_revThis PaperMutagenic PCR primerGGGTTTATTTAAGGTGTTGCGTTTAAAC
Sequence-based reagentDL121_pos41_revThis PaperMutagenic PCR primerCACGGGTTTATTTAAGGTGTTGCGT
Sequence-based reagentDL121_pos42_revThis PaperMutagenic PCR primerAATCACGGGTTTATTTAAGGTGTTGC
Sequence-based reagentDL121_pos43_revThis PaperMutagenic PCR primerCATAATCACGGGTTTATTTAAGGTGTTG
Sequence-based reagentDL121_pos44_revThis PaperMutagenic PCR primerGCCCATAATCACGGGTTTATTTAAGG
Sequence-based reagentDL121_pos45_revThis PaperMutagenic PCR primerGCGGCCCATAATCACGGG
Sequence-based reagentDL121_pos46_revThis PaperMutagenic PCR primerATGGCGGCCCATAATCAC
Sequence-based reagentDL121_pos47_revThis PaperMutagenic PCR primerGGTATGGCGGCCCATAATC
Sequence-based reagentDL121_pos48_revThis PaperMutagenic PCR primerCCAGGTATGGCGGCCCATA
Sequence-based reagentDL121_pos49_revThis PaperMutagenic PCR primerTTCCCAGGTATGGCGGCC
Sequence-based reagentDL121_pos50_revThis PaperMutagenic PCR primerCGATTCCCAGGTATGGCG
Sequence-based reagentDL121_pos51_revThis PaperMutagenic PCR primerGATCGATTCCCAGGTATGGCG
Sequence-based reagentDL121_pos52_revThis PaperMutagenic PCR primerACCGATCGATTCCCAGGTATG
Sequence-based reagentDL121_pos53_revThis PaperMutagenic PCR primerACGACCGATCGATTCCCA
Sequence-based reagentDL121_pos54_revThis PaperMutagenic PCR primerCGGACGACCGATCGATTC
Sequence-based reagentDL121_pos55_revThis PaperMutagenic PCR primerCAACGGACGACCGATCGA
Sequence-based reagentDL121_pos56_revThis PaperMutagenic PCR primerTGGCAACGGACGACCGAT
Sequence-based reagentDL121_pos57_revThis PaperMutagenic PCR primerTCCTGGCAACGGACGACC
Sequence-based reagentDL121_pos58_revThis PaperMutagenic PCR primerGCGTCCTGGCAACGGACG
Sequence-based reagentDL121_pos59_revThis PaperMutagenic PCR primerTTTGCGTCCTGGCAACGG
Sequence-based reagentDL121_pos60_revThis PaperMutagenic PCR primerATTTTTGCGTCCTGGCAAC
Sequence-based reagentDL121_pos61_revThis PaperMutagenic PCR primerAATATTTTTGCGTCCTGGCAAC
Sequence-based reagentDL121_pos62_revThis PaperMutagenic PCR primerGATAATATTTTTGCGTCCTGGCAAC
Sequence-based reagentDL121_pos63_revThis PaperMutagenic PCR primerCAGGATAATATTTTTGCGTCCTGGC
Sequence-based reagentDL121_pos64_revThis PaperMutagenic PCR primerGCTCAGGATAATATTTTTGCGTCCTG
Sequence-based reagentDL121_pos65_revThis PaperMutagenic PCR primerTGAGCTCAGGATAATATTTTTGCGTCCT
Sequence-based reagentDL121_pos66_revThis PaperMutagenic PCR primerTTGTGAGCTCAGGATAATATTTTTGCG
Sequence-based reagentDL121_pos67_revThis PaperMutagenic PCR primerCGGTTGTGAGCTCAGGATAATATTTTTG
Sequence-based reagentDL121_pos68_revThis PaperMutagenic PCR primerACCCGGTTGTGAGCTCAG
Sequence-based reagentDL121_pos69_revThis PaperMutagenic PCR primerCGTACCCGGTTGTGAGCT
Sequence-based reagentDL121_pos70_revThis PaperMutagenic PCR primerGTCCGTACCCGGTTGTGA
Sequence-based reagentDL121_pos71_revThis PaperMutagenic PCR primerATCGTCCGTACCCGGTTG
Sequence-based reagentDL121_pos72_revThis PaperMutagenic PCR primerGCGATCGTCCGTACCCGG
Sequence-based reagentDL121_pos73_revThis PaperMutagenic PCR primerTACGCGATCGTCCGTACC
Sequence-based reagentDL121_pos74_rev2This PaperMutagenic PCR primerCGTTACGCGATCGTCC
Sequence-based reagentDL121_pos75_revThis PaperMutagenic PCR primerCCACGTTACGCGATCGTC
Sequence-based reagentDL121_pos76_revThis PaperMutagenic PCR primerCACCCACGTTACGCGATC
Sequence-based reagentDL121_pos77_revThis PaperMutagenic PCR primerCTTCACCCACGTTACGCG
Sequence-based reagentDL121_pos78_revThis PaperMutagenic PCR primerCGACTTCACCCACGTTACG
Sequence-based reagentDL121_pos79_revThis PaperMutagenic PCR primerCACCGACTTCACCCACGT
Sequence-based reagentDL121_pos80_revThis PaperMutagenic PCR primerATCCACCGACTTCACCCA
Sequence-based reagentDL121_pos81_revThis PaperMutagenic PCR primerTTCATCCACCGACTTCACC
Sequence-based reagentDL121_pos82_revThis PaperMutagenic PCR primerTGCTTCATCCACCGACTTCACC
Sequence-based reagentDL121_pos83_revThis PaperMutagenic PCR primerAATTGCTTCATCCACCGACTTC
Sequence-based reagentDL121_pos84_revThis PaperMutagenic PCR primerCGCAATTGCTTCATCCACC
Sequence-based reagentDL121_pos85_revThis PaperMutagenic PCR primerCGCCGCAATTGCTTCATC
Sequence-based reagentDL121_pos86_revThis PaperMutagenic PCR primerACACGCCGCAATTGCTTC
Sequence-based reagentDL121_pos87_revThis PaperMutagenic PCR primerACCACACGCCGCAATTGC
Sequence-based reagentDL121_pos88_revThis PaperMutagenic PCR primerGTCACCACACGCCGCAAT
Sequence-based reagentDL121_pos89_rev2This PaperMutagenic PCR primerTACGTCACCACACGCC
Sequence-based reagentDL121_pos90_revThis PaperMutagenic PCR primerTGGTACGTCACCACACGC
Sequence-based reagentDL121_pos91_revThis PaperMutagenic PCR primerTTCTGGTACGTCACCACACGC
Sequence-based reagentDL121_pos92_revThis PaperMutagenic PCR primerGATTTCTGGTACGTCACCACACGCC
Sequence-based reagentDL121_pos93_revThis PaperMutagenic PCR primerCATGATTTCTGGTACGTCACCACACGC
Sequence-based reagentDL121_pos94_revThis PaperMutagenic PCR primerCACCATGATTTCTGGTACGTCACCACA
Sequence-based reagentDL121_pos95_revThis PaperMutagenic PCR primerAATCACCATGATTTCTGGTACGTCA
Sequence-based reagentDL121_pos96_revThis PaperMutagenic PCR primerGCCAATCACCATGATTTCTGGTAC
Sequence-based reagentDL121_pos97_revThis PaperMutagenic PCR primerGCCGCCAATCACCATGATTT
Sequence-based reagentDL121_pos98_revThis PaperMutagenic PCR primerGCCGCCGCCAATCACCATG
Sequence-based reagentDL121_pos99_revThis PaperMutagenic PCR primerGCGGCCGCCGCCAATCAC
Sequence-based reagentDL121_pos100_revThis PaperMutagenic PCR primerAACGCGGCCGCCGCCAAT
Sequence-based reagentDL121_pos101_revThis PaperMutagenic PCR primerATAAACGCGGCCGCCGCC
Sequence-based reagentDL121_pos102_revThis PaperMutagenic PCR primerTTCATAAACGCGGCCGCC
Sequence-based reagentDL121_pos103_revThis PaperMutagenic PCR primerCTGTTCATAAACGCGGCC
Sequence-based reagentDL121_pos104_revThis PaperMutagenic PCR primerGAACTGTTCATAAACGCGGC
Sequence-based reagentDL121_pos105_revThis PaperMutagenic PCR primerCAAGAACTGTTCATAAACGCGG
Sequence-based reagentDL121_pos106_revThis PaperMutagenic PCR primerTGGCAAGAACTGTTCATAAACGC
Sequence-based reagentDL121_pos107_revThis PaperMutagenic PCR primerTTTTGGCAAGAACTGTTCATAAACG
Sequence-based reagentDL121_pos108_revThis PaperMutagenic PCR primerCGCTTTTGGCAAGAACTGTTCATAAA
Sequence-based reagentDL121_pos109_revThis PaperMutagenic PCR primerTTGCGCTTTTGGCAAGAACT
Sequence-based reagentDL121_pos110_revThis PaperMutagenic PCR primerCTTTTGCGCTTTTGGCAAGAAC
Sequence-based reagentDL121_pos111_revThis PaperMutagenic PCR primerAAGCTTTTGCGCTTTTGGC
Sequence-based reagentDL121_pos112_revThis PaperMutagenic PCR primerATAAAGCTTTTGCGCTTTTGGCA
Sequence-based reagentDL121_pos113_revThis PaperMutagenic PCR primerCAGATAAAGCTTTTGCGCTTTTGG
Sequence-based reagentDL121_pos114_revThis PaperMutagenic PCR primerCGTCAGATAAAGCTTTTGCGCTTT
Sequence-based reagentDL121_pos115_revThis PaperMutagenic PCR primerATGCGTCAGATAAAGCTTTTGCG
Sequence-based reagentDL121_pos116_revThis PaperMutagenic PCR primerGATATGCGTCAGATAAAGCTTTTGC
Sequence-based reagentDL121_pos117_revThis PaperMutagenic PCR primerGTCGATATGCGTCAGATAAAGCTTTTG
Sequence-based reagentDL121_pos118_revThis PaperMutagenic PCR primerTGCGTCGATATGCGTCAGATAAA
Sequence-based reagentDL121_pos119_revThis PaperMutagenic PCR primerTTCTGCGTCGATATGCGTCA
Sequence-based reagentDL121_pos120_revThis PaperMutagenic PCR primerCACTTCTGCGTCGATATGCG
Sequence-based reagentDL121_pos121_revThis PaperMutagenic PCR primerGTCGATGTTCTCGGCGGT
Sequence-based reagentDL121_pos122_revThis PaperMutagenic PCR primerGCCGTCGATGTTCTCGGC
Sequence-based reagentDL121_pos123_revThis PaperMutagenic PCR primerGTCGCCGTCGATGTTCTC
Sequence-based reagentDL121_pos124_revThis PaperMutagenic PCR primerGGTGTCGCCGTCGATGTT
Sequence-based reagentDL121_pos125_revThis PaperMutagenic PCR primerATGGGTGTCGCCGTCGAT
Sequence-based reagentDL121_pos126_revThis PaperMutagenic PCR primerGAAATGGGTGTCGCCGTC
Sequence-based reagentDL121_pos127_revThis PaperMutagenic PCR primerCGGGAAATGGGTGTCGCC
Sequence-based reagentDL121_pos128_revThis PaperMutagenic PCR primerATCCGGGAAATGGGTGTC
Sequence-based reagentDL121_pos129_revThis PaperMutagenic PCR primerGTAATCCGGGAAATGGGTGTC
Sequence-based reagentDL121_pos130_revThis PaperMutagenic PCR primerCTCGTAATCCGGGAAATGGG
Sequence-based reagentDL121_pos131_revThis PaperMutagenic PCR primerCGGCTCGTAATCCGGGAA
Sequence-based reagentDL121_pos132_revThis PaperMutagenic PCR primerATCCGGCTCGTAATCCGG
Sequence-based reagentDL121_pos133_revThis PaperMutagenic PCR primerGTCATCCGGCTCGTAATCC
Sequence-based reagentDL121_pos134_revThis PaperMutagenic PCR primerCCAGTCATCCGGCTCGTA
Sequence-based reagentDL121_pos135_revThis PaperMutagenic PCR primerTTCCCAGTCATCCGGCTC
Sequence-based reagentDL121_pos136_revThis PaperMutagenic PCR primerCGATTCCCAGTCATCCGG
Sequence-based reagentDL121_pos137_revThis PaperMutagenic PCR primerTACCGATTCCCAGTCATCCG
Sequence-based reagentDL121_pos138_revThis PaperMutagenic PCR primerGAATACCGATTCCCAGTCATCC
Sequence-based reagentDL121_pos139_revThis PaperMutagenic PCR primerGCTGAATACCGATTCCCAGTC
Sequence-based reagentDL121_pos140_revThis PaperMutagenic PCR primerTTCGCTGAATACCGATTCCCA
Sequence-based reagentDL121_pos141_revThis PaperMutagenic PCR primerGAATTCGCTGAATACCGATTCCC
Sequence-based reagentDL121_pos142_revThis PaperMutagenic PCR primerGTGGAATTCGCTGAATACCGATTC
Sequence-based reagentDL121_pos143_revThis PaperMutagenic PCR primerATCGTGGAATTCGCTGAATACC
Sequence-based reagentDL121_pos144_revThis PaperMutagenic PCR primerAGCATCGTGGAATTCGCTG
Sequence-based reagentDL121_pos145_revThis PaperMutagenic PCR primerATCAGCATCGTGGAATTCGC
Sequence-based reagentDL121_pos146_revThis PaperMutagenic PCR primerCGCATCAGCATCGTGGAATT
Sequence-based reagentDL121_pos147_revThis PaperMutagenic PCR primerCTGCGCATCAGCATCGTG
Sequence-based reagentDL121_pos148_revThis PaperMutagenic PCR primerGTTCTGCGCATCAGCATC
Sequence-based reagentDL121_pos149_revThis PaperMutagenic PCR primerAGAGTTCTGCGCATCAGC
Sequence-based reagentDL121_pos150_revThis PaperMutagenic PCR primerGTGAGAGTTCTGCGCATCAG
Sequence-based reagentDL121_pos151_revThis PaperMutagenic PCR primerGCTGTGAGAGTTCTGCGC
Sequence-based reagentDL121_pos152_revThis PaperMutagenic PCR primerATAGCTGTGAGAGTTCTGCG
Sequence-based reagentDL121_pos153_revThis PaperMutagenic PCR primerGCAATAGCTGTGAGAGTTCTGC
Sequence-based reagentDL121_pos154_revThis PaperMutagenic PCR primerAAAGCAATAGCTGTGAGAGTTCTG
Sequence-based reagentDL121_pos155_revThis PaperMutagenic PCR primerCTCAAAGCAATAGCTGTGAGAGTTC
Sequence-based reagentDL121_pos156_revThis PaperMutagenic PCR primerAATCTCAAAGCAATAGCTGTGAGAGTT
Sequence-based reagentDL121_pos157_revThis PaperMutagenic PCR primerCAGAATCTCAAAGCAATAGCTGTGAG
Sequence-based reagentDL121_pos158_revThis PaperMutagenic PCR primerCTCCAGAATCTCAAAGCAATAGCTG
Sequence-based reagentDL121_pos159_revThis PaperMutagenic PCR primerCCGCTCCAGAATCTCAAAGC
Sequence-based reagentDL121_E154R_FThis PaperMutagenic PCR primerctctcacagctattgctttaggattctggagcggcggtaa
Sequence-based reagentDL121_E154R_RThis PaperMutagenic PCR primerttaccgccgctccagaatcctaaagcaatagctgtgagag
Sequence-based reagentDL121_D122W_FThis PaperMutagenic PCR primergtaatccgggaaatgggtccagccgtcgatgttctcggc
Sequence-based reagentDL121_D122W_RThis PaperMutagenic PCR primergccgagaacatcgacggctggacccatttcccggattac
Sequence-based reagentDL121_D127W_FThis PaperMutagenic PCR primercagtcatccggctcgtaccacgggaaatgggtgtcgc
Sequence-based reagentDL121_D127W_RThis PaperMutagenic PCR primergcgacacccatttcccgtggtacgagccggatgactg
Sequence-based reagentDL121_M16A_FThis PaperMutagenic PCR primercggcatggcgttttccgcgccgataacgcgatct
Sequence-based reagentDL121_M16A_RThis PaperMutagenic PCR primeragatcgcgttatcggcgcggaaaacgccatgccg
Sequence-based reagentDL121_A9N_FThis PaperMutagenic PCR primercatgccgataacgcgatctacatttaacgccgcaatcagactgatc
Sequence-based reagentDL121_A9N_RThis PaperMutagenic PCR primergatcagtctgattgcggcgttaaatgtagatcgcgttatcggcatg
Sequence-based reagentDL121_R52K_FThis PaperMutagenic PCR primertcctggcaacggcttaccgatcgattcccaggtatggc
Sequence-based reagentDL121_R52K_RThis PaperMutagenic PCR primergccatacctgggaatcgatcggtaagccgttgccagga
Sequence-based reagentDL121_E120P_FThis PaperMutagenic PCR primerctagagtggtggccagtggcacttctgcgtcgatat
Sequence-based reagentDL121_E120P_RThis PaperMutagenic PCR primeratatcgacgcagaagtgccactggccaccactctag
Sequence-based reagentDL121_S148C_FThis PaperMutagenic PCR primeraagcaatagctgtgacagttctgcgcatcagcatc
Sequence-based reagentDL121_S148C_RThis PaperMutagenic PCR primergatgctgatgcgcagaactgtcacagctattgctt
Sequence-based reagentDL121_H124Q_FThis PaperMutagenic PCR primertcgtaatccgggaactgggtgtcgccgtc
Sequence-based reagentDL121_H12RQ_RThis PaperMutagenic PCR primergacggcgacacccagttcccggattacga
Sequence-based reagentDL121_D27N_FThis PaperMutagenic PCR primeraaaccaggcgagattggcaggcaggttcc
Sequence-based reagentDL121_D27N_RThis PaperMutagenic PCR primerggaacctgcctgccaatctcgcctggttt
Sequence-based reagentDL121_D87A_FThis PaperMutagenic PCR primercatgatttctggtacggcaccacacgccgcaat
Sequence-based reagentDL121_D87A_RThis PaperMutagenic PCR primerattgcggcgtgtggtgccgtaccagaaatcatg
Sequence-based reagentThrombin_to_TEV_FThis PaperMutagenic PCR primercttccagggtcatgggatgatgatcagtctgattgc
Sequence-based reagentThrombin_to_TEV_RThis PaperMutagenic PCR primertacaggttctcaccaccgtggtggtggtg
Sequence-based reagentDL121_SL1V2_FThis PaperRound one Amplicon PCR primercactctttccctacacgacgctcttccgatctnnnnatcaccatcatcaccacagc
Sequence-based reagentDL121_SL1V2_RThis PaperRound one Amplicon PCR primertgactggagttcagacgtgtgctcttccgatctnnnnaccgatcgattcccaggta
Sequence-based reagentDL121_SL2V2_FThis PaperRound one Amplicon PCR primercactctttccctacacgacgctcttccgatctnnnngcaacaccttaaataaacccg
Sequence-based reagentDL121_SL2V2_RThis PaperRound one Amplicon PCR primertgactggagttcagacgtgtgctcttccgatctnnnngatttctggtacgtcaccaca
Sequence-based reagentDL121_SL3V2_FThis PaperRound one Amplicon PCR primercactctttccctacacgacgctcttccgatctnnnngtaacgtgggtgaagtcg
Sequence-based reagentDL121_SL3V2_RThis PaperRound one Amplicon PCR primertgactggagttcagacgtgtgctcttccgatctnnnnctcgatgcgctctagagtg
Sequence-based reagentDL121_SL4V2_FThis PaperRound one Amplicon PCR primercactctttccctacacgacgctcttccgatctnnnnaagaagaccgccgagaacat
Sequence-based reagentDL121_SL4V2_RThis PaperRound one Amplicon PCR primertgactggagttcagacgtgtgctcttccgatctnnnncttaagcattatgcggccg
Sequence-based reagentDL121_CLV3_FThis PaperRound one Amplicon PCR primercactctttccctacacgacgctcttccgatctnnnngacacccatttcccggattacgagc
Sequence-based reagentDL_WTTS_R3This PaperRound one Amplicon PCR primertgactggagttcagacgtgtgctcttccgatctnnnngccgtgtacaatacgattactttctg
Sequence-based reagentD501Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacactatagcctacactctttccctacacgac
Sequence-based reagentD502Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacacatagaggcacactctttccctacacgac
Sequence-based reagentD503Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacaccctatcctacactctttccctacacgac
Sequence-based reagentD504Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacacggctctgaacactctttccctacacgac
Sequence-based reagentD505Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacacaggcgaagacactctttccctacacgac
Sequence-based reagentD506Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacactaatcttaacactctttccctacacgac
Sequence-based reagentD507Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacaccaggacgtacactctttccctacacgac
Sequence-based reagentD508Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primeraatgatacggcgaccaccgagatctacacgtactgacacactctttccctacacgac
Sequence-based reagentD701Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatcgagtaatgtgactggagttcagacgtg
Sequence-based reagentD702Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagattctccggagtgactggagttcagacgtg
Sequence-based reagentD703Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagataatgagcggtgactggagttcagacgtg
Sequence-based reagentD704Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatggaatctcgtgactggagttcagacgtg
Sequence-based reagentD705Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatttctgaatgtgactggagttcagacgtg
Sequence-based reagentD706Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatacgaattcgtgactggagttcagacgtg
Sequence-based reagentD707Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatagcttcaggtgactggagttcagacgtg
Sequence-based reagentD708Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatgcgcattagtgactggagttcagacgtg
Sequence-based reagentD709Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatcatagccggtgactggagttcagacgtg
Sequence-based reagentD710Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatttcgcggagtgactggagttcagacgtg
Sequence-based reagentD711Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatgcgcgagagtgactggagttcagacgtg
Sequence-based reagentD712Illumina/Reynolds et al. Cell 2011 [20]Round two Amplicon PCR primercaagcagaagacggcatacgagatctatcgctgtgactggagttcagacgtg
Commercial assay or kitQuikChange II site-directed mutagenesis kitAgilentCat. #: 200523
Software, algorithmusearch v11.0.667Edgar Bioinformatics 2010 (PMID:20709691)Merge read pairshttps://www.drive5.com/usearch/

Experimental model and subject details

Escherichia coli expression and selection strains

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ER2566 ΔfolA ΔthyA E. coli were used for all growth in vivo growth rate measurements; this strain was a kind gift from Dr. Steven Benkovic and is the same used in Reynolds et al., 2011 and Thompson et al., 2020 (Reynolds et al., 2011; Thompson et al., 2020). XL1-Blue E. coli (genotype: recA1 endA1 gyrA96 thi-1 hsdR17 supE44 relA1 lac [F’ proAB lacIqZΔM15 Tn10(Tetr)]) from Agilent Technologies were used for cloning, mutagenesis, and plasmid propagation. BL21(DE3) E. coli (genotype: fhuA2 [lon] ompT gal (λ DE3) [dcm] ∆hsdS. λ DE3 = λ sBamHIo ∆EcoRI-B int::(lacI::PlacUV5::T7 gene1) i21 ∆nin5) from New England Biolabs were used for protein expression.

Method details

DHFR saturation mutagenesis library construction

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The construction of the DHFR-LOV2 saturation mutagenesis library was done as described in Thompson et al., 2020 (Thompson et al., 2020). Four sublibraries were generated to cover the entire mutational space of E. coli DHFR: positions 1–40 (sublibrary1, SL1), positions 41–80 (sublibrary2, SL2), positions 81–120 (sublibrary3, SL3), and positions 121–159 (sublibrary4, SL4) Inverse PCR with NNS mutagenic primers (N = A/T/G/C, S = G/C) was done at every position in DHFR to produce all amino acid substitution. The vector with DHFR-LOV2 121 and TYMS in a pACYC-Duet vector was described in Reynolds et al., 2011 (Reynolds et al., 2011).

The NNS primers were phosphorylated with T4 polynucleotide kinase (NEB, cat#M0201S). 20 µL phosphorylations was prepared according to the following recipe: 16.5 µL sterile water, 2 µL T4 ligase buffer, 0.5 µL T4 PNK enzyme, and 1 µL 100 µM NNS primers. The reactions were then heated at 37°C for 1 hr and 65°C for 20 min.

PCR reactions were set up using 2x Q5 mastermix (NEB, cat#M0492), 10 ng of plasmid template, and 500 nM forward and reverse primers. PCR was performed in the following steps: (1) 98°C for 30 s, (2) 98°C for 10 s, (3) 55°C for 30 s, (4) 72°C for 2 min, (5) return to step 2 for 22 cycles, (6) 72°C for 5 min. 25 µL of PCR reaction was mixed with 1 µL of DpnI (NEB, cat#R0176) at 37°C for 4 hr. The samples were then purified by gel extraction and a DNA Clean and Concentrator −5 kit (Zymo Research, cat#D4014). PCR product solution were then phosphorylated with a second round of T4 PNK: 100 µL of gel-extracted PCR product,12 µL of 10x T4 ligase buffer, 5 µL of T4 PNK, 5 µL of sterile water and were incubated at 37°C for 1 hr with 90°C for 30 s. The reactions were ligated with 100 µL PNK phosphorylated PCR product, 15 µL T4 ligase (NEB, cat#M0202S), 30 µL T4 ligase buffer and, 155 µL sterile water. The reaction was incubated at room temperature for 24 hr.

The concentration of each reaction was quantified by gel densitometry (ImageJ) and combined in equimolar ratios to form sublibraries. The library was divided up into four sublibraries with sublibrary 1 covering positions 1–40, sublibrary 2 covering positions 41–80, sublibrary 3 covering positions 81–120, and sublibrary 4 covering positions 121–150. Sublibraries were transformed into electrocompetent XL1-Blue E. coli using a MicroPulser Electroporator (Bio Rad) and gene pulser cuvettes (Bio Rad, cat#165–2089). Cultures were miniprepped using a GeneJET plasmid miniprep kit (Thermo Scientific, cat#K05053). Library completeness was verified by deep sequencing on a MiSeq (Illumina).

Growth rate measurements in the turbidostat for DHFR DL121 mutant library

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DHFR DL121 sublibraries were transformed into ER2566 ∆folA ∆thyA E. coli by electroporation using a MicroPulser Electroporator (Bio Rad) and gene pulser cuvettes (Bio Rad, cat#165–2089). Cultures were grown overnight at 37°C in GM9 minimal media (93.0 mM Sodium (Na+), 22.1 mM Potassium (K+), 18.7 mM Ammonium (NH4), 1.0 mM Calcium (Ca2+), 0.1 mM Magnesium (Mg2+), 29.2 mM Chloride (Cl-), 0.1 mM Sulfate (SO42-), and 42.2 mM Phosphate (PO43-), 0.4% glucose) pH 6.50, containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol (Sigma, cat#C0378-5G) as well as folA mix which contains 38 µg/mL glycine (Sigma, cat#50046), 75.5 µg/mL L-methionine (Sigma, cat#M9625) 1 µg/mL calcium pantothenate (Sigma, cat#C8731), and 20 µg/mL adenosine (Sigma, cat#A9251). Four hours before the start of the experiment, the overnight culture was diluted to an optical density of 0.1 at 600 nm in GM9 minimal media containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol and incubated for four hours at 30°C. The cultures were centrifuged at 2000 RCF for 10 min and resuspended in the experimental conditions of GM9 minimal media containing 1 µg/mL thymidine and 30 µg/mL chloramphenicol. This was repeated two more times. The cultures were then back-diluted to an OD600 of 0.1 in 16 mL/vial of media. The turbidostat described in Toprak et al., 2013 was used in continuous culture (turbidostat) mode with a clamp OD600 of 0.15 and a temperature of 30°C. Each vial had a stir bar. Vials designated as ‘lit’ had one 5V blue LED active. The optical density was continuously monitored throughout the experiment. 1 mL samples were taken at the beginning of selection (0 hr) and at 4, 8, 12, 16, 20, and 24 hr into selection and were centrifuged at 21,130 RCF for 5 min at room temperature with the pellet being stored at −20°C for sequencing sample preparation.

Growth rate measurements in the turbidostat for DHFR control library

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Wild-type DHFR, 12 DHFR point mutants (D27N, F31V, F31Y, F31Y-L54I, G121V, G121V-F31Y, G121V-M42F, L54I, L54I-G121V, M42F, and W22H), and three chimeric DHFR-LOV2 fusion constructs (DL116, DL121, and DL121-C450S) each in a pACYC-Duet vector with TYMS as described in Reynolds et al., 2011 were transformed into ER2566 ∆folA ∆thyA E. coli by electroporation using a MicroPulser Electroporator (Bio Rad) and gene pulser cuvettes (Bio Rad, cat#165–2089) (Reynolds et al., 2011). Cultures were grown overnight at 37°C in GM9 minimal media (93.0 mM Sodium (Na+), 22.1 mM Potassium (K+), 18.7 mM Ammonium (NH4), 1.0 mM Calcium (Ca2+), 0.1 mM Magnesium (Mg2+), 29.2 mM Chloride (Cl-), 0.1 mM Sulfate (SO42-), and 42.2 mM Phosphate (PO43-), 0.4% glucose) pH 6.50, containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol (Sigma, cat#C0378-5G) as well as folA mix which contains 38 µg/mL glycine (Sigma, cat#50046), 75.5 µg/mL L-methionine (Sigma, cat#M9625) 1 µg/mL calcium pantothenate (Sigma, cat#C8731), and 20 µg/mL adenosine (Sigma, cat#A9251). Four hours before the start of the experiment the overnight culture was diluted to an optical density of 0.1 at 600 nm in GM9 minimal media containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol and incubated for four hours at 30°C. The cultures were centrifuged at 2000 RCF for 10 min and resuspended in the experimental conditions of GM9 minimal media containing 1 µg/mL thymidine and 30 µg/mL chloramphenicol. This was repeated two more times. The cultures were then back-diluted to an OD600 of 0.1 and pooled at equal (1/16th) ratios and aliquoted into four ‘dark’ and four ‘lit’ vials with 16 ml culture. The turbidostat described in Toprak et al., 2013 was used in continuous culture (turbidostat) mode with a clamp OD600 of 0.15 and a temperature of 30°C. Each vial had a stir bar. Vials designated as ‘lit’ had one 5V blue LED active. The optical density was continuously monitored throughout the experiment. One mL samples were taken at the beginning of selection (0 hr) and at 4, 8, 12, 16, 20, and 24 hr into selection and were centrifuged at 21,130 RCF for 5 min at room temperature with the pellet being stored at −20°C for sequencing sample preparation.

Plate reader assay for E. coli growth

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Single point mutant DHFR-D27N, DL121 chimeric protein, and DL121 with a point mutant D27N each in a pACYC-Duet vector with TYMS as described in Reynolds et al., 2011 were transformed into ER2566 ∆folA ∆thyA E. coli by electroporation using a MicroPulser Electroporator (Bio Rad) and gene pulser cuvettes (Bio Rad, cat#165–2089) (Reynolds et al., 2011). Cultures were grown overnight at 37°C in GM9 minimal media (93.0 mM Sodium (Na+), 22.1 mM Potassium (K+), 18.7 mM Ammonium (NH4), 1.0 mM Calcium (Ca2+), 0.1 mM Magnesium (Mg2+), 29.2 mM Chloride (Cl-), 0.1 mM Sulfate (SO42-), and 42.2 mM Phosphate (PO43-), 0.4% glucose) pH 6.50, containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol (Sigma, cat#C0378-5G) as well as folA mix which contains 38 µg/mL glycine (Sigma, cat#50046), 75.5 µg/mL L-methionine (Sigma, cat#M9625) 1 µg/mL calcium pantothenate (Sigma, cat#C8731), and 20 µg/mL adenosine (Sigma, cat#A9251). Four hours before the start of the experiment, the overnight culture was diluted to an optical density of 0.1 at 600 nm in GM9 minimal media containing 50 µg/mL thymidine and 30 µg/mL chloramphenicol and incubated for four hours at 30°C. The cultures were centrifuged at 2000 RCF for 10 min and resuspended in the experimental conditions of GM9 minimal media containing either 0, 1, or 50 µg/mL thymidine and 30 µg/mL chloramphenicol. The cells were centrifuged and resuspended two more times. The cultures were then back-diluted to an OD600 of 0.005 into 96-well plates with six replicates each.

Next-generation sequencing Amplicon sample preparation

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Cell pellets were lysed by the addition of 10 µL sterile water, mixed by pipetting, and incubated at 98°C for 5 min. One µL of this was then combined with 5 µL Q5 buffer (NEB, cat#M0491S), 0.5 µL 10 mM DNTP (Thermo Scientific, cat#R0192), 2.5 µL of 10 mM forward and reverse primers specific to the sublibrary and containing the TruSeq adapter sequence (Appendix 1: SL1V2, SL2V2, SL3V2, SL4V2, DL121CLV3F, and DL_WTTS_R3), 0.25 µL of Q5 enzyme (NEB, cat#M0491S) and 13.25 µL of sterile water. These samples were then heated at 98°C for 90 s and then cycled through 98°C for 10 s 63–65°C (sublibrary 1: 66°C, sublibrary 2: 63°C, sublibrary 3: 64°C, and sublibrary 4: 65°C) for 15 s and then 72°C for 15 s, repeating 20 times with a final 72°C heating for 120 s in a Veriti 96-well thermocycler (Applied Biosystems). These samples were then amplified using TruSeq PCR reactions with a unique combination of i5/i7 indexing primers for each timepoint. 1 µL of this PCR reaction was then combined with 5 µL Q5 buffer (NEB, cat#M0491S), 0.5 µL 10 mM DNTP (Thermo Scientific, cat#R0192), 2.5 µL of 10 mM forward and reverse primers, 0.25 µL of Q5 enzyme (NEB, cat#M0491S) and 13.25 µL of sterile water. These samples were then heated at 98°C for 30 s and then cycled through 98°C for 10 s 55°C for 10 s and then 72°C for 15 s, repeating 20 times with a final 72°C heating for 60 s in a Veriti 96 well thermocycler (Applied Biosystems). Amplified DNA from i5/i7 PCR reaction was quantified using the picogreen assay (Thermo Scientific, cat#P7589) on a Victor X3 multimode plate reader (Perkin Elmer) and the samples were mixed in an equimolar ratio. The DNA was then purified by gel extraction and a DNA Clean and Concentrator −5 kit (Zymo Research, cat#D4014). DNA quality was determined by 260 nm/230 nm and 260 nm/280 nm ratios on a DS-11 +spectrophotometer (DeNovix) and concentration was determined using the Qubit 3 (Thermo Scientific). Pooled samples were sent to GeneWiz where they were analyzed by TapeStation (Agilent Technologies) and sequenced on a HiSeq 4000 sequencer (Illumina) with 2 × 150 bp dual index run with 30% PhiX spike-in yielding 1.13 billion reads. The control library was sequenced in-house using a MiSeq sequencer (Illumina) with 2 × 150 bp dual index 300 cycle MiSeq Nano Kit V2 (Illumina cat#15036522) with 20% PhiX (Illumina cat#FC-110–3001) spike-in yielding 903,488 reads.

DHFR chimeric expression constructs

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The E. coli DHFR LOV2 fusion was cloned as an NcoI/XhoI fragment into the expression vector pHIS8-3 (Lee et al., 2008; Reynolds et al., 2011). Point mutants were engineered into the DHFR gene using QuikChange II site-directed mutagenesis kits (Agilent cat#200523) using primers specified in Appendix 1. All DHFR/LOV2 fusions for purification were expressed under control of a T7 promoter, with an N-terminal 8X His-tag for nickel affinity purification. The existing thrombin cleavage site (LVPRGS) following the His-tag in pHIS8-3 was changed to a TEV cleavage site using restriction-free PCR to improve the specificity of tag removal (Bond and Naus, 2012). All constructs were verified by Sanger DNA sequencing.

Protein expression and purification

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DHFR-LOV2 chimeric proteins were expressed in BL21(DE3) E. coli grown at 30°C in Terrific Broth (12 g/L Tryptone, 24 g/L yeast extract, 4 mL/L glycerol, 17 mM KH2PO4, and 72 mM K2HPO4). Protein expression was induced when the cells reached an absorbance at 600 nm of 0.7 with 0.25 mM IPTG, and cells were grown at 18°C overnight. Cell pellets were lysed by sonication in binding buffer (500 mM NaCl, 10 mM imidazole, 50 mM Tris-HCL, pH 8.0) added at a volume of 5 ml/g cell pellet. Next the lysate was clarified by centrifugation and the soluble fraction was incubated with equilibrated Ni-NTA resin (Qiagen cat#4561) for 1 hr at 4°C. After washing with one column volume of wash buffer (300 mM NaCl, 20 mM imidazole, 50 mM Tris-HCL, pH 8.0) the DHFR-LOV2 protein was eluted with elution buffer (1M NaCl, 250 mM imidazole, 50 mM Tris-HCL, pH 8.0) at 4°C. Eluted protein was dialyzed into dialysis buffer (300 mM NaCl, 1% glycerol, 50 mM Tris-HCl, pH 8.0) at 4°C overnight in 10,000 MWCO Thermo protein Slide A Lyzer (Fisher Scientific cat#PI87730). Following dialysis, the protein was then purified by size exclusion chromatography (HiLoad 16/600 Superdex 75 pg column, GE Life Sciences cat#28989333). Purified protein was concentrated using Amicon Ulta 10 k M.W. cutoff concentrator (Sigma cat#UFC801024) and flash frozen using liquid N2 prior to enzymatic assays.

Steady state Michaelis Menten measurements

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The protein was spun down at 21,130 RCF at 4°C for 10 min and the supernatant was moved to a new tube with any pellet being discarded. The concentration of the protein was quantitated by A280 using a DS-11 +spectrophotometer (DeNovix) with an extinction coefficient of 44920 mM−1 cm−1. The parameters kcat and Km under Michaelis-Menten conditions were determined by measuring the initial velocity for the depletion of NADPH as measured in absorbance at 340 nm, with an extinction coefficient of 13.2 mM−1 cm−1. This is done in a range of substrate concentrations with a minimum of 8 data points around 4 Km, 2 Km, 1.5 Km, Km, 0.8 Km, 0.5 Km, 0.25 Km and 0. The initial velocities (slope of the first 15 s) were plotted vs. the concentration of Dihydrofolate and fit to a Michaelis Menten model using non-linear regression in GraphPad Prism 7. The reactions are run in MTEN buffer (50 mM 2-(N-morpholino)ethanesulfonic acid, 25 mM tris base, 25 mM ethanolamine, 100 mM NaCl) pH 7.00, 5 mM Dithiothreitol, 90 µM NADPH (Sigma-Aldrich cat#N7505) quantitated by A340. Dihydrofolate (Sigma-Aldrich cat#D7006) is suspended in MTEN buffer pH 7.00 with 0.35% β-mercaptoethanol and quantitated by A282 with an extinction coefficient of 28 mM−1 cm−1. Depletion of NADPH is observed in 1 mL cuvettes with a path length of 1 cm in a Lambda 650 UV/VIS spectrometer (Perkin Elmer) with attached water Peltier system set to 17°C. Lit samples are illuminated for at least 2 min by full spectrum 125 watt 6400K compact fluorescent bulb (Hydrofarm Inc cat#FLC125D). Dark samples were also exposed to the light in the same way as the lit samples but were in opaque tubs. Velocity, V=kcat[P][S]KM+[S], was calculated using the concentration of DHF found in wild-type E. coli (~25 µM Kwon et al., 2008).

Spectrophotometry of the LOV2 chromophore

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The spectra of the LOV2 chromophore is determined with a Lambda 650 UV/VIS spectrometer (Perkin Elmer) at 350–550 nm using paired 100 μL Hellma ultra micro cuvettes (Sigma cat#Z600350-1EA) with a path length of 1 cm. Purified protein in was diluted (when possible) to 20 μM in MTEN buffer pH 7.00 with 0.35% β-mercaptoethanol The lit samples are illuminated for at least 2 min by full spectrum 125 watt 6400K compact fluorescent bulb (hydrofarm Inc). Relaxation of the lit state chromophore is observed in the Lambda 650 UV/VIS spectrometer (Perkin Elmer) at 447 nm (dark peak) using paired 100 μL Hellma ultra micro cuvettes (Sigma cat#Z600350-1EA) with a path length of 1 cm.

Quantification and statistical analysis

Next-generation sequencing

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The sequencing data analysis can be divided into two portions: (1) Read Joining, Filtering and Counting, followed by (2) Calculating Relative Fitness and Final Filtering. We describe each step below; all code was implemented in Bash shell scripting or Python 3.6.4. All analysis codes have been made available as a series of python 3 Jupyter Notebooks on github (https://github.com/reynoldsk/allostery-in-dhfr; McCormick et al., 2021; copy archived at swh:1:rev:dd8ee13f775f8b08548d64868f15e46583cbf543).

Read joining, filtering, and counting

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The data analysis began with unjoined illumina fastq.gz files separated by index (generated by GeneWiz). The forward and reverse reads were combined using usearch v11.0.667 using the i86linux32 package. The commands given to usearch are contained in the script UCOMBINER.bsh.

Reads of each paired fastq file are identified and quality checked using the script DL121_fastq_analysis.py. Mutant nucleotide counts and number of wild-type reads are stored in a dictionary where the read count is separated by file name (vial and timepoint eg: T2V3) and sublibrary. If any nucleotide in the coding region is below a qscore cutoff of 30, that read is discarded. Counts of every nucleotide are saved in a text file by timepoint and vial.

Converting nucleotide variation to amino acid count as well as probabilistic sequencer error correction is done by the Hamming_analysis.ipynb script. Given the probabilistic nature of base calling on the Illumina platform, one can expect a number of reads that were errantly called. For each codon, the expected number of reads due to sequencing noise was calculated with the formula:

NErranttMut=NtWT10μQ-10HD

The number of errant mutants (NErranttMut) can be calculated from the number of observed wild type (NtWT), the average Q score of the sequencing run μQ, and the hamming distance (HD) or number of mutations away from. The number of errant mutants then subtracted from the actual mutant count. In addition to the number of observed wild type, this is calculated for every possible mutation observed, up to the 31 other nucleotide codons, (NNK codons are discarded due to the nature of library construction). Once the total number of errant reads are calculated and subtracted from the mutant and wild-type counts, they are then converted into the amino acid sequence and are saved into text files. These files are then used to load information for calculation of growth rate and allostery.

Calculating relative fitness and final filtering

Growth_Rate_and_Allostery.ipynb

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was the python script used for this analysis. Relative frequency was calculated as follows:

ft=ln⁡NtMut/NtWtNt=0Mut/Nt=0Wt

Variant frequencies (NtMut) were determined relative to WT (NtWt) and normalized to the initial frequency distribution at t=0. The relative growth rate then calculated by linear regression of these normalized frequencies. Light dependence was calculated as the difference between lit vs. dark growth rates. Variant frequency was only calculated if there were more than 50 mutant reads at time zero. Definitions for sector identity, conservation values, and surface identity used in SectorSurfaceDefinitions.ipynb are the same as those from Reynolds et al., 2011. Accessible surface area was calculated using MSMS, using a probe size of 1.4Å and excluding water as well as heteroatoms (Sanner et al., 1996). Values for total surface areas were taken from Chothia, 1976. Together these were used to calculate relative solvent accessible surface area, and 25% was used as a cutoff for 'surface'. A surface site is considered to contact the sector if the atoms comprising the peptide bond contact *any* sector atoms. Contact is defined as the sum of the atom's Pauling radii + 20%.

To determine significant allosteric mutations, a p-value for each mutation was computed by unequal variance t-test under the null hypothesis that the lit and dark replicate measurements have equal means. Two cutoffs were used, a standard cutoff of p=0.05, and a more stringent cutoff that is adjusted to consider multiple hypothesis testing. A multiple-hypothesis testing adjusted p-value of p=0.016 was determined by Sequential Goodness of Fit (Carvajal-Rodriguez and de Uña-Alvarez, 2011). General analysis and figures made from this data are performed in allostery_analysis.ipynb.

Data availability

Sequencing data (resulting from amplicon sequencing) have been deposited in the NCBI SRA under BioProject: PRJNA706683. All analysis codes have been made available as a series of python 3 Jupyter Notebooks on github: https://github.com/reynoldsk/allostery-in-dhfr (copy archived at https://archive.softwareheritage.org/swh:1:rev:dd8ee13f775f8b08548d64868f15e46583cbf543).

The following data sets were generated
    1. McCormick JW
    2. Russo MAX
    3. Thompson S
    4. Blevins A
    5. Reynolds KA
    (2021) NCBI BioProject
    ID PRJNA706683. Effect of saturation mutagenesis to novel allosteric system on allosteric effect.
    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA706683

References

Decision letter

  1. Christian R Landry
    Reviewing Editor; Université Laval, Canada
  2. Michael A Marletta
    Senior Editor; University of California, Berkeley, United States
  3. Georg Hochberg
    Reviewer
  4. Adrian Serohijos
    Reviewer; University of Montreal, Canada

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors use an innovative experimental system to study how mutations can influence allosteric protein regulation. They fuse a light sensitive protein to an enzyme and examine how light can become a regulator of the enzyme. They show that allostery-improving mutations can occur on the surface of the enzyme. These experiments illustrate how allosteric regulation can evolve and be improved by mutations following protein domain fusion. This work is of broad interest for scientists interested in the structural basis of allostery, how it can be engineered into proteins and how it evolves.

Decision letter after peer review:

Thank you for submitting your article "Structurally distributed surface sites tune allosteric regulation" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Michael Marletta as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Georg Hochberg (Reviewer #1); Adrian Serohijos (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. You will see below changes that are recommended by the reviewers and that I would like to request. Some are comments regarding the text, missing information in the figures or supplementary figures that would better fit in the main text. Another one regards the claim about the additive effects of the mutations. The reviewers felt that the experiments or measurements were not designed to test this so it may be better to only discuss this point rather than be affirmative about the lack of non-additivity. Finally, there are few comments on results interpretation and comparison with other, already existing experiments that would help improve the manuscript if dealt with.

Reviewer #1:

McCormick et al. present a very interesting study of how mutations can affect allosteric regulation of proteins. This phenomenon is of interest to biochemists, synthetic biologists, and evolutionary biochemists. Their approach uses a synthetic fusion between an enzyme and a light regulated domain that makes the enzyme weakly regulated by light. James W. McCormick et al. perform a deep mutational single site scan to test whether single mutations in the enzyme domain can enhance or disrupt allostery. They find a small but appreciable fraction that can enhance allostery and that these mutations can in some cases be additive. These findings suggest how allosteric regulation might evolve after domain fusion: by a sequence of small effect, potentially additive substitutions. There is currently very little data about how exactly allostery evolves, so this is a valuable study and of interest to a wide audience.

There are three main conclusions that I judge to be of wide interest.

1) That allostery improving sites fall outside of so-called 'sectors'. These interconnected sets of sites, usually derived from statistical coupling analysis, have been proposed to represent allosteric networks. This works is an interesting challenge to that idea. By a well know practitioner of SCA, no less.

2) That allostery improving mutations are enriched on surface sites and have comparatively small effect sizes.

3) That at least some allostery improving mutations can act additively to produce large dynamic allosteric ranges when introduced together.

Together these data suggest that allostery can be tuned through a gradual evolutionary trajectory, in which many individually small effect steps are taken to make a protein allosteric. This view would be consistent with how early proponents of the modern synthesis would have thought about the emergence of complex traits in general.

The study is in general carefully carried out and the interpretations appropriate and interesting as far as I can tell. I find the system used to study this question quite innovative. Though I will admit that I am no expert in analyzing deep mutational scanning data. I therefore cannot comment with authority on the appropriateness of the analyses that generate fitness values from sequencing data..But based on what I can reasonably judge the major conclusions are supported.

As caveats I can only note that of course this system is somewhat artificial, but the authors are aware of this. They correctly note that it simulates what might happen directly after a domain fusion event that introduces a base level of allostery. Another criticism may be that near-additivity is only shown for a group of three mutations, so the generality of this observation is somewhat unclear. But studies on the evolvability of allostery are still so rare that this is nonetheless a valuable contribution.

I am a bit confused about exactly what correlates with growth rates. The authors initially show that kcat/Km correlates very well with relative growth rate. When they authors then do experiments on the library in the dark and lit state, instead only the kcat value seems to matter. I was confused about this difference and didn't really understand their explanation of it. I'm pretty sure this is just a writing issue and would encourage the authors just to re-read the relevant sections with fresh eyes.

I was also unable to locate the E145R and S148C mutants in Figure 4C. They are indicated as having been characterized in vitro, is there a reason there were left out of the correlation?

Reviewer #2:

Allosteric regulation enables the activity of one site on a protein to modulate function at another spatially distinct site. This work by McCormick et al. studied the spectrum of mutational effects on allosteric regulation by deep mutational scanning of a well-established system of DHFR-LOV2 fused synthetic construct (Dihydrofolate reductase/Light-oxygen-voltage-sensing domain). Major strengths of this work are clear statement of objectives (which mutations improve, decrease or tune an allosteric system and by how much) and well-designed approaches. As in any deep mutational scan study, the crux is in the validity of the selection system. The DHFR-LOV2 construct was previously developed by the last author (Reynolds, Cell 2011) to demonstrate the existence of "protein sectors" or networks of physically contiguous and coevolving amino acids that relay allosteric regulation signals. In addition, the group recently published a plasmid-based based deep mutational scan of DHFR using a folA and thyA knock-out E. coli (Thompson eLife 2020). In essence, by combining the approaches of these two earlier papers, they evaluated the effects of ~1540 single mutations on allostery. These high-throughput measurements were validated and found to be in excellent agreement with 11 DHFR point mutations whose allosteric effects were measured in isolation. Using robust statistical cut-offs, they found that only ~4.5% (69) of mutations significantly influence allostery. Mutations improving allostery are distributed and enriched on the protein surface, while mutations disrupting allostery are enriched in the LOV2 insertion site. The latter is perhaps not surprising, since LOV2 insertion at this site could have perturbed residue contacts in DHFR crucial for allosteric regulation. This also provides a valuable lesson that synthetic-coupled domains can be furthered engineered at the fusion site to improve regulatory control.

It remains to be seen whether the findings from DHFR-LOV2 construct is generalizable to other proteins or to protein complexes that evolved naturally. But overall, this is a significant work towards our mechanistic understanding not only on the structural basis of protein allostery, but also of its evolution and potential optimization and design.

1) The authors claim that allosteric effects of mutations are additive (non-epistatic), which is quite profound and unintuitive. However, in there data, there is a difference between measured and expectations from log-additivity of the double (M16A,H124Q) and triple (M16A,D87A,H124Q) mutants (Figure 6B). This difference suggest epitasis for allostery. The authors may want to clarify this apparent discrepancy.

2) An interesting result is the stronger effect of allostery on kcat but not on Km. The authors surmise this from the in vivo concentration of DHF in E. coli, which is ~25 μm and well above the measured Km. In the selection system, the proteins are expressed on plasmids, presumably at concentrations higher than endogenous in vivo concentrations of DHFR. Might the protein abundance due to expression on plasmid affect this particular result?

3) Since there is a prior DHFR deep mutational scan (Thompson eLife 2020), which estimated the effects of mutations in vivo activity. I believe it would have been enlightening to correlate and compare those results with the mutational effects measured for allostery. This could shed light on the pleiotropic effects of mutations on protein properties (e.g., activity vs. allostery).

4) Overall, the manuscript is written logically and very clearly. However, the authors should consider promoting Supplementary Figure S4 as a panel in Figure 3 or Figure 4. The supplementary figure provides a compelling picture of the "allosteric" landscape of DHFR.

5) Reference 19 is not shown correctly.

Reviewer #3:

The authors report four key findings: 1) A highly quantitative high-throughput assay for this enzyme's function; 2) Relatively few mutants affected allostery, and those that did had a small effect; 3) Allostery-disrupting mutations tended to be near the active site; 4) Allostery-enhancing mutations tended to be on the surface.

On the whole, their conclusions were amply justified. The collected data were extraordinarily clean, and the analysis refreshingly transparent. The authors do an excellent job supporting each of their claims with evidence. The manuscript is well-written and the figures are clear. The authors do a good job in their supplement of showing the material, controls, and methods necessary to reproduce their work.

The work is well-done, but not flashy. The effect-sizes were small and the structural patterns of the mutational effects relatively weak. The sheer quality of the work, however, makes it an important and interesting addition to the literature. Further, the structural pattern of allostery-enhancement on the surface and not in "sectors" detected by co-evolution was intriguing and somewhat unexpected.

1. I think the manuscript could benefit from a discussion of context. The number of allostery-altering mutations and effect-sizes are small. Is this unexpected? The authors note that their results could come from the fact that they are in an artificial, unoptimized system. But what sorts of numbers/magnitudes would they expect in a naturally evolved or optimized protein?

2. I also felt there was a relative paucity of structural rationale for their findings. Do the authors have any hypotheses for why surface positions would be uniquely good at enhancing allostery in this context? They note several other studies that found unconserved sites were a bit better at tuning allostery than conserved. Does their new work provide any insights (or hints of insights) as to why this would hold?

https://doi.org/10.7554/eLife.68346.sa1

Author response

Reviewer #1:

[…]

I am a bit confused about exactly what correlates with growth rates. The authors initially show that kcat/Km correlates very well with relative growth rate. When the authors then do experiments on the library in the dark and lit state, instead only the kcat value seems to matter. I was confused about this difference and didn't really understand their explanation of it. I'm pretty sure this is just a writing issue and would encourage the authors just to re-read the relevant sections with fresh eyes.

We would like to thank the reviewer for raising this important point. Our control library consists of well characterized DHFR point mutants chosen to span a range of values for both kcat (0.05-79.2 s-1) and Km (330-1.1 µM). Somewhat by serendipity, the assumption that all of these enzyme mutations operate in a first order kinetics regime where kcat and Km contribute equally to growth rate (in the plot of growth rate vs catalytic power) yielded a high correlation.

However, the DL121 fusion construct and all characterized mutations have a Km less than 2, far below the reported in vivo DHF concentration of 25µM in E. coli (Kwon et al., 2008, Nat Chem Biol v4:602-608). If these values are accurate in vivo, we would expect the DHFR domain of our chimera to operate under a pseudo-zero-order regime where perturbations to kcat are disproportionately or entirely responsible for alterations in growth rate. Indeed, the correlation plots in Figure 4—figure supplement 6 lend evidence to this explanation. As reviewer 1 has rightly identified, this has resulted in confusion and serves to add a layer of difficulty for the reader.

To rectify this we have simplified the main text to use only velocity (V = Vmax[S]/(Km + [S])) at 25µM DHF to compare with growth rate, and we now use the ratio in lit/dark velocity to quantify the allosteric effect. We have altered figures 1D, 2C, 4D, 6B, as well as the text to reflect this change. The original version of Figure 4D has been moved to the supplement (Figure 4—figure supplement 6). This change allows us to relate growth rate to a consistent biochemical measure in all figures.

In using velocity to describe our data, we have incorporated two assumptions: 1) we presume minimal variation in protein abundance between mutants (enzyme concentration is equal to one) and 2) we fix the substrate concentration at 25 µM. These assumptions are described in the main text, on page 7, lines 140-146. The strong correlation between velocity and our experimentally measured growth rates (Figure 2C) indicates these assumptions are reasonable. Additionally, the observed correlation between the allosteric effect as measured in vitro and in vivo (Figure 4D) is robust to variation in the concentration of DHF above ~5 µM. Thus, the precise choice of substrate concentration is not critical to our claims (see Author response image 1). Given that presenting our results in terms of velocity allows for a more consistent presentation of the data, we have chosen to go forward with these assumptions.

Author response image 1
Download asset Open asset

I was also unable to locate the E145R and S148C mutants in Figure 4C. They are indicated as having been characterized in vitro, is there a reason there were left out of the correlation?

While E154R and S148C were selected for in vitro characterization, we were unable to purify active protein at quantities needed for kinetics. We found that E154R and S148C fell out of solution under several different purification strategies. Given that E154R is a high confidence allostery-enhancing mutation in our in vivo experiments, we opted to report our failed attempt at purification as the observed instability is suggestive of potential allosteric mechanism. In response to the concern raised by reviewer 1, we have expanded our discussion of these mutants on page 14, line 264 as well as mentioning them in the figure legend of 4B.

Page 14 – lines 267-271:

Modified from: “We expressed and purified the selected DL121 mutants to near homogeneity; S148C and E154R did not yield sufficient quantities of active protein for in vitro studies.”

To: “We expressed and purified the selected DL121 mutants to near homogeneity; S148C and E154R did not yield sufficient quantities of active protein for in vitro studies. We find it noteworthy that E154R – one of the strongest allostery-enhancing mutations in vivo – was unstable in multiple purification strategies.”

Updated figure legend 4B to include:

“S148C and E154R did not yield sufficient quantities of active protein for further in vitro characterization.”

Reviewer #2:

[…]

1) The authors claim that allosteric effects of mutations are additive (non-epistatic), which is quite profound and unintuitive. However, in there data, there is a difference between measured and expectations from log-additivity of the double (M16A,H124Q) and triple (M16A,D87A,H124Q) mutants (Figure 6B). This difference suggest epitasis for allostery. The authors may want to clarify this apparent discrepancy.

We agree that additivity in the allosteric effects of single mutations is unintuitive. However, when considering error in the measurements, the difference between the log-additive expectation (given single mutation data) and the experimentally observed allosteric effect for the mutant combinations is statistically insignificant. This was true whether we considered the ratio of lit and dark kcat values (as in the original Figure 6B), or the ratio of lit and dark velocities (as in the updated Figure 6B). For clarity we have added the phrase: “There is not a statistically significant difference between the expected and observed allosteric effects (p = 0.07 for M16A,H124Q, p=0.48 for M16A,D87A,H124Q; as computed by unpaired t-test).” To the figure legend of Figure 6B.

2) An interesting result is the stronger effect of allostery on kcat but not on Km. The authors surmise this from the in vivo concentration of DHF in E. coli, which is ~25 μm and well above the measured Km. In the selection system, the proteins are expressed on plasmids, presumably at concentrations higher than endogenous in vivo concentrations of DHFR. Might the protein abundance due to expression on plasmid affect this particular result?

Here, we understand the reviewer is asking about potential explanations for the fact that all of the biochemically characterized mutations modulated allostery through kcat rather than Km. In the manuscript, we suggest this could be a consequence of in vivo experimental conditions that reflect pseudo-zero-order kinetics ([DHF] >> Km). In this scenario, Km has little impact on enzyme velocity (and thus growth rate), and we would expect that our assay would predominantly detect mutations effecting kcat. This idea is loosely supported by the fact that the in vivo concentration of DHF in wildtype E. coli is 25 µM, an order of magnitude above the measured Km for the characterized DL121 mutations (~1 µM).

However, as the reviewer correctly points out, our system differs from wild type E. coli in several important ways: 1) the DL121 chimera has much-reduced activity relative to native DHFR, 2) it is expressed on a low-copy plasmid using a non-native promoter, and 3) the plasmid also contains the gene encoding thymidylate synthase, a folate metabolic enzyme which produces DHF (thyA is deleted from the chromosome in our selection strain). It is difficult to intuit what the combined effect of these changes on DHF concentration might be, and quite possible that the in vivo concentration of DHF varies from the 25 µM measurement made for wild type E. coli… potentially even in a mutant-specific fashion. Nevertheless, the strong correlation between DHFR velocity (calculated at 25 µM DHF) and relative growth rate suggests that 25 µM is a reasonable choice (new Figure 2C). Importantly, we observe that the correlation between allosteric effect (as measured in vivo) and the ratio of lit:dark enzyme velocities (as determined in vitro) is stable across a broad range of intracellular DHF concentrations (>5 µM, see also the response to reviewer 1), so our results do not strongly depend on a specific choice of DHF concentration. Thus, we feel our original explanation is plausible, though the present data do not conclusively demonstrate it.

Alternatively, it could be that the allosteric mutations predominantly impact kcat for biophysical reasons. Perhaps the placement of the LOV2 domain makes it more energetically feasible for light to modulate kcat than Km. Our result that distributed surface mutations modulate allosteric regulation requires additional mechanistic explanation, and this is something we hope to address in future work. We now include a more developed and nuanced explanation of these ideas on page 14, line 285 – page 15, line 336:

“So why might the characterized allosteric mutations predominantly effect kcat? One plausible explanation is that the conditions of our in vivo experiments fall within a pseudo-zero-order kinetics regime ([DHF] >> Km). […] In any case, the 1.3 to 2 fold changes in kcat translate to similar fold changes in enzyme velocity”.

3) Since there is a prior DHFR deep mutational scan (Thompson eLife 2020), which estimated the effects of mutations in vivo activity. I believe it would have been enlightening to correlate and compare those results with the mutational effects measured for allostery. This could shed light on the pleiotropic effects of mutations on protein properties (e.g., activity vs. allostery).

In Thompson et al., the authors (which partly overlap with those of this manuscript) investigated how the presence of Lon protease reshapes the single mutation fitness landscape of DHFR. The selection conditions for that experiment were chosen to resolve changes in catalytic activity near WT DHFR, while the conditions of the present work (McCormick et al) were chosen to resolve changes in activity near the less-active DL121 chimera. Accordingly, these experiments differ in the stringency of selection in two ways: (1) McCormick et al. included 1 µg/mL thymidine supplementation in the media, while Thompson et al. did not (2) In the McCormick et al. experiments, DHFR is translated from a Shine-Delgarno consensus ribosome binding site (RBS) with high translation initiation rate, while the construct in Thompson et al. makes use of an RBS that is predicted to have 0.05x translation rate (by the RBS calculator, Salis et al., 2009, Nat Biotech v27:946). Beyond these differences in experimental setup, the LOV2 insertion in DL121 renders the DHFR domain less active, and potentially less stable. For these reasons, a strong relationship between the two datasets is not expected.

Nonetheless, we very much agree with the reviewer that it is important to have a look at the two datasets together and see what can be learned. We scaled each deep mutational scanning dataset by the appropriate reference WT growth rate (unmutated DL121 for McCormick et al., WT DHFR for Thompson et al) to facilitate comparison. In the following plot, zero represents non-growing mutations, while one represents WT-like growth. DL121 mutations classified as slow-growing are not shown (growth rate < DL121 D27N).

The data reveal that DL121 was far less robust to mutation than native DHFR. Native DHFR exhibited a peak of relative growth rates near one, with a tail of deleterious mutations. In contrast, the distribution of fitness effects for DL121 was strongly skewed towards deleterious mutations with no observable peak near one. Indeed, many of the mutations that were deleterious to DL121 activity displayed growth rates near WT in the context of native DHFR (lower right quadrant of Author response image 2).

Author response image 2
Download asset Open asset

Importantly, the set of null mutations for each experiment (those mutations which were too deleterious to fit a reliable growth rate) show statistically significant overlap (p-value = 0.0002), indicating that the mutations yielding gross disruptions to activity are largely the same. There was no relationship between DHFR mutations that significantly altered Lon protease sensitivity and DL121 mutations that significantly altered allostery (p-value = 0.826). Due to the significant differences in the model system, experimental design, and limited comparability between the two sets of experiments, we have refrained from including a comparison and a discussion of Thompson et al. in the main text.

4) Overall, the manuscript is written logically and very clearly. However, the authors should consider promoting Supplementary Figure S4 as a panel in Figure 3 or Figure 4. The supplementary figure provides a compelling picture of the "allosteric" landscape of DHFR.

In reflection, we agree and have implemented this suggestion. The heatmap of allosteric effects was moved from (former) Supplemental Figure 4C to Figure 4A and the text has been accordingly updated.

5) Reference 19 is not shown correctly.

This has been fixed. Thank you for bringing this to our attention.

Reviewer #3:

[…] 1. I think the manuscript could benefit from a discussion of context. The number of allostery-altering mutations and effect-sizes are small. Is this unexpected? The authors note that their results could come from the fact that they are in an artificial, unoptimized system. But what sorts of numbers/magnitudes would they expect in a naturally evolved or optimized protein?

The number and effect size of allosteric mutations in naturally evolved systems still remains relatively uncharacterized. Quantifying the frequency of allostery-altering mutations requires a comprehensive single mutation study. We are aware of only two near-comprehensive mutagenesis experiments that have directly measured the allosteric effects of mutations in native proteins. The first is an alanine scan of human liver pyruvate kinase wherein allosteric effect was characterized in vitro (Tang and Fenton, 2017, Hum Mutat. 38:1132). The second is the pioneering work of Jeffrey Miller on lac repressor (Kleina et al., 1990, JMB 212:295, Markiewicz et al., 1994, JMB 240:421, Suckow et al., 1996, JMB 261:509). In the lac repressor studies, mutations that impact allostery are categorized with those that fail to bind inducer, so it is difficult to distill out the separate roles of mutations. For pyruvate kinase, only ~5% of mutations effected allosteric inhibition by alanine, while nearly ~30% impacted allosteric activation by fructose-1,6-bisphosphate. Given this, it remains difficult to define a general expectation for how many mutations tune allostery in an evolved system. However, the results on pyruvate kinase suggest it might be higher than what we observed for DL121.

These same studies also provide some insight into mutational effect sizes, and suggest that effect sizes in natural proteins can be larger than what we observed here. Tang et al. write that “most mutations that increased allosteric inhibition by alanine only did so marginally”, though they did identify a single mutation that increased alanine inhibition by six-fold. They also reported a single mutation of pyruvate kinase that increased allosteric activation by 22-fold. With further study – including deep mutational scans of natural allosteric systems – we should gain a better appreciation of how our results (in a synthetic system) compare to more optimized or evolved systems.

To provide this additional context, we have added a reference to Tang and Fenton, on page 22 – lines 447-450:

“While we observed that the number of allosteric mutations is few and the effect sizes are generally small, a previous study of allostery tuning mutations in pyruvate kinase indicated that up to 30% of mutations can tune allostery, with the maximum effect size approaching 22-fold [54].”

2. I also felt there was a relative paucity of structural rationale for their findings. Do the authors have any hypotheses for why surface positions would be uniquely good at enhancing allostery in this context? They note several other studies that found unconserved sites were a bit better at tuning allostery than conserved. Does their new work provide any insights (or hints of insights) as to why this would hold?

Yes, we agree with the reviewer – we intentionally provide only limited structural rationale for our findings. While the experiments here map the extent, distribution, and strength of allosteric mutations on an unoptimized allosteric system, they do not provide information on the mechanism by which this allosteric signal is modulated. Beyond briefly mentioning a possible role for solvent and the protein hydration layer (p.24, lines 485-487), we have refrained from commenting about the structural mechanism of allostery from a concern of unsupported claims and misrepresenting our findings. While a mechanistic analysis of these mutations is out of the scope of this work, we strongly agree our findings now demand biophysical explanation. This is an active area of interest and the subject of ongoing research in our laboratory.

https://doi.org/10.7554/eLife.68346.sa2

Article and author information

Author details

  1. James W McCormick

    1. The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7573-2300

  2. Marielle AX Russo

    1. The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  3. Samuel Thompson

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Resources, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  4. Aubrie Blevins

    The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared

  5. Kimberly A Reynolds

    1. The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    kimberly.reynolds@utsouthwestern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4805-0317

Funding

National Science Foundation (CAREER Award 1942354)

  • Kimberly A Reynolds

Gordon and Betty Moore Foundation (Data Driven Discovery Initiative GBMF4557)

  • Kimberly A Reynolds

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors are grateful to Dr. Tanja Kortemme for facilitating our collaboration with Samuel Thompson. We also acknowledge Dr. Elliott Ross and Dr. Rama Ranganathan for thoughtful discussion and feedback. We thank Christine Ingle for her assistance with DHFR purification and kinetics protocols, and other members of the Reynolds lab for comments on the manuscript and discussions throughout the development of this work. FUNDING This work was supported by NSF Grant # 1942354 to KAR, and in part by the Gordon and Betty Moore Foundation’s Data Driven Discovery Initiative through grant GBMF4557 to KAR.

Senior Editor

  1. Michael A Marletta, University of California, Berkeley, United States

Reviewing Editor

  1. Christian R Landry, Université Laval, Canada

Reviewers

  1. Georg Hochberg
  2. Adrian Serohijos, University of Montreal, Canada

Version history

  1. Received: March 12, 2021
  2. Accepted: June 15, 2021
  3. Accepted Manuscript published: June 16, 2021 (version 1)
  4. Version of Record published: July 30, 2021 (version 2)

Copyright

© 2021, McCormick et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. James W McCormick
  2. Marielle AX Russo
  3. Samuel Thompson
  4. Aubrie Blevins
  5. Kimberly A Reynolds
(2021)
Structurally distributed surface sites tune allosteric regulation
eLife 10:e68346.
https://doi.org/10.7554/eLife.68346

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