High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

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Version of Record published: March 31, 2023 (This version)
Accepted Manuscript published: March 16, 2023 (Go to version)
Accepted: March 15, 2023
Received: August 1, 2022
Preprint posted: August 1, 2022 (Go to version)

1. Of interest
Status and physiological significance of circulating adiponectin in the very old and centenarians: an observational study

Takashi Sasaki, Yoshinori Nishimoto ... Yasumichi Arai

Research Article Sep 28, 2023
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Abstract
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Results and discussion
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Abstract

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

Editor's evaluation

This paper reports an improved bacterial surface peptide display technology and its use to survey the primary sequence specificities of a broad range of tyrosine kinases and to assess the effects of naturally-occurring positional variations around sites of tyrosine phosphorylation on the efficiency of phosphorylation. The versatility of this approach was demonstrated by using expanded genetic code technology to investigate the consequences of installing post-translationally modified amino acids, such as acetyl-lysine, at positions upstream and downstream of a target tyrosine on the efficiency of phosphorylation by different tyrosine kinases. In addition, pre-phosphorylated surface peptide display libraries were exploited to interrogate the primary sequence binding specificities of SH2 phosphotyrosine-binding domains.

https://doi.org/10.7554/eLife.82345.sa0

Introduction

Cells respond to external stimuli by activating a finely-tuned cascade of enzymatic reactions and protein-protein interactions. This signal transduction is governed, in large part, by post-translational modifications that alter protein activity, stability, and localization, as well as the formation of higher-order macromolecular complexes. Despite its low abundance relative to serine and threonine phosphorylation, tyrosine phosphorylation is an essential post-translational modification in metazoans (Lim and Pawson, 2010). Tyrosine kinases, the enzymes that phosphorylate tyrosine residues on proteins, and Src homology 2 (SH2) domains, protein modules that bind tyrosine-phosphorylated sequences, must have the ability to discriminate among a myriad of potential phosphorylation sites (phosphosites) in the proteome, in order to ensure proper signal transduction. The preferential engagement of specific phosphosites by tyrosine kinases and SH2 domains is dependent on the amino acid sequence surrounding the tyrosine or phosphotyrosine residue (Songyang et al., 1995; Songyang et al., 1993).

Isolated tyrosine kinase domains most efficiently engage phosphosites that conform to specific sequence motifs, which are defined by a small number of key residues that contribute significantly to recognition (Songyang et al., 1995). These motifs suggest a mechanism by which a specific set of phosphosites in a proteome is selectively engaged by an individual kinase, based on the presence of favorable sequence features around that site. Negative selection of specific sequence features can also play a role in kinase specificity (Alexander et al., 2011). For example, the T cell tyrosine kinase ZAP-70 cannot readily phosphorylate co-localized proteins that contain even a modest positive charge (Shah et al., 2016).

Phosphosite sequence recognition by kinase domains is just one mechanism of substrate selection for tyrosine kinases, and other interactions are necessary to achieve efficient substrate targeting in vivo. Binding domains, such as SH2 domains, can strongly influence specificity by localizing kinases to the vicinity of phosphorylation targets (Pawson and Nash, 2000). Secondary interactions between SH2 and kinase domains can also refine the substrate preferences of a tyrosine kinase by stabilizing its active state (Filippakopoulos et al., 2008). Thus, for signaling systems that involve a tyrosine kinase domain and a tethered SH2 domain, the sequence specificities of both domains contribute to the intricate control of phosphotyrosine signaling responses.

Many methods have been developed to characterize sequence recognition by tyrosine kinases and SH2 domains. The most prominent approach employs purified kinases/SH2 domains and oriented peptide libraries, which are synthetic, degenerate peptide libraries with a central tyrosine or phosphotyrosine residue (Songyang et al., 1995; Songyang et al., 1993). Several variations on this technique have been reported to improve the throughput and quantification of sequence preferences (Deng et al., 2014; Huang et al., 2008; Hutti et al., 2004; Mok et al., 2010). Notably, this method is also applicable to serine/threonine kinases, and large swaths of the yeast and human kinomes have been characterized using oriented peptide libraries, providing significant insights into kinase-substrate recognition and phospho-signaling (Deng et al., 2014; Johnson et al., 2023; Mok et al., 2010; Songyang et al., 1995). Oriented peptide library screens have primarily been useful for determining the preference for each amino acid at a given position, independent of sequence context, but evidence suggests that some amino acid preferences may depend on the surrounding sequence (Cantor et al., 2018).

Several groups have developed strategies to compare the phosphorylation of specific sequences, rather than obtain position-averaged amino acid preferences from pooled degenerate libraries. Strategies include ‘one-bead-one-peptide’ combinatorial libraries (Imhof et al., 2006; Ren et al., 2011; Sweeney et al., 2005; Trinh et al., 2013; Wavreille et al., 2007) and protein/peptide microarrays (Amanchy et al., 2008; Jones et al., 2006; Koytiger et al., 2013; Mok et al., 2009; Schutkowski et al., 2004; Uttamchandani et al., 2003). One-bead-one-peptide methods often require manual isolation and individual sequencing of positive (phosphorylated or SH2-bound) beads, making the method technically challenging. Microarrays offer the capacity to analyze thousands of discrete sequences and require small quantities of proteins, but their use can be limited by the high cost of reagents. As an alternative, several groups have conducted mass spectrometry proteomics on heterologously expressed purified peptide libraries, kinase-treated cell extracts, and cells over-expressing a kinase of interest (Barber et al., 2018; Chou et al., 2012; Corwin et al., 2017; Douglass et al., 2012; Finneran et al., 2020; Imamura et al., 2014; Kettenbach et al., 2012; Lubner et al., 2018; Sugiyama et al., 2019; Xue et al., 2012). This strategy has enabled the identification of potential substrates and can also be used to infer position-specific amino acid preferences. Studies using intact proteomes have the added benefit that the kinase of interest is operating on intact proteins, rather than isolated peptides, but interpretation of the results can be convoluted by the presence of endogenous kinases.

Molecular display techniques, such as mRNA, phage, yeast, and bacterial display, have also been used for specificity profiling. Early investigations employed phage or mRNA display to profile tyrosine kinase and SH2 specificity. These methods were relatively low-throughput, as they relied on Sanger sequencing of individual clones (Cujec et al., 2002; Dente et al., 1997). The advent of deep sequencing technologies has transformed this style of specificity profiling, by enabling rapid, quantitative analysis of library composition without requiring the sequencing of individual clones. This was demonstrated recently in a series of studies that employed bacterial/yeast peptide display, fluorescence-activated cell sorting (FACS), and deep sequencing to profile tyrosine kinase and SH2 domain specificity (Cantor et al., 2018; Lo et al., 2019; Shah et al., 2018; Shah et al., 2016; Taft et al., 2019). A key facet of these investigations was the facile generation of peptide libraries tailored to specific mechanistic questions: these included scanning mutagenesis libraries derived from individual substrates (Shah et al., 2016), as well as diverse peptide libraries encoding known phosphosites in the human proteome (Shah et al., 2018).

In this report, we describe a high-throughput platform to profile the recognition of large peptide libraries by any tyrosine kinase or SH2 domain. Our approach uses biotinylated bait proteins (pan-phosphotyrosine antibodies or SH2 domains) and avidin-functionalized magnetic beads to isolate tyrosine kinase-phosphorylated bacterial cells, and is coupled to deep sequencing for a quantitative readout (Figure 1A). The use of magnetic bead-based separation, rather than FACS, permits simultaneous, benchtop processing of multiple samples and enables the analysis of larger libraries for less time and cost. Libraries can be custom-made for specific readouts: mutational scanning for structure-activity relationships, libraries derived from natural proteomes to answer specific signaling questions, or degenerate libraries for the generation of predictive models.

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High-throughput profiling of tyrosine kinase substrate specificity using bacterial peptide display.

(A) Schematic representation of the workflow for kinase specificity profiling. (B) Heatmap depicting the specificity of the c-Src kinase domain, measured using the X₅-Y-X₅ library. Enrichment scores were log₂-transformed and are displayed on a color scale from blue (disfavored sequence features, negative value), to white (neutral sequence features, near zero value), to red (favored sequence features, positive value). Values in the heatmap are the average of three replicates. (C) Correlation between position-specific amino acid enrichments from screens with the 4G10 Platinum and PY20 biotinylated pan-phosphotyrosine antibodies.

To demonstrate the versatility of our approach, we designed two new bacterial peptide display libraries that provide distinct insights into tyrosine kinase and SH2 sequence recognition. The first library contains 10⁶–107 random 11-residue sequences with a central tyrosine (referred to as the X₅-Y-X₅ library). Screens with the X₅-Y-X₅ library recapitulate previously reported specificity motifs and can be used to generate highly efficient peptide substrates. The second library contains defined sequences spanning 3000 human tyrosine phosphorylation sites, along with 5000 variant sequences bearing disease-associated mutations and natural polymorphisms (referred to as the pTyr-Var library). Kinase and SH2 screens with the pTyr-Var library reveal hundreds of phosphosite-proximal mutations that significantly impact phosphosite recognition by individual protein domains. These datasets will be a valuable resource in the growing efforts to understand the functional impact of protein variants across the human population that may contribute to disease (Stein et al., 2019). Finally, we show that our peptide display platform is compatible with Amber codon suppression, enabling analysis of how non-canonical or post-translationally modified amino acids impact sequence recognition. Overall, the method described in this report provides an accessible, high-throughput platform to study the specificity of phosphotyrosine signaling proteins.

Results and discussion

A bacterial display and deep sequencing platform to screen tyrosine kinases against large peptide libraries

We expanded upon a previously established screening platform that combines bacterial display of genetically encoded peptide libraries and deep sequencing to quantitatively compare phosphorylation efficiencies across a substrate library (Shah et al., 2016). In the published approach, peptides are displayed on the surface of E. coli cells as fusions to an engineered bacterial surface-display protein, eCPX (Rice and Daugherty, 2008), then phosphorylated by a purified kinase (Henriques et al., 2013). Following this, the cells are labeled with a pan-phosphotyrosine antibody, and cells with high phosphorylation levels are separated by FACS. The DNA encoding the peptides is then amplified and analyzed by Illumina deep sequencing to determine the frequency of each peptide in the library before and after selection (Shah et al., 2018; Shah et al., 2016). In order to determine the phosphorylation efficiency of each peptide by a particular kinase, an enrichment score is determined by calculating the frequency of that peptide in the kinase-selected sample normalized to the frequency in the input sample.

While peptide libraries of virtually any composition can theoretically be screened using this approach, previous implementations focused on libraries containing less than 5000 peptides, due to the low throughput of FACS (Shah et al., 2018). In those experiments, the objective was to over-sample the library at the cell sorting step by a factor of 100–1000, to ensure that enrichment or depletion of every member of the library could be accurately quantified by deep sequencing. When multiple screens were conducted in parallel, the throughput of FACS limited experiments to small libraries (less than 5000 sequences). To improve the scalability and cost-effectiveness of this approach, we switched to a bead-based sorting method, using avidin-coated magnetic beads to enrich highly-phosphorylated cells, thus circumventing the need for FACS (Figure 1A). With this approach, the cells are instead labeled with biotinylated pan-phosphotyrosine antibodies and then sorted using magnetic beads. The use of magnetic beads permits simultaneous separation of multiple samples of virtually any size, enabling larger library analysis for less time and cost. Notably, these screens can be carried out in any laboratory, without the need for a fluorescence-activated cell sorter.

To test our upgraded screening platform, we generated a random library of 11-residue sequences with a central tyrosine (the X₅-Y-X₅ library, where X is any of the 20 canonical amino acids). The library was generated using a degenerate synthetic oligonucleotide with five NNS codons (N=A,T,G,C and S=G,C) before and after the central codon that encodes for tyrosine (TAT). The NNS triplet has the benefit of encoding all 20 amino acids, but it can still contain an Amber stop codon (TAG) roughly 3% of the time. Therefore, up to 30% of the peptide-coding sequences in the library are expected to have an Amber stop codon – a feature that we take advantage of later in this study. The degenerate oligonucleotide mixture was cloned into a plasmid in between the DNA encoding a signal sequence and the eCPX surface-display scaffold. In a previously reported version of this platform, the eCPX scaffold contained a C-terminal strep-tag to detect surface-display level (Shah et al., 2016). Due to the potential background binding of the strep-tag with the avidin-coated magnetic beads during cell enrichment, we cloned both a strep-tagged and a myc-tagged version of the library. Deep sequencing of both versions of the X₅-Y-X₅ library confirmed that they have 1–10 million unique peptide sequences, 20% of which contain one or more stop codons. Furthermore, all 20 canonical amino acids were well-represented at each of the 10 variable positions surrounding the fixed tyrosine residue (Figure 1—figure supplement 1). Notably, our library includes peptides containing Cys residues and non-central Tyr residues, both of which are often excluded from tyrosine kinase specificity screens to avoid oxidation-related artifacts and challenges in interpreting signal from multi-Tyr sequences (Deng et al., 2014). These sequences can be filtered during data analysis, if needed, although they did not pose significant issues in our studies.

Using the myc-tagged X₅-Y-X₅ library, we determined the position-specific amino acid preferences of the kinase domain of c-Src. Cells displaying the library were phosphorylated by c-Src to achieve roughly 20–30% phosphorylation, as determined by flow cytometry (Figure 1—figure supplement 2). The phosphorylated cells were labeled with a biotinylated anti-phosphotyrosine antibody and enriched with magnetic beads, then peptide-coding DNA sequences were counted by deep sequencing. We visualized the sequence preferences of c-Src by generating a heatmap and sequence logo based on the position-specific enrichment scores of each amino acid residue surrounding the central tyrosine (Figure 1B, Figure 1—figure supplement 3). Sequences containing a stop codon were not considered in these calculations, but the depletion of stop codons at each position was separately confirmed and is reported below the heatmap on the same color scale. The preferences determined from this screen matched the sequence specificity of c-Src defined by prior reports using oriented peptide libraries (Deng et al., 2014; Songyang et al., 1995). We observed a strong preference for bulky aliphatic residues (Ile/Leu/Val) at the –1 position relative to the central tyrosine and a phenylalanine at the +3 position (Figure 1B, Figure 1—figure supplement 3). Our results showed modest differences from the specificity observed by oriented peptide libraries, including a strong preference for a+1 Asp/Glu/Ser in addition to the previously reported +1 Gly. To test whether these differences were due to biases introduced by the specific pan-phosphotyrosine antibody used, we obtained a different commercially available biotinylated pan-phosphotyrosine antibody and repeated the screen. The position-specific amino acid enrichments obtained using both antibodies were nearly identical (Figure 1C). This suggests that there is no significant bias in the enrichment of peptides introduced by the pan-phosphotyrosine antibody.

Degenerate library screens capture specificity profiles for diverse tyrosine kinases

We next used the degenerate X₅-Y-X₅ library to characterize the sequence preferences of four additional tyrosine kinase domains, derived from the non-receptor tyrosine kinases c-Abl and Fer, and the receptor tyrosine kinases EPHB1 and EPHB2. The kinases were selected because they represent a few distinct branches of the tyrosine kinome and can be easily produced through bacterial expression (Albanese et al., 2018). The X₅-Y-X₅ library was screened against the kinases in triplicate, and the data from replicates were averaged to generate specificity profiles for each kinase (Figure 2A and Figure 2—source data 1). The amino acid preferences for c-Abl are well-characterized and were recapitulated in this screen (Deng et al., 2014; Songyang et al., 1995; Till et al., 1999; Till et al., 1994). Like c-Src, c-Abl preferred bulky aliphatic residues at the –1 position with respect to the central tyrosine. Unlike c-Src, c-Abl preferred an alanine at the +1 position and had a notably strong preference for proline at the +3 position (Figures 1B and 2A, Figure 2—figure supplement 1). Fer showed a specificity pattern distinct from both c-Src and c-Abl, which included a preference for tryptophan residues at the +2,+3, and +4 positions. As expected, the closely related EPHB1 and EPHB2 kinases had similar specificities, which included a unique preference for Asn and Asp at the –1 residue that was not observed for the tested non-receptor tyrosine kinases (Figure 2A, Figure 2—figure supplement 1).

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Specificity profiling of tyrosine kinases using the X₅-Y-X₅ library.

(A) Heatmaps depicting the specificities of c-Abl, Fer, EPHB1, and EPHB2. Enrichment scores were log₂-transformed and are displayed on a color scale from blue (disfavored sequence features, negative value), to white (neutral sequence features, near zero value), to red (favored sequence features, positive value). Values in the heatmaps are the average of three replicates. (B) Sequences of consensus peptides identified through X₅-Y-X₅ screens, compared with previously reported SrcTide and AblTide sequences. (C) Phosphorylation kinetics of five consensus peptides against five kinases. Initial rates were normalized to the rate of the cognate consensus peptide. All peptides were used at a concentration of 100 μM, and the kinases were used at a concentration of 10–50 nM. Error bars represent the standard deviation from at least three measurements.

Figure 2—source data 1 Position-specific amino acid enrichment matrices from the tyrosine kinase X₅-Y-X₅ library screens. Matrices calculated with and without inclusion of multi-tyrosine sequences are provided.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig2-data1-v2.xlsx
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Degenerate library screens can be used to design highly-efficient peptide substrates

Specificity profiling methods are often used to design consensus sequences that serve as optimal peptide substrates for biochemical assays and biosensor design (Deng et al., 2014; Lin et al., 2019; Songyang et al., 1995). We wanted to assess whether our method could also be used to generate high-efficiency substrates, and whether these would differ from sequences identified using oriented peptide libraries. To test this, we combined the most favorable amino acids in each position flanking the central tyrosine residue in our specificity profiles, excluding tyrosine, to generate unique consensus peptide substrates for c-Src, c-Abl, Fer, EPHB1, and EPHB2. Consensus sequences for c-Src and c-Abl have been identified previously using oriented peptide libraries (Deng et al., 2014; Songyang et al., 1995). These sequences, often referred to as SrcTide and AblTide, are different than our consensus sequences at a few residues surrounding the phospho-acceptor tyrosine (Figure 2B). The SrcTide and AblTide peptides are canonically embedded within a conserved peptide scaffold containing N-terminal Gly and C-terminal (Lys)₃-Gly flanks. For direct comparison, we embedded our consensus peptides in the same scaffold and conducted a series of kinetic studies.

First, we used an in vitro continuous fluorimetric assay to compare the steady-state kinetic parameters (k_cat and K_M) for our c-Src and c-Abl consensus peptides with the SrcTide and AblTide peptides. The Michaelis-Menten parameters for our Src Consensus peptide were on par with one of the previously reported SrcTide substrates (SrcTide 1995, Songyang et al., 1995), but the K_M value for a more recently reported SrcTide variant (SrcTide 2014, Deng et al., 2014) was substantially tighter (Table 1). Our Src Consensus peptide had a higher maximal catalytic rate (k_cat) but a lower apparent binding affinity (K_M) when compared to both SrcTides. We were surprised to see that our Src Consensus peptide had a+1 Asp residue, as opposed to the +1 Gly residue in both SrcTides. Substitution of the +1 Asp for a Gly in a related peptide marginally improved the K_M value but reduced k_cat (Table 1). These results indicate that our c-Src specificity screens may select for peptides with a high k_cat, and that there is a trade-off between k_cat and K_M for c-Src substrate recognition. For c-Abl, our consensus peptide had both a higher maximal rate (k_cat) and tighter apparent affinity (K_M) relative to the previously reported AblTide peptide (Table 1). Collectively, these experiments suggest that different methods may be biased toward slightly different realms of sequence space, and that there are multiple solutions to achieving high-efficiency phosphorylation.

Table 1

Michaelis-Menten parameters for consensus peptides against c-Src and c-Abl kinase domains.

All measurements were carried out using the ADP-Quest assay in three to five replicates. Errors represent the standard error in global fits of all replicates to the Michaelis-Menten equation.

Entry	Kinase	Peptide name	Peptide sequence	k_cat (s^–1)	K_M (μM)
1	c-Src	Src Consensus	GPDECIYDMFPFKKKG	4.9±0.4	196±38
2	c-Src	Src Consensus (P-5C, D+1 G)	GCDECIYGMFPFKKKG	4.4±0.2	97±10
3	c-Src	SrcTide (1995)	GAEEEIYGEFEAKKKG	3.1±0.2	64±10
4	c-Src	SrcTide (2014)	GAEEEIYGIFGAKKKG	1.8±0.1	7±3
5	c-Src	Fer Consensus	GPDEPIYEWWWIKKKG	0.4±0.1	8±4
6	c-Src	Abl Consensus	GPDEPIYAVPPIKKKG	2.0±0.2	159±31
7	c-Abl	Abl Consensus	GPDEPIYAVPPIKKKG	3.0±0.2	6±2
8	c-Abl	AblTide (2014)	GAPEVIYATPGAKKKG	2.5±0.2	35±8

Next, we assayed all of the consensus peptides generated using our approach against their cognate kinases, as well as the other kinases in our screens. For the non-receptor tyrosine kinases (c-Src, c-Abl, and Fer), the corresponding consensus peptides were the best substrates tested. At a higher substrate concentration (100 μM), c-Abl also efficiently phosphorylated the Src and EPHB2 consensus peptides (Figure 2C), but selectivity for the Abl Consensus improved at a lower concentration (20 μM), consistent with selectivity being driven by K_M for this set of peptides (Figure 2—figure supplement 2 and Table 1). By contrast, c-Src was selective for the Src Consensus peptide at high concentrations (Figure 2C), but showed significant off-target activity toward the Fer Consensus at low concentrations (Figure 2—figure supplement 2). Michaelis-Menten analysis of the Fer Consensus with c-Src revealed that it has a remarkably tight K_M for c-Src, with a low k_cat as a trade-off (Table 1). Finally, we observed that the receptor tyrosine kinase EPHB1 showed very little selectivity across the consensus peptides and did not prefer its own cognate consensus sequence (Figure 2C and Figure 2—figure supplement 2). EPHB2, on the other hand, efficiently phosphorylated its own consensus peptide, as well as the Abl Consensus. Both of these sequences contain a –1 Ile and +3 Pro (Figure 2B and C). These experiments demonstrate the applicability of our bacterial peptide display method to the design of high-activity substrates. Our results also suggest that not all consensus peptides will be selective for their given kinase, as there can be overlap in substrate specificities.

Data from X₅-Y-X₅ library screens can be used to predict the relative phosphorylation rates of peptides

Given that data from the X₅-Y-X₅ library screens could yield high-efficiency substrates, we investigated whether the same data could be used to quantitatively predict the relative phosphorylation rates of biologically interesting sequences. If so, this would be a potentially powerful tool for the identification of native substrates and the dissection of phosphotyrosine signaling pathways. Indeed, oriented peptide libraries have been applied extensively to predict the native substrates of protein kinases (Johnson et al., 2023; Miller et al., 2008; Obenauer et al., 2003). We are particularly interested in using high-throughput specificity screens to predict how mutations proximal to phosphorylation sites affect tyrosine kinase selectivity. The PhosphoSitePlus database documents thousands of missense mutations within five residues of tyrosine phosphorylation sites, many of which are associated with human diseases or are human polymorphisms, but the functional consequences of most of these mutations are unexplored (Hornbeck et al., 2019; Hornbeck et al., 2015; Krassowski et al., 2018; Landrum et al., 2018).

We used the c-Src X₅-Y-X₅ screening data to predict the relative phosphorylation rates of six peptide pairs, corresponding to reference and variant sequences derived from human phosphorylation sites. Each peptide sequence was scored using an approach that is similar to that used for oriented peptide libraries in the Scansite database (Obenauer et al., 2003; Yaffe et al., 2001). For each peptide sequence, we summed the log₂-transformed enrichment values for the appropriate amino acid at each position in the peptide (the numerical values that make up the heatmaps in Figures 1B and 2A). This sum was divided by the number of variable positions (10 positions for all peptides in this study), then normalized to be on a scale from 0 (the worst possible sequence) to 1 (the best possible sequence). We compared the predicted scores to in vitro phosphorylation rates measured using a highly-sensitive assay based on reverse-phase high-performance liquid chromatography (RP-HPLC) (Figure 3 and Figure 3—figure supplement 1). We found that our predictions, which were derived from log-transformed enrichment scores, correlated moderately well with the log-transformed rates of phosphorylation by c-Src (Figure 3A). The predictions could differentiate high, medium, and low activity substrates but could not accurately rank peptides within these clusters. Focusing specifically on the effects of the mutations in this set of peptides, we found that the X₅-Y-X₅ screening data could accurately predict the directionality of the effects of five out of six mutations (Figure 3B).

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Predicting relative phosphorylation rates using data from X₅-Y-X₅ library screens.

(A) Correlation between measured phosphorylation rates and X₅-Y-X₅ predictions for 12 peptides with c-Src. All peptides were used at a concentration of 100 μM, and c-Src was used at a concentration of 500 nM. Error bars represent the standard deviation from at least three rate measurements and three separate scores with individual replicates of the X₅-Y-X₅ screen. (B) Correlation between the magnitude of mutational effects for 6 peptide pairs with mutational effects predicted from X₅-Y-X₅ library screens. Error bars represent the standard deviation of at least three rate measurements and three separate scores with individual replicates of the X₅-Y-X₅ screen.

One drawback to the aforementioned scoring approach, like all models based on position-specific scoring matrices, is that it cannot capture context-dependent amino acid preferences. We recently explored a machine-learning approach, using screening data from a related degenerate library, to model c-Src kinase specificity (Rube et al., 2022). The model not only incorporated pairwise inter-residue dependencies, but also data from multiple time points. This approach could reasonably predict absolute rate constants, as well as the directionality and magnitude of several phosphosite-proximal mutational effects. As an alternative to building models based on random library screens, we reasoned that direct measurements of reference and variant peptides using our screening platform might also provide reliable assessment of mutational effects.

A proteome-derived peptide library accurately measures sequence specificity and phosphorylation rates

To refine our assessment of phosphosite-proximal mutational effects, we designed a library, derived from the PhosphoSitePlus database, that is composed of 11-residue sequences spanning 3159 human phosphosites and 4760 disease-associated variants of these phosphosites bearing a single amino acid substitution (pTyr-Var library; Figure 4—figure supplement 1; Hornbeck et al., 2019). While the majority of sequences in this library contained a single tyrosine residue, some sequences contained multiple tyrosines, for which we included additional variants where the non-central tyrosine residues were mutated to phenylalanine. Including these tyrosine mutants and additional control sequences, such as previously designed consensus substrates, the library totaled ~10,000 unique sequences. As with the X₅-Y-X₅ library, we generated two versions of this library, bearing a C-terminal strep-tag or myc-tag. We conducted specificity screens with the myc-tagged pTyr-Var library against 7 non-receptor tyrosine kinases (c-Src, Fyn, Hck, c-Abl, Fer, Jak2, and AncSZ, an engineered homolog of Syk and ZAP-70 Hobbs et al., 2022) and 5 receptor tyrosine kinases (EPHB1, EPHB2, FGFR1, FGFR3, and MERTK). The majority of these kinases could be expressed in bacteria and purified in good yield (Albanese et al., 2018; Hobbs et al., 2022). One of these kinases (Jak2) was purchased from a commercial vendor.

Using the catalytically active tyrosine kinase constructs, we identified an optimal concentration (typically between 0.1–1.5 μM) to ensure 20–30% of maximal phosphorylation in three minutes. For some kinases (FGFR1, FGFR3, and MERTK), pre-incubation with ATP was required in order to activate the kinase by auto-phosphorylation (Figure 4—figure supplement 2). We conducted the screens analogously to those with the X₅-Y-X₅ library, but rather than calculate position-specific residue preferences from the deep sequencing data, we directly calculated enrichment scores for each peptide in the pTyr-Var library (Figure 4A and Figure 4—source data 1). Three to five replicates of the pTyr-Var screen were conducted with each kinase, and the results were reproducible across replicates (Figure 4—figure supplement 3). To validate our pTyr-Var screens, we examined enrichment scores from the c-Src experiments for the same six peptide pairs for which predictions using X₅-Y-X₅ screening data were only moderately accurate. We found a strong correlation between the pTyr-Var enrichment scores and phosphorylation rates, particularly for high-activity sequences (Figure 4B). Furthermore, the effects of mutations in the screens were consistent with those observed using the in vitro RP-HPLC assay with purified peptides (Figure 4C).

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Specificity profiling of tyrosine kinases using the pTyr-Var library.

(A) Distribution of enrichment scores from pTyr-Var screens with 13 tyrosine kinases. Each point represents a peptide sequence in the pTyr-Var library. Data points in orange-red represent sequences without a Tyr residue and data points in dark gray represent sequences with a Tyr residue. Each dataset represents the average of three to five replicates. (B) Correlation between enrichment scores and measured phosphorylation rates for 12 peptides (100 μM) with c-Src (500 nM). (C) Correlation between the magnitude of mutational effects for 6 peptide pairs in the pTyr-Var library with mutational effects measured using an *in vitro* kinetic assay. Error bars in panels B and C represent the standard deviation from 3 to 4 rate measurements and four pTyr-Var screens. (D) Matrix of Pearson’s correlation coefficients for all pairwise comparisons between replicate-averaged pTyr-Var datasets for 13 kinases. (E) Volcano plot depicting mutational effects in the pTyr-Var screen with c-Src kinase domain. Data points represent the average of four replicates. Hits are colored orange-red. (F) Percent phosphorylation of SHP2 wild-type, D61V, and D61N (10 μM) after an hour incubation with c-Src, Fyn, and FGFR1 (1 μM). Error bars represent the standard deviation from 2 to 3 measurements.

Figure 4—source data 1 Enrichment scores from tyrosine kinase pTyr-Var screens. Data are provided in a flat sheet with average and standard deviation values for all kinase-substrate pairs. Data are also provided for each kinase as a side-by-side comparison of enrichment scores reference and variant sequences and whether the mutation was considered a significant in our analysis. Three sheets are provided listing substrates for c-Src, Fyn, and c-Abl that are also found in a curated list of kinase-substrate pairs in the PhosphositePlus database.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig4-data1-v2.xlsx
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Figure 4—source data 2 Position-specific amino acid enrichment matrices from the tyrosine kinase pTyr-Var library screens for sequences containing a single central tyrosine residue.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig4-data2-v2.xlsx
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A total of 370 peptides in the pTyr-Var library contain no tyrosine residues and thus serve as controls to determine background noise in our screens. For every kinase tested, the tyrosine-free sequences showed distinctly low enrichment scores, consistent with signal in these screens being driven by tyrosine phosphorylation of the surface-displayed peptides (Figure 4A). For each kinase, a subset of the library (between 7% and 10%) showed enrichment scores above this background level (Figure 4—figure supplement 4). To confirm that the pTyr-Var screens were reporting on unique substrate specificities across these tyrosine kinases, we calculated Pearson’s correlation coefficients for the average datasets of each kinase pair and visualized position-specific amino acid preferences as heatmaps (Figure 4D, Figure 4—figure supplement 5, and Figure 4—source data 2). We found strong correlation in specificity between kinases of the same family (the Src-family kinases c-Src/Fyn/Hck, and receptor pairs EPHB1/EPHB2 and FGFR1/FGFR3). We also observed that the specificity of Src-family kinases partly overlapped with the Ephrin receptors and MERTK. The specificity of AncSZ and Jak2 correlated with that of FGFRs.

Next, we compared the results of our pTyr-Var library screens with a curated list of kinase-substrate pairs found in the PhosphositePlus database (Hornbeck et al., 2019). For c-Src, Fyn, and c-Abl, out of the sequences that overlapped between our library and the curated list, 30–40% of the kinase-substrate pairs showed efficient phosphorylation in the peptide-display screen (Figure 4—source data 1). This is consistent with a previous study using bacterial display and a different proteome-derived peptide library (Shah et al., 2018). The modest overlap between peptide screens and literature-reported kinase-substrate pairs is not surprising, given that other mechanisms in kinase-substrate recognition, such as localization, may override kinase domain sequence preferences (Miller and Turk, 2018). Furthermore, the curated list of kinase-substrate pairs comes from both in vitro and in vivo studies and may not accurately represent bona-fide substrates for each kinase.

Natural variants of tyrosine phosphorylation sites impact kinase recognition

For pairs of peptides in the pTyr-Var library that correspond to a disease-associated variant and a reference sequence, we calculated the log₂-fold change in enrichment for the variant relative to the reference. The large number of replicates for each screen afforded a robust analysis of phosphosite-proximal mutational effects for each kinase. We filtered the results in five steps to identify significant mutations: (1) We omitted phosphosite pairs where there was no statistically significant difference in enrichment between the variant and reference (p-value cutoff of 0.05). (2) We then applied a second filtering step to remove phosphosite pairs where the fold-change in enrichment between the variant and reference sequence was less than two. (3) Next, we excluded pairs where both sequences were low-activity substrates (enrichment score less than 1.5). (4) We removed mutations that added or removed a tyrosine residue, as their interpretation is ambiguous in our assay. (5) Lastly, we excluded phosphosite pairs in which the average read count of either the variant or wild-type sequence was less than 50. This left us with unique set of 50–400 high-confidence candidates for each tyrosine kinase (Figure 4E, Figure 4—figure supplement 6, and Figure 4—source data 1). From this filtered list, we found that kinases showed distinct patterns of mutational sensitivity at each position around the central tyrosine, consistent with their distinct sequence preferences (Figure 4—figure supplement 7).

For c-Src, we identified 381 high-confidence mutations (Figure 4E). A number of these mutations were on proteins involved in neurotrophin-regulated signaling, cyclin-dependent serine/threonine kinase activity, and other receptor/non-receptor tyrosine kinase activity. We found notable mutational effects at a known target of c-Src, Tyr 149 of the tumor suppressor protein FHL1 (Wang et al., 2018), as well as on other proteins known to interact with c-Src, such as the lipid and protein phosphatase PTEN and the immune receptor LILRB4 (Kang et al., 2016; Lu et al., 2003). We were particularly interested in cases where a kinase not previously known to phosphorylate a specific phosphosite showed a dramatic gain-of-function upon phosphosite-proximal mutation. For example, we found that the R982C mutation, proximal to Tyr 981 on the receptor tyrosine kinase RET, significantly enhanced phosphorylation by c-Src (Figure 4—figure supplement 8). This phosphosite is a known to engage the SH2 domain of c-Src and facilitate c-Src activation upon recruitment to RET, but it is not considered a kinase substrate of c-Src (Encinas et al., 2004). This mutation could potentially rewire signaling by promoting phosphorylation of RET by c-Src, and in doing so, sustaining c-Src activation by its binding to phospho-RET. The RET R982C mutation also enhanced Tyr 981 phosphorylation by several other kinases, most notably Fer (Figure 4—figure supplement 8). These examples show how the pTyr-Var data could be used as a resource to guide mutation-focused signaling studies.

To further validate our approach, we examined the effects of phosphosite-proximal mutations on the phosphorylation of an intact protein, rather than a peptide. Tyr 62 in the tyrosine phosphatase SHP2 sits within a region of this protein that is frequently mutated in various human diseases (Tartaglia et al., 2006), and this residue is highly phosphorylated in receptor tyrosine kinase-driven cancers (Gillette et al., 2020; Pfeiffer et al., 2022). Several Tyr 62-proximal mutations are encoded in the pTyr-Var library. In our screens, the reference peptide for Tyr 62 was preferentially phosphorylated by receptor tyrosine kinases, such as FGFR1, over non-receptors such as c-Src and Fyn, and nearby mutations showed varied effects on Tyr 62 phosphorylation, depending on the kinase tested (Figure 4—figure supplement 9). For example, D61V enhanced and D61N attenuated phosphorylation by Src-family kinases, but these mutations had little impact on recognition by FGFR1. To assess whether the effects of D61 mutations in the screens were retained in the context of the intact protein, we monitored phosphorylation of wild-type, D61V, and D61N SHP2 by c-Src, Fyn, and FGFR1 using intact protein mass spectrometry. We made two modifications to SHP2 to facilitate measurements: (1) substitution of the catalytic residue (C459E) to prevent dephosphorylation by the SHP2 phosphatase domain and (2) deletion of the disordered C-terminal tail to avoid background phosphorylation of an accessible site. Our measurements recapitulated the relative phosphorylation efficiencies for the Tyr 62 reference peptides, with Fyn being the slowest, and FGFR1 being the fastest (Figure 4F and Figure 4—figure supplement 9). Both D61V and D61N dramatically enhanced phosphorylation by all three kinases, consistent with reports that mutations at this site dramatically alter SHP2 structure and probably also increase Tyr 62 accessibility (Keilhack et al., 2005). For c-Src and Fyn, but not FGFR1, D61V showed a stronger enhancement of phosphorylation than D61N, consistent with our peptide screens (Figure 4F and Figure 4—figure supplement 9). The effects of these mutations in SHP2 on signal rewiring in cells warrants further investigation.

Position-specific amino acid preferences for tyrosine kinases are context-dependent

As noted earlier, position-specific scoring matrices do not reflect context-dependent sequence preferences. To illustrate this further, we scored peptide sequences in the pTyr-Var library using the position-specific scoring matrices generated from the X₅-Y-X₅ library. For peptides that showed significant enrichment in the pTyr-Var screens (enrichment >1), there was a modest correlation with the scores predicted using the X₅-Y-X₅ library, with many outliers (Figure 5A and Figure 5—figure supplement 1). We selected peptides for c-Src and c-Abl that were high-activity sequences based on the pTyr-Var screens (enrichment >4) but deviated significantly from canonical recognition motifs, and therefore were low scoring (score <0.5). The peptides selected for c-Src had unfavorable residues downstream of the central tyrosine (+1 Arg and +3 Gly for MISP_Y95;+1 Asn,+2 Arg, and +3 Glu for HLA-DPB1_Y59_F64L_YF). For c-Abl, the peptides had an unfavorable –1 Glu and +2 Ser (SIRPA_Y496_P491L) or an unfavorable +2 Glu and +3 Gly (HGD_Y166_F169L). We measured phosphorylation rates for these peptides using our RP-HPLC assay. Phosphorylation rates for these peptides deviated from what would be expected based on a position-specific scoring matrix (Figure 5B, Figure 5—figure supplement 1, and Figure 5—source data 1). This suggests that the putatively unfavorable sequence features in these peptides were tolerated in their specific sequence contexts.

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Context-dependent effects of tyrosine kinase recognition.

(A) Correlation of enrichment scores measured for c-Src in the pTyr-Var library screen with scores predicted from the X₅-Y-X₅ library using a position-specific scoring matrix. (B) Correlation between predicted scores and measured phosphorylation rates for 14 peptides (100 μM) with c-Src (500 nM). Peptides that could not be accurately scored by the X₅-Y-X₅ data are highlighted in orange. (C) Correlation of variant effects measured in the pTyr-Var library screen with those predicted from the X₅-Y-X₅ library screen for c-Src. Several points lie in the top-left and bottom-right quadrants, indicating a discrepancy between the measured mutational effect in the pTyr-Var screen and the predicted mutational effect from the X₅-Y-X₅ screen. (D) Effects of serine-to-proline substitution at the –2 position in various assays with c-Src. The left panels show the enrichment levels of –2 serine and proline in the X₅-Y-X₅ screen (top), and the effect of a –2 serine to proline substitution in a specific peptide in the pTyr-Var screen, (bottom). The right panels show rate measurements using the RP-HPLC assay for the same substitution in the Src consensus peptide (top) and the peptide from the pTyr-Var screen (bottom).

Figure 5—source data 1 Peptide sequences and their phosphorylation rates by c-Src or c-Abl, measured using the RP-HPLC kinetic assay.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig5-data1-v2.xlsx
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Figure 5—source data 2 Mutational effects measured from the pTyr-Var library screens and their corresponding predictions based on the X₅-Y-X₅ library screening data. Only those sequence pairs with high-quality sequencing data (read counts >100) and a single central tyrosine were included in the analysis.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig5-data2-v2.xlsx
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The observation that there are context-dependent sequence preferences for kinase-substrate interactions has important consequences for predicting the effects of phosphosite-proximal mutations. The same substitution could have different effects depending on the composition of the surrounding sequence. This phenomenon is uniquely visible in our screening approach, as we are measuring the phosphorylation of defined peptide sequences, and we are conducting screens with thousands of peptide pairs that vary by only a single amino acid substitution. To test our hypothesis, we assessed whether the directionality of mutational effects observed for specific peptides in the pTyr-Var screen could be predicted using the position-specific scoring matrix derived from the X₅-Y-X₅ screen (which would represent the effect of making a substitution averaged over all sequence contexts). While the directionality of the effect of most mutations could be predicted by the X₅-Y-X₅ screen, we observed many mutations that showed a significant effect where none was predicted, as well as mutations where the effect was the opposite of what was predicted (Figure 5C, Figure 5—figure supplement 2, and Figure 5—source data 2).

To validate this observation, we selected a peptide pair in the pTyr-Var library where a mutation (–2 Ser to Pro) had the opposite effect of that predicted by our X₅-Y-X₅ screen for c-Src (Figure 5D), as well as published results with oriented peptide libraries (Begley et al., 2015; Obenauer et al., 2003). Additionally, we made the same substitutions to the c-Src consensus peptide to determine whether the X₅-Y-X₅ predictions would hold true in that context. Measurements of these purified peptides by c-Src show that the same amino acid substitution had different impacts on c-Src recognition, depending on the sequence context (Figure 5D). A previous study that analyzed the specificity of the epidermal growth factor receptor (EGFR) kinase using bacterial peptide display showed that the effect of mutations at the –2 position was sometimes dependent on the identity of the –1 residue (Cantor et al., 2018). Molecular dynamics analyses in that report suggested that the amino acid identity at the –1 position determined how the side chain of the –2 residue was presented to the kinase, and vice versa, thereby dictating context-dependent preferences at both positions. Our pTyr-Var screens suggest that context dependent sequence preferences may be commonplace. Depending on the kinase, 5–15% of all significant mutations in the pTyr-Var screen had the opposite effect of that predicted using the X₅-Y-X₅ library data. Mapping these context-dependent effects comprehensively could have a significant impact on our ability to predict native substrates of kinases, and it will improve our understanding of the structural basis for substrate specificity.

Phosphorylation of bacterial peptide display libraries enables profiling of SH2 domains

In previous implementations of our bacterial peptide display and deep sequencing approach, the specificities of phosphotyrosine recognition domains (e.g. SH2 domains and phosphotyrosine binding (PTB) domains) were analyzed in addition to tyrosine kinase domains (Cantor et al., 2018; Lo et al., 2019). This approach required two amendments to the kinase screening protocol. First, the surface-displayed libraries were phosphorylated to saturating levels using a cocktail of tyrosine kinases. Second, because phosphotyrosine recognition domains generally have fast dissociation rates from their ligands (Morimatsu et al., 2007; Oh et al., 2012), making binding-based selection assays challenging, constructs were generated in which two identical copies of an SH2 domain were artificially fused together. The tandem-SH2 constructs enhanced avidity for phosphopeptides displayed on the cell surface through multivalent effects, thereby enabling enrichment of cells via FACS (Cantor et al., 2018).

For this study, we reasoned that a multivalent SH2 construct could be mimicked by functionalizing avidin-coated magnetic beads with biotinylated SH2 domains. These SH2-coated beads could then be used to select E. coli cells displaying enzymatically phosphorylated peptide display libraries, followed by deep sequencing to determine SH2 sequence preferences (Figure 6A). Thus, we first established a protocol to produce site-specifically biotinylated SH2 domains in E. coli, by co-expressing an Avi-tagged SH2 construct with the biotin ligase BirA (Gräslund et al., 2017). This system yielded quantitatively biotinylated SH2 domains, as confirmed by mass spectrometry (Figure 6—figure supplement 1). Since the biotinylated SH2 domains could be produced in high yields through bacterial expression, the recognition domains were immobilized on the magnetic beads at saturating concentrations to ensure a uniform concentration across experiments. This also prevented background binding of strep-tagged libraries to the beads, making this method compatible with previously reported strep-tagged libraries (Cantor et al., 2018; Shah et al., 2018; Shah et al., 2016).

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High-throughput profiling of SH2 domain ligand specificity using bacterial peptide display.

(A) Schematic representation of the workflow for SH2 domain specificity profiling. (B) Heatmaps depicting the specificities of the c-Src, SHP2-C, and Grb2 SH2 domains, measured using the X₅-Y-X₅ library. Enrichment scores were log₂-transformed and are displayed on a color scale from blue (disfavored), to white (neutral), to red (favored). Values in the heatmaps are the average of three replicates. (C) Distribution of enrichment scores from pTyr-Var screens with three SH2 domains and the pan-phosphotyrosine antibody 4G10 Platinum. Each point represents a peptide sequence in the library. The antibody selection was done similar to the kinase screens, with antibody labeling of cells, followed by bead-based enrichment, as opposed to cell enrichment with antibody-saturated beads. Each dataset represents the average of three replicates. (D) Correlation between enrichment scores for 9 peptides from the pTyr-Var screen and binding affinities measured using a fluorescence polarization assay. Error bars represent the standard deviations from three screens or binding measurements. (E) Examples of phosphosite-proximal mutations that selectively enhance binding to specific SH2 domains. Error bars represent the standard deviations from three screens.

Figure 6—source data 1 Position-specific amino acid enrichment matrices from the SH2 domain X₅-Y-X₅ library screens. Matrices calculated with and without inclusion of multi-tyrosine sequences are provided.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig6-data1-v2.xlsx
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Figure 6—source data 2 Enrichment scores from SH2 domain pTyr-Var screens. Data are provided in a flat sheet with average and standard deviation values for all SH2-ligand pairs. Data are also provided for each SH2 domain as a side-by-side comparison of enrichment scores reference and variant sequences and whether the mutation was considered a significant in our analysis.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig6-data2-v2.xlsx
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Figure 6—source data 3 Position-specific amino acid enrichment matrices from the SH2 domain pTyr-Var library screens for sequences containing a single central tyrosine residue.: https://cdn.elifesciences.org/articles/82345/elife-82345-fig6-data3-v2.xlsx
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To implement SH2 specificity screens, the strep-tagged X₅-Y-X₅ library was phosphorylated to a high level using a mixture of c-Src, c-Abl, AncSZ, and EPHB1 (Figure 6—figure supplement 2). The phosphorylated library was screened against three SH2 domains that fall into distinct specificity classes and are derived from three different types of signaling proteins: the SH2 domain from the tyrosine kinase c-Src, the C-terminal SH2 (C-SH2) domain from the tyrosine phosphatase SHP2, and the SH2 domain from the non-catalytic adaptor protein Grb2 (Figure 6B, Figure 6—figure supplement 3, and Figure 6—source data 1). The X₅-Y-X₅ library screens recapitulated known sequences preferences for each SH2 domain. For c-Src, there was a distinctive preference for –2 His,+1 Asp/Glu, and +3 Ile, as previously reported from oriented peptide libraries (Huang et al., 2008). For Grb2, a characteristic +2 Asn preference dominated the specificity profile (Gram et al., 1997; Huang et al., 2008; Kessels et al., 2002; Rahuel et al., 1996; Songyang et al., 1994). Notably, our Grb2 screen also reveals subtle amino acid preferences at other positions, which could tune the affinity for +2 Asn-containing sequences. Several studies have measured the sequence specificity of the SHP2 C-SH2 domain using diverse methods, including peptide microarrays, oriented peptide libraries, and one-bead-on-peptide libraries (Huang et al., 2008; Miller et al., 2008; Sweeney et al., 2005; Tinti et al., 2013). The results of these reported screens are not concordant. Our method indicates a preference for β-branched amino acids (Thr/Val/Ile) at the –2 position, a small residue (Ala/Ser/Thr) at the +1 position, and strong preference for an aliphatic residue (Ile/Val/Leu) at the +3 position. Our results are most in-line with the one-bead-one-peptide screens (Sweeney et al., 2005).

We next phosphorylated and screened the pTyr-Var library against the same three SH2 domains in triplicate (Figure 6C, Figure 6—source data 2, and Figure 6—source data 3). The replicates for each SH2 domain were highly correlated, but datasets between SH2 domains had poor correlation, suggesting distinct ligand specificities (Figure 6—figure supplement 4). As observed for kinases, we saw negligible enrichment of peptides lacking a tyrosine residue, but each SH2 domain showed strong enrichment of a few hundred peptides containing one or more tyrosines (Figure 6C). With the phosphorylated pTyr-Var library, we also carried out selection with a biotinylated pan-phosphotyrosine antibody to assess the level of bias in phosphorylation across the library. Compared to selection with SH2 domains, selection with the antibody yielded a narrower distribution of enrichment scores, with very few highly enriched sequences, suggesting relatively uniform phosphorylation (Figure 6C). We further validated the SH2 screening method by measuring the binding affinities of 9 peptides from the pTyr-Var library with the c-Src SH2 domain, using a fluorescence polarization binding assay. Enrichment scores from the pTyr-Var screen showed a good linear correlation with measured K_d values over two orders of magnitude (Figure 6D).

The pTyr-Var library screens with the SH2 domains were analyzed and filtered similarly to those with kinase domains. For each SH2 domain, we identified 50–300 phosphosite-proximal mutations that significantly and reproducibly enhanced or attenuated binding (Figure 6—figure supplement 5 and Figure 6—source data 2). As expected, given their distinct specificities, the c-Src, SHP2-C, and Grb2 SH2 domains showed unique sensitivities to mutations (Figure 6—figure supplement 6). We identified several phosphosite-proximal mutations that were selectively gain-of-function for one or two SH2 domains (Figure 6E and Figure 6—source data 2). These mutations could drive the rewiring of signaling pathways by changing which downstream effector engages a phosphosite. This phenomenon was recently reported for lung-cancer associated mutations near phosphorylation sites in EGFR, which impacted the recruitment of Grb2 and SHP2 to the receptor and altered downstream signaling (Lundby et al., 2019).

Finally, we note that our pTyr-Var datasets included screens with both the kinase and SH2 domains of c-Src. When the SH2 domain of c-Src interacts with phosphoproteins, it both localizes the kinase domain in proximity to its substrates and activates the enzyme (Liu et al., 1993). Our screens revealed that the phosphorylation profiles of c-Src kinase and SH2 domains against the pTyr-Var were completely orthogonal (Figure 6—figure supplement 7). Their starkly different activities toward the pTyr-Var library can largely be attributed to kinase domain preference for a+3 Phe and SH2 domain preference for a+3 Ile/Val/Leu/Met. This is in contrast to previous observations for c-Abl, which has kinase and SH2 domains with largely overlapping sequence specificities, dominated by a+3 Pro preference (Songyang et al., 1995). For c-Src, phosphosite mutations that impacted recognition by one domain generally had no effect on the other, because preferred sequence features for one domain were typically tolerated (neutral) for the other (Figure 6—figure supplement 8). A consequence of this is that phosphosite-proximal mutations may alter c-Src function in two mechanistically distinct ways: (1) mutations that enhance SH2 binding can alter the localization and local activation of c-Src or (2) mutations that enhance kinase recognition will directly increase phosphorylation rates by c-Src. These insights highlight value in profiling multiple domains of the same signaling protein against the same peptide library.

Amber codon suppression yields an expanded repertoire of peptides for specificity profiling

The specificity profiling screens described thus far were constrained to sequences that contain the canonical twenty amino acids. Several studies have suggested that non-canonical amino acids and post-translationally modified amino acids can also impact sequence recognition by kinases and SH2 domains (Alfaro-Lopez et al., 1998; Begley et al., 2015; Chapelat et al., 2012; Johnson et al., 2023; Yeh et al., 2001). The most notable example of this is phospho-priming, whereby phosphorylation of one residue on a protein enhances the ability of a kinase to recognize and phosphorylate a proximal residue. This phenomenon was recently described for EGFR, which preferentially phosphorylates sequences containing a tyrosine followed by a+1 phosphotyrosine (Begley et al., 2015). Other prevalent post-translational modifications, such as lysine acetylation, may also impact the ability of kinases or SH2 domains to recognize a particular phosphosite (Parker et al., 2014; Rust and Thompson, 2011).

We sought to expand our specificity profiling method to incorporate non-canonical and post-translationally modified amino acids (Figure 7A). Since our libraries are genetically encoded, we employed Amber codon suppression and repurposing, using engineered tRNA molecules and aminoacyl tRNA synthetases (Amiram et al., 2015; Xie et al., 2007; Zheng et al., 2018). The degenerate (X) positions in our X₅-Y-X₅ library are encoded using an NNS codon, which means that an Amber codon (TAG) is sampled at each position 3% of the time. Thus, this library theoretically contains a sufficiently large number of diverse sequences to profile specificity with a 21 amino acid alphabet. For Amber suppression in E. coli, tRNA/synthetase pairs are commonly expressed from pEVOL or pULTRA plasmids (chloramphenicol and streptomycin resistant, respectively) (Chatterjee et al., 2013). Both of these systems are incompatible with our surface-display platform, which uses MC1061 cells (streptomycin resistance encoded in the genome) and libraries in a pBAD33 vector (chloramphenicol resistant). Thus, we designed a variant of the pULTRA plasmid in which we swapped the streptomycin resistance gene for an ampicillin resistance gene from a common pET vector for protein expression (pULTRA-Amp).

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Expansion of peptide display libraries using Amber suppression.

(A) Non-canonical amino acids used in this study. CMF = 4-carboxymethyl phenylalanine, AzF = 4-azido phenylalanine, and AcK = N-ε-acetyl-L-lysine. (B) Amber suppression in the strep-tagged X₅-Y-X₅ library using CMF. Library surface-display level was monitored by flow cytometry using a fluorophore-labeled StrepMAB antibody for samples with or without Amber suppression components. (C) AzF labeling on bacterial cells using a DIBO-conjugated fluorophore. Cells expressing the X₅-Y-X₅ library, with and without various Amber suppression components, were treated with DIBO-conjugated Alexa Fluor 555 then analyzed by flow cytometry. (D) Heatmaps depicting the specificities of c-Src, Hck, and c-Abl after CMF or acetyl lysine incorporation. Only sequences with one stop codon were used in this analysis. Enrichment scores were log₂-transformed and are displayed on a color scale from blue (disfavored), to white (neutral), to red (favored). Values in heatmaps are the average of three replicates.

To confirm that non-canonical amino acids could be incorporated into the X₅-Y-X₅ library, we co-transformed E. coli with the library and a pULTRA-Amp plasmid encoding a tRNA/synthetase pair that can incorporate 4-carboxymethyl phenylalanine (CMF) via Amber suppression (Figure 7A; Xie et al., 2007). We measured peptide display levels by flow cytometry for cultures that were grown with or without CMF in the media. For the cultures grown without CMF, roughly 20% of the cells had no surface-displayed peptides, consistent with termination of translation at Amber codons within the peptide-coding region (Figure 7B). In the presence of CMF, this premature termination was significantly suppressed, and a larger fraction of the cells displayed peptides. As an additional test, we incorporated 4-azido phenylalanine (AzF) into the X₅-Y-X₅ library (Figure 7A; Amiram et al., 2015). Cells expressing this expanded library were treated with a dibenzocyclooctyne (DIBO)-functionalized fluorophore, which should selectively react with the azide on AzF via strain-promoted azide-alkyne cycloaddition (Ning et al., 2008). Only cells expressing the synthetase and grown in the presence of AzF showed significant DIBO labeling, confirming Amber suppression and non-canonical amino acid incorporation into our library (Figure 7C).

Using this library expansion strategy, we assessed how substrate recognition by c-Src is impacted by neighboring CMF or acetyl-lysine residues. We subjected CMF- or AcK-containing X₅-Y-X₅ libraries to c-Src phosphorylation, selection, and sequencing, using the same methods described above. When analyzing X₅-Y-X₅ libraries in standard kinase and SH2 screens, we typically omit all Amber-containing sequences from our calculations, as they do not encode expressed peptides (Figure 1B and Figure 2A). For these experiments, we included Amber-containing sequences in our analysis. Using this strategy, we found that the Amber codon was less depleted at each position surrounding the central tyrosine than we observed for libraries without Amber suppression, but the log-transformed enrichment scores for Amber codons at all positions surrounding the tyrosine residue were still negative (Figure 7—figure supplement 1). We reasoned that, if Amber suppression efficiency was not 100%, any Amber-containing sequence would still be depleted relative to a sequencing lacking a stop codon, due to some premature termination. Thus, we re-analyzed the data by exclusively counting sequences that contained one Amber codon, under the assumption that every sequence would have approximately the same amount of premature termination. This revealed positive enrichment for CMF and AcK at select positions (Figure 7D and Figure 7—figure supplement 1). Although we only included a fraction of the total library in our new analysis, the overall specificity profile was almost identical to that observed when including the whole library, indicating that this sub-sampling approach was valid (Figure 7—figure supplement 2).

Next, we compared the preferences for CMF and AcK at each position to their closest canonical amino acids, phenylalanine (Phe) and lysine (Lys). CMF was enriched at the –3 and –2 positions, where Phe is not tolerated by c-Src (Figure 7D). Negatively-charged amino acids (Asp and Glu) are also preferred at these positions, and the negative charge on the carboxymethyl group of CMF at neutral pH may be able to mimic this recognition. c-Src has a strong selective preference for Phe at the +3 position, which it engages via a well-formed hydrophobic pocket near the active site (Bose et al., 2006; Shah et al., 2018). The charged carboxymethyl group on CMF is likely to be incompatible with this mode of binding, consistent with depletion of CMF at this site (Figure 7D). The difference between Lys and AcK was even more striking. Lys is unfavorable for c-Src at every position around the phospho-acceptor tyrosine. By contrast, AcK was not only tolerated, but even favorable at a few positions (Figure 7D).

To determine whether the position-specific responsiveness to lysine acetylation was kinase-dependent, we also performed additional screens of the AcK-containing X₅-Y-X₅ library with Hck and c-Abl. These screens showed that all three kinases had very similar position-dependent tolerance for AcK over Lys, with the closely-related c-Src and Hck being more similar to one another than their distant relative c-Abl (Figure 7D). Finally, we assessed how the effect of lysine acetylation translated to actual changes in phosphorylation rates. We produced variants of the c-Src and c-Abl consensus peptides with Lys or AcK at various positions and measured their rates of phosphorylation by their respective cognate kinases (Figure 7—figure supplement 3). Of the positions tested (−2,+1, and +5 relative to the tyrosine), we saw the largest effect at the +1 position, consistent with the screens. At the +1 position, where Lys is not tolerated, acetylation enhanced activity as much as five-to-ten-fold, depending on the peptide concentration. In the long-term, we envision using this approach to predict sites in the proteome where lysine acetylation creates new, high-activity substrates for tyrosine kinases. Furthermore, the same analysis could be applied to other tyrosine kinases and to SH2 domains, and our strategy could be readily expanded to other post-translational modifications that can be encoded using Amber suppression.

Concluding remarks

In this report, we describe a significant expansion to a previously developed method for profiling the sequence specificities of tyrosine kinases and SH2 (phosphotyrosine recognition) domains (Cantor et al., 2018; Shah et al., 2018; Shah et al., 2016). Our method relies on bacterial display of DNA-encoded peptide libraries and deep sequencing, and it enables the simultaneous analysis of multiple phosphotyrosine signaling proteins against thousands-to-millions of peptides or phosphopeptides. The resulting data can be used to design high-activity consensus sequences, predict the activities of uncharacterized sequences, and accurately measure the effects of amino acid substitutions on sequence recognition. A notable feature of our platform is that it relies on deep sequencing as a readout, yielding quantitative results. Furthermore, the data generated from our screens show a strong correlation with phosphorylation rates and binding affinities measured using orthogonal biochemical assays.

We envision a number of exciting applications of this expanded specificity profiling platform. Several recent reports have aimed to explain the molecular basis for tyrosine kinase and SH2 sequence specificity and affinity, by combining protein sequence and structure analysis with specificity profiling data (Bradley et al., 2021; Creixell et al., 2015a; Kaneko et al., 2010; Liu et al., 2019). The rich datasets generated using our platform will augment these approaches, particularly when coupled with screening data for additional proteins. A long-term goal of these efforts will undoubtedly be to accurately predict the sequence specificity and signaling properties of any uncharacterized phosphotyrosine signaling protein, such as a disease-associated kinase variant (Creixell et al., 2015b). Given the nature of the data generated by our platform, we expect that it will also aid the development and implementation of machine learning models for sequence specificity and design (Creixell et al., 2015a; Cunningham et al., 2020; Kundu et al., 2013). Indeed, our initial efforts in this realm suggest that specificity profiling data using the X₅-Y-X₅ library, without any protein structural information, may be sufficient to build models of sequence specificity that can accurately predict phosphorylation rates (Rube et al., 2022).

The pTyr-Var Library described in this report provides a unique opportunity to investigate variant effects across the human proteome. The vast majority of mutations near tyrosine phosphorylation sites are functionally uncharacterized (Hornbeck et al., 2019; Krassowski et al., 2018). Our screens are yielding some of the first mechanistic biochemical hypotheses about how many of these mutations could impact cell signaling. For example, these datasets will allow us to identify mutations that tune signaling pathways by altering the phosphorylation efficiency of specific phosphosites or the binding of SH2-containing effector proteins to those sites. Alternatively, these screens may help identify instances of network rewiring, in which a phosphosite-proximal mutation alters the canonical topology of a pathway by changing which kinases phosphorylate a phosphosite or which SH2-containing proteins get recruited to that site. The biological effects of signal tuning and rewiring caused by phosphosite-proximal mutations remain largely unexplored.

Our high-throughput platform to profile tyrosine kinase and SH2 sequence recognition is accessible and easy to use in labs that are equipped to culture E. coli and execute common molecular biology and biochemistry techniques. Screens can be conducted on the benchtop with proteins produced in-house or obtained from commercial vendors. Peptide libraries of virtually any composition, tailored to address specific biochemical questions, can be produced using commercially available oligonucleotides and standard molecular cloning techniques. Furthermore, facile chemical changes to the library (e.g. enzymatic phosphorylation or the introduction of non-canonical amino acids via Amber suppression) afford access to new biochemical questions. For example, the tyrosine-phosphorylated libraries described here will also be useful for the characterization of tyrosine phosphatase specificity, and acetyl-lysine-containing libraries could be used to profile lysine deacetylases and bromodomains. Additional amendments to this platform will enable the analysis of serine/threonine kinases and other protein modification or recognition domains, adding to the growing arsenal of robust methods for the high-throughput biochemical characterization of cell signaling proteins.

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Strain, strain background (E. coli)	MC1061	Lucigen	Lucigen: 10361012	bacterial cells used for surface-display screens
Strain, strain background (E. coli)	DH5α	Invitrogen	Invitrogen: 18265017	bacterial cells used for general cloning and library cloning
Strain, strain background (E. coli)	BL21(DE3)	ThermoFisher Scientific	Thermo: C600003	bacterial cells for general protein-expression; pre-transformed with pCDF-YopH for tyrosine kinase overexpression
Strain, strain background (E. coli)	C43(DE3)	Lucigen	Lucigen: NC9581214	bacterial cells used for SH2 domain over-expression; pre-transformed with pCDFDuet-BirA-WT for biotinylation
Antibody	4 G10 Platinum, Biotin (mouse monoclonal)	Millipore Sigma	Millipore Sigma: 16–452-MI	biotin conjugated mouse monoclonal pan-phosphotyrosine antibody dilution: (1:1000)
Antibody	PY20-PerCP-eFluor 710 (mouse monoclonal)	eBioscience	eBioscience: 46-5001-42	PerCP-eFluor 710-conjugated mouse monoclonal pan-phosphotyrosine antibody, clone PY20 dilution: (1:25)
Antibody	PY20-biotin (mouse monoclonal)	Exalpha	Exalpha: 50-210-1865	biotin conjugated mouse monoclonal pan-phosphotyrosine antibody dilution (1:500)
Antibody	StrepMAB Chromeo 488 (mouse monoclonal)	IBA LifeSciences	IBA: 2-1546-050	Chromeo 488-conjugated antibody that recognizes the strep-tag dilution: (1:50–100). Discontinued, but can be replaced with IBA LifeSciences StrepMAB-Classic conjugate DY-488 (IBA: 2-1563-050)
Recombinant DNA reagent	pBAD33-eCPX	PMID:18480093	Addgene: 23336	pBAD33 plasmid encoding the eCPX bacterial display gene with flanking 5' and 3' SfiI restriction sites
Recombinant DNA reagent	pBAD33-eCPX-cStrep	PMID:29547119		pBAD33 plasmid encoding the eCPX bacterial display gene with a 3' sequence encoding a strep-tag and flanking 5' and 3' SfiI restriction sites
Recombinant DNA reagent	pBAD33-eCPX-cMyc	this paper		pBAD33 plasmid encoding the eCPX bacterial display gene with a 3' sequence encoding a myc-tag and flanking 5' and 3' SfiI restriction sites
Recombinant DNA reagent	X5-Y-X5 Library (myc-tagged)	this paper		peptide display library in the pBAD33 vector, fused to the eCPX scaffold, containing 1–10 million unique sequences with the structure X5-Y-X5, where X is encoded by an NNS codon. The scaffold protein is encoded to have a C-terminal myc-tag: EQKLISEEDL.
Recombinant DNA reagent	X5-Y-X5 Library (strep-tagged)	this paper		peptide display library in the pBAD33 vector, fused to the eCPX scaffold, containing 1–10 million unique sequences with the structure X5-Y-X5, where X is encoded by an NNS codon. The scaffold protein is encoded to have a C-terminal strep-tag: WSHPQFEK.
Recombinant DNA reagent	pTyr-Var Library (myc-tagged)	this paper		peptide display library in the pBAD33 vector, fused to the eCPX scaffold, containing ~10,000 unique sequences encoding reference and variant phosphosite pairs deried from the PhosphoSitePlus database. The scaffold protein is encoded to have a C-terminal myc-tag: EQKLISEEDL.
Recombinant DNA reagent	pTyr-Var Library (strep-tagged)	this paper		peptide display library in the pBAD33 vector, fused to the eCPX scaffold, containing ~10,000 unique sequences encoding reference and variant phosphosite pairs deried from the PhosphoSitePlus database. The scaffold protein is encoded to have a C-terminal strep-tag: WSHPQFEK.
Recombinant DNA reagent	pET-23a-His6-TEV-Src(KD)	PMID:29547119		bacterial expression vector encoding the human c-Src kinase domain (residues 260–528), with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-23a-His6-TEV-Fyn(KD)	PMID:29547119		bacterial expression vector encoding the human Fyn kinase domain (residues 261–529) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-23a-His6-TEV-Hck(KD)	PMID:29547119		bacterial expression vector encoding the human Hck kinase domain (residues 252–520) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-23a-His6-TEV-Abl(KD)	PMID:29547119		bacterial expression vector encoding the mouse c-Abl kinase domain (residues 232–502) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-23a-His6-TEV-AncSZ(KD)	DOI: 10.1101/2022.04.24.489292		bacterial expression vector encoding the AncSZ kinase domain (residues 352–627) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET23a-His6-TEV-Fer(KD)	this paper		bacterial expression vector encoding the mouse Fer kinase domain (residues 553–823) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-His6-TEV-FGFR1(KD)	PMID:30004690	Addgene: 79719	bacterial expression vector encoding the human FGFR1 kinase domain (residues 456–763) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-His6-TEV-FGFR3(KD)	PMID:30004690	Addgene: 79731	bacterial expression vector encoding the human FGFR3 kinase domain (residues 449–759) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-His6-TEV-EPHB1(KD)	PMID:30004690	Addgene: 79694	bacterial expression vector encoding the human EPHB1 kinase domain (residues 602–896) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-His6-TEV-EPHB2(KD)	PMID:30004690	Addgene: 79697	bacterial expression vector encoding the human EPHB2 kinase domain (residues 604–898) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pET-His6-TEV-MERTK(KD)	PMID:30004690	Addgene: 79705	bacterial expression vector encoding the human MERTK kinase domain (residues 570–864) with an N-terminal His6-tag and TEV protease recognition sequence
Recombinant DNA reagent	pCDF-YopH	PMID:16260764		bacterial expression vector for co-expression of untagged YopH phosphatase with tyrosine kinases
Recombinant DNA reagent	pET28-His6-TEV-SHP2-C459E-no tail	this paper		bacterial expression vector encoding the human SHP2 (residues 1–526) with the C459E mutation, an N-terminal His6-tag, and TEV protease recognition sequence
Recombinant DNA reagent	pET28-His6-TEV-SHP2-C459E-no tail-D61V	this paper		bacterial expression vector encoding the human SHP2 (residues 1–526) with C459E and D61V mutations, an N-terminal His6-tag, and TEV protease recognition sequence
Recombinant DNA reagent	pET28-His6-TEV-SHP2-C459E-no tail-D61N	this paper		bacterial expression vector encoding the human SHP2 (residues 1–526) with C459E and D61N mutations, an N-terminal His6-tag, and TEV protease recognition sequence
Recombinant DNA reagent	pCDFDuet-BirA-WT	this paper		bacterial expression vector encoding BirA biotin ligase, used to coexpress with SH2 domain expression vector for biotinylation of SH2 domain
Recombinant DNA reagent	pET-His6-SUMO-Src(SH2)	this paper		bacterial expression vector encoding the human cSrc SH2 domain (residues 143–250) with an N-terminal His6-SUMO tag
Recombinant DNA reagent	pET-His6-SUMO-SHP2(CSH2)	this paper		bacterial expression vector encoding the human SHP2 CSH2 domain (residues 105–220) with an N-terminal His6-SUMO tag
Recombinant DNA reagent	pET-His6-SUMO-Grb2(SH2)	this paper		bacterial expression vector encoding the human Grb2 SH2 domain (residues 56–152) with an N-terminal His6-SUMO tag
Recombinant DNA reagent	pULTRA CMF	PMID:28604693		bacterial expression vector encoding the tRNA/syntetase pair for incorporation of 4-carboxymethyl phenylalanine via Amber suppression
Recombinant DNA reagent	pEVOL pAzFRS.2.t1	PMID:26571098	Addgene: 73546	bacterial expression vector encoding the tRNA/syntetase pair for incorporation of 4-azido phenylalanine and other Phe derivatives via Amber suppression
Recombinant DNA reagent	pULTRA chAcKRS3	PMID:29544052		bacterial expression vector encoding the tRNA/syntetase pair for incorporation of acetyl-lysine via Amber suppression; gift from Abhishek Chatterjee at Boston College
Recombinant DNA reagent	pULTRA-Amp CMF	this paper		bacterial expression vector encoding the tRNA/syntetase pair for incorporation of 4-carboxymethyl phenylalanine via Amber suppression, altered to have an ampicillin resistance marker
Recombinant DNA reagent	pULTRA-Amp pAzFRS.2.t1	this paper		bacterial expression vector encoding the tRNA/syntetase pair for incorporation of 4-azido phenylalanine and other Phe derivatives via Amber suppression, altered to have an ampicillin resistance marker
Recombinant DNA reagent	pULTRA-Amp chAcKRS3	this paper		bacterial expression vector encoding the tRNA/syntetase pair for incorporation of acetyl-lysine via Amber suppression, altered to have an ampicillin resistance marker

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High-throughput profiling of tyrosine kinase substrate specificity using bacterial peptide display.

Specificity profiling of tyrosine kinases using the X5-Y-X5 library.

Figure 2—source data 1

Michaelis-Menten parameters for consensus peptides against c-Src and c-Abl kinase domains.

Predicting relative phosphorylation rates using data from X5-Y-X5 library screens.

Specificity profiling of tyrosine kinases using the pTyr-Var library.

Figure 4—source data 1

Figure 4—source data 2

Context-dependent effects of tyrosine kinase recognition.

Figure 5—source data 1

Figure 5—source data 2

High-throughput profiling of SH2 domain ligand specificity using bacterial peptide display.

Figure 6—source data 1

Figure 6—source data 2

Figure 6—source data 3

Expansion of peptide display libraries using Amber suppression.

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Allyson Li

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Rashmi Voleti

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Minhee Lee

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Dejan Gagoski

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Neel H Shah

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For correspondence

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Specificity profiling of tyrosine kinases using the X₅-Y-X₅ library.

Predicting relative phosphorylation rates using data from X₅-Y-X₅ library screens.