Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads

In vivo, rapid regulation of weak, transient protein-protein interactions is essential for dynamically shaping cellular responses. Nearly 40% of these interactions are mediated by 3–10 amino acid Short Linear Motifs (SLiMs) interacting with protein globular domains (e.g. SH3, SH2, and PDZ domains) or enzymes (e.g. kinases and phosphatases) (Dinkel et al., 2016; Neduva and Russell, 2006; Tompa et al., 2014). The human proteome is estimated to contain more than 100,000 of these SLiMs, many of which are highly regulated by post-translational modifications (PTMs) such as phosphorylation (Tompa et al., 2014; Ivarsson and Jemth, 2019). As the weak affinities of these interactions (${\displaystyle K_d}$ values of ∼1 to 500 μM) are often close to the physiological concentrations of the interacting partners in vivo, subtle differences in concentrations or interaction affinities can have large effects on the fraction bound and downstream signaling output (Roy et al., 2007; Hein et al., 2017). Measuring and predicting not only which SLiMs a protein binds but also the affinities of SLiM-binding interactions is therefore essential for predicting signal strengths within signaling networks, understanding how these networks are perturbed by human disease, and identifying new therapeutic inhibitors (Uyar et al., 2014).

Calcineurin (CN), a conserved Ca²⁺/calmodulin-dependent phosphatase, is a prime example of a signaling protein that relies on weak SLiM-mediated interactions for substrate recognition. Although CN plays critical roles in the human immune, nervous, and cardiovascular systems and likely dephosphorylates hundreds of downstream targets, only ∼70 are known to date (Sheftic et al., 2016). These include the NFAT family of transcription factors, whose dephosphorylation by CN is required for T-cell activation and adaptive immunity (Jain et al., 1993). Consequently, CN is the target of the widely used immunosuppressants cyclosporin A (CysA) and FK506. CN dephosphorylates sites with little sequence similarity, instead recognizing substrates by binding to two characterized SLiMs (PxIxIT and LxVP) located at variable distances from the phosphosite (Roy and Cyert, 2009). Blocking SLiM binding to CN prevents dephosphorylation without altering its catalytic center: FK506 and CysA prevent LxVP docking, the viral inhibitor A238L blocks PxIxIT and LxVP binding, and the high-affinity peptide inhibitor PVIVIT blocks PxIxIT binding (Grigoriu et al., 2013; Aramburu et al., 1999). The affinities of PxIxIT motifs determine biological output by specifying the Ca²⁺ concentration-dependence of substrate dephosphorylation in vivo (Müller et al., 2009; Roy et al., 2007). However, the relationship between PxIxIT sequence and CN binding affinity has never been probed outside of the core motif. A comprehensive understanding of PxIxIT-CN binding would allow discovery of novel CN substrates and aid efforts to rationally design CN inhibitors with enhanced selectivity.

Unfortunately, the limited binding interfaces associated with SLiM-mediated interactions result in low to moderate affinities (with typical ${\displaystyle K_d}$ values in the range of 1–500 uM), high dissociation rates (Zhou, 2012; Dogan et al., 2015), and a rapid equilibrium (Gianni and Jemth, 2017; Bagshaw, 2017), complicating experimental efforts to measure affinities. Display-based methods such as combinatorial phage display (Tonikian et al., 2008) and ProP-PD (Ivarsson et al., 2014; Sundell and Ivarsson, 2014) allow screening for binding between a protein of interest and large libraries (up to 10¹⁰) of candidate SLiM-containing peptides. However, these methods typically identify only strong binders, cannot return negative information about residues that ablate binding critical for downstream target prediction in vivo (Gfeller et al., 2011), and do not directly probe effects of PTMs. Array-based methods (Fodor et al., 1991) allow quantification of binding of labeled proteins to 10s-100s (SPOT arrays) (Frank et al., 1990), tens of thousands (Atwater et al., 2018), or millions (ultra-high density arrays) (Buus et al., 2012; Forsström et al., 2014; Price et al., 2012; Carmona et al., 2015) of peptides chemically synthesized in situ, permitting direct incorporation of PTMs or unnatural amino acids at specific positions (Engelmann et al., 2014; Tinti et al., 2013; Filippakopoulos et al., 2012). However, it is difficult to evaluate the yield and purity of peptides in each spot, contributing to false positives and negatives (Tinti et al., 2013; Blikstad and Ivarsson, 2015). Most importantly, display- and array-based screening methods cannot measure quantitative interaction affinities (${\displaystyle K_d}$s or ${\displaystyle \mathrm{\Delta }\mathrm{\Delta }G}$s), as these measurements require washes that take the system out of thermodynamic equilibrium and lead to preferential loss of interactions with faster off-rates (Zhou, 2012; Dogan et al., 2015; Ivarsson and Jemth, 2019). As a result, candidate interactions identified through screening methods are typically validated and their affinities quantified via subsequent quantitative, low-throughput methods that require large amounts of material and are labor-intensive (e.g. isothermal calorimetry, fluorescence polarization, or surface plasmon resonance) (Gibson et al., 2015; Davey et al., 2017; Dinkel et al., 2016; Tonikian et al., 2008). The ability to measure binding affinities for hundreds of protein-peptide interactions in parallel would simultaneously remove this validation and quantification bottleneck and allow quantitative mapping of binding specificity landscapes to improve target prediction in vivo.

Here, we present a powerful bead-based technology for quantitatively measuring affinities for many SLiM-mediated protein-peptide interactions in parallel using very small amounts of material. Peptides are synthesized directly on spectrally encoded beads (MRBLEs, Microspheres with Ratiometric Barcode Lanthanide Encoding) (Gerver et al., 2012; Nguyen et al., 2017a) with a unique linkage between each peptide sequence and a given spectral code. MRBLE-pep libraries can then be pooled and assayed for protein binding in a single small volume before being imaged to identify the peptide sequence associated with each bead and quantify the amount of protein bound. On-MRBLE chemical synthesis allows for precise control of peptide density, incorporation of PTMs at known locations, and in-line assessment of peptide quality via mass spectrometry to identify amino acids that ablate and promote protein binding with equal confidence. Most importantly, MRBLE polymeric beads have been deliberately engineered to have slow on- and off-rates, thereby slowing dissociation and allowing quantitative measurement interaction strengths for of weak and transient interactions difficult to detect via other techniques. MRBLE-pep provides a critical complement to high-throughput screening approaches, enabling facile downstream screening of candidate motif hits as well as systematic mapping of how individual residues at each position within the motif contribute to binding affinity and specificity.

We use MRBLE-pep to systematically mutate residues at each position within and flanking three previously-characterized PxIxIT sequences and quantify their effects on affinity. We find that flanking amino acids and post-translational modifications play surprisingly critical and previously unappreciated roles in determining affinity and specificity. Through iterative cycles of mutagenesis, in vitro affinity measurements, and in vivo activity measurements, we map the CN-PxIxIT affinity landscape and identify several PxIxIT peptides of unprecedented binding affinity that could prove useful for future development of additional potent CN inhibitors. This approach can be applied to a broad range of protein-SLiM interactions to map binding energy landscapes, model signaling networks, and identify novel therapeutic inhibitors.

Here, we demonstrate a novel strategy to quantitatively profile parts of the binding specificity landscape for a peptide-protein interaction, thereby generating key insights required to map and model essential signaling networks in healthy and diseased cells. Our approach is complementary to current techniques, with the potential to provide new insights into SLiM recognition and function throughout signaling networks. Yeast and phage display can powerfully discriminate between ‘bound’ and ‘unbound’ peptide populations (Bazan et al., 2012; Cherf and Cochran, 2015) for > 10⁸ protein-peptide interactions, and array-based methods can return information about qualitative binding strengths for many protein-peptide interactions in parallel. However, neither method can return the quantitative thermodynamic constants (dissociation constants (K_ds) and relative binding energies (${\displaystyle \mathrm{\Delta }\mathrm{\Delta }G}$s) essential for predicting protein occupancies in vivo and effects on downstream signaling responses. For display- and array-based screens, measurements take place far from equilibrium, biasing recovered interactions towards the strongest binding motifs and losing information about those interactions with ${\displaystyle K_d}$s close to physiological concentrations most likely to be dynamically regulated in vivo.

The MRBLE-pep assay is faster and requires less in material than alternative low-throughput quantitative methods: measuring interaction affinities for 384 peptides using MRBLE-pep requires ∼400x, ∼700x, and ∼2000x less purified protein than surface plasmon resonance, fluorescence polarization, and isothermal calorimetry, respectively, with savings increasing with library size (Supplementary file 2). This reduction in material should allow future profiling of a wide range of biologically important SLiM-binding domains, including those that are uncharacterized, unstable, and/or difficult to express recombinantly. MRBLE-pep libraries can be synthesized, assayed, and imaged in days, facilitating iterative rounds of synthesis and measurement for efficient affinity landscape mapping as well as testing and refining computational prediction algorithms. After screening large sequence spaces for candidate interaction motifs, MRBLE-pep can be used to quickly and efficiently scan through thousands of potential SLiMs to validate true hits, measure affinities for these hits in high-throughput, and systematically perturb amino acids within motifs to derive a position-specific affinity matrix that can be used to predict additional candidate downstream targets in vivo. Moreover, the ability to tune the density of applied peptides enables fast optimization to probe the affinity range of interest as well as test for surface density-induced binding artifacts. An elegant ‘hold-up’ assay that couples high-throughput affinity purification to microfluidic gel electrophoresis recently demonstrated a similar ability to measure relative affinities for 400 domain-peptide pairs (Vincentelli et al., 2015). However, this method requires considerably more infrastructure than the MRBLE-pep assay and has not yet been used in subsequent work. In future work, the MRBLE-pep platform could be combined with a simple and portable imaging device to allow labs to employ the assay with only standard laboratory equipment (e.g. pipettes and a benchtop centrifuge).

MRBLE-pep measurements can also be combined with data from high-throughput in vivo methods to decipher the structure and wiring of cellular signaling networks. Yeast two-hybrid (Y2H) assays and affinity purification coupled to mass spectrometry (AP-MS) techniques have revealed tens of thousands of new interactions within the human interactome (Rolland et al., 2014; Huttlin et al., 2015; Huttlin et al., 2017). However, SLiM-mediated binding interactions remain statistically underrepresented in these datasets (Neduva and Russell, 2006; Van Roey et al., 2014; Davey et al., 2012), likely due to fast dissociation rates, and AP-MS results cannot distinguish between binary interactions and higher-order complexes, and provide no direct information about the motif involved. MRBLE-pep measurements of peptides tiled across candidate interacting proteins could confirm candidate interactors, identify the SLiMs responsible, and determine whether a candidate interaction is direct or indirect. In particular, the ability to return information about residues that ablate binding can significantly improve the ability to predict true targets in vivo (Krystkowiak et al., 2018). To test candidate inhibitor specificities, MRBLE-pep could profile binding of a broad set of cellular proteins against high-affinitiy inhibitors with unnatural amino acids returned from recently developed high-throughput mass spectrometry-based (LC/MS/MS) approaches (Gates et al., 2018; Vinogradov et al., 2017).

Beyond the method itself, our findings reveal biologically significant insights into CN-PxIxIT specificity and illustrate how systematic analyses can shed light on elusive SLiM-protein interactions. The MRBLE-pep data and in vivo validation presented here are the first to reveal the impact of PxIxIT flanking residues on affinity, allowing identification of several peptides whose affinity for CN is two orders of magnitude higher than that of the highest affinity known substrate (PVIVIT) and potently inhibit CN-NFAT signaling in vivo. We reveal the presence of significant epistasis between residues, including one interaction important for the high affinity of a known CN inhibitor (A238L) (Figure 3E). Finally, we provide affinity measurements for a collection of peptides based on different known PxIxIT scaffolds with a wide variety of affinities, yielding a tool kit of peptide sequences that can be used to probe CN recognition and function in vivo.

In addition to identifying candidate peptides for manipulation of CN signaling, this first demonstration that residues both upstream and downstream contribute to PxIxIT-CN affinity redefines the motif itself. Current computational strategies attempt to identify novel CN substrates by searching for sequences within the proteome that match a consensus for the six core residues alone. The expanded 10-residue definition of the PxIxIT motif derived here will significantly enhance future computational efforts to comprehensively map the CN signaling network, reducing the number of both false positive and false negative substrates. In addition, MRBLE-pep provides critical Information about residues that reduce affinity (e.g. acidic residues at the −1 position reduces affinity) that is never revealed by positive screening methods and is rarely collected due to the resource-intensive nature of such analyses using established techniques. The ability to predict affinity based on SLiM sequence is an essential step toward accurate modeling of signaling dynamics in vivo, as the extent of in vivo dephosphorylation has previously been shown to depend on primary PxIxIT amino acid sequence (and the associated interaction affinity) (Roy et al., 2007). Even subtle (∼2-fold) differences in binding could have profound effects on downstream signaling under physiologic conditions given the low affinities of CN-PxIxIT interactions.

Establishing the impact of post translational modifications on SLiM-protein affinity is critical for understanding cross talk between signaling networks in vivo. Consistent with our findings that acidic residues at the −1 position decrease affinity, JNK kinase regulates CN signaling by phosphorylating a serine that immediately precedes the PxIxIT of NFAT4 (Chow et al., 2000). Similarly, our demonstration that phosphorylated residues downstream of the core PxIxIT sequence at position nine enhance PVIVIT affinity echoes observations that phosphorylation of a threonine in this position is required for binding of CN to a PxIxIT site in C16ORF74, and for its ability to promote invasiveness of pancreatic ductal adenocarcinoma (PDAC) (Nakamura et al., 2017). This positive effect on binding is context-dependent, increasing PVIVIT binding 50-fold but having little or no effect on other PxIxIT sequences, reinforcing the importance of systematic analyses for generating predictive information.

Beyond improving the ability to reconstruct downstream CN signaling networks in vivo, these results also have direct relevance to precision medicine. Current annotations of 2RQ, 7 PA, and 7PL missense mutations within the NFATc2 PxIxIT site recovered from sequenced lung and breast adenocarcinomas describe them as unlikely to have functional effects in vivo (cBioPortal, 2018). While the experiments presented here did not directly test the functional consequences of a R2Q mutation, all mutations away from the native residue at this position resulted in a dramatic loss of binding (Figure 3—figure supplement 17), and the P7A substitution led to a nearly complete loss of binding (Figure 3—figure supplement 6, Figure 3—figure supplement 17, Figure 3—figure supplement 18). Thus, binding specificity maps like those obtained here could both help clinicians identify functionally significant missense mutations, and refine the computational algorithms that predict the impact of such alleles. Overall, the approaches outlined here will help decipher the language of SLiM-domain binding events, and of the cellular signaling networks they define in both healthy and diseased cells.

Reagents for peptide synthesis were purchased and used without further purification from NovaBiochem, AnaSpec (Fremont, CA), and Sigma-Aldrich (St. Louis, MO). All other solvents and chemical reagents were purchased from Sigma-Aldrich.

All data generated or analysed during this study are included in the manuscript and supporting files. In addition, all data generated or analyzed during this study are available in an associated public OSF repository (https://doi.org/10.17605/OSF.IO/FPVE2).

1. 1. Polly Morrell Fordyce
   2. Huy Quoc Nguyen
   3. Björn Harink

   (2018) Open Science Framework

   Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads.

   https://doi.org/10.17605/OSF.IO/FPVE2

1. 1. Aldeghi M
   2. Gapsys V
   3. de Groot BL

   (2018) Accurate estimation of ligand binding affinity changes upon protein mutation

   ACS Central Science 4:1708–1718.

   https://doi.org/10.1021/acscentsci.8b00717

   • PubMed
   • Google Scholar
2. 1. Aramburu J
   2. Garcia-Cózar F
   3. Raghavan A
   4. Okamura H
   5. Rao A
   6. Hogan PG

   (1998) Selective inhibition of NFAT activation by a peptide spanning the calcineurin targeting site of NFAT

   Molecular Cell 1:627–637.

   https://doi.org/10.1016/S1097-2765(00)80063-5

   • PubMed
   • Google Scholar
3. 1. Aramburu J
   2. Yaffe MB
   3. López-Rodríguez C
   4. Cantley LC
   5. Hogan PG
   6. Rao A

   (1999) Affinity-driven peptide selection of an NFAT inhibitor more selective than cyclosporin A

   Science 285:2129–2133.

   https://doi.org/10.1126/science.285.5436.2129

   • PubMed
   • Google Scholar
4. 1. Atwater J
   2. Mattes DS
   3. Streit B
   4. von Bojničić-Kninski C
   5. Loeffler FF
   6. Breitling F
   7. Fuchs H
   8. Hirtz M

   (2018) Combinatorial synthesis of macromolecular arrays by microchannel cantilever spotting (µCS)

   Advanced Materials 30:1801632.

   https://doi.org/10.1002/adma.201801632

   • Google Scholar
5. Book

   1. Bagshaw CR

   (2017)

   Biomolecular Kinetics: A Step-by-Step Guide

   CRC Press.

   • Google Scholar
6. 1. Barlow KA
   2. Ó Conchúir S
   3. Thompson S
   4. Suresh P
   5. Lucas JE
   6. Heinonen M
   7. Kortemme T

   (2018) Flex ddG: rosetta Ensemble-Based estimation of changes in Protein-Protein binding affinity upon mutation

   The Journal of Physical Chemistry B 122:5389–5399.

   https://doi.org/10.1021/acs.jpcb.7b11367

   • PubMed
   • Google Scholar
7. 1. Bazan J
   2. Całkosiński I
   3. Gamian A

   (2012) Phage display—A powerful technique for immunotherapy

   Human Vaccines & Immunotherapeutics 8:1817–1828.

   https://doi.org/10.4161/hv.21703

   • Google Scholar
8. 1. Blikstad C
   2. Ivarsson Y

   (2015) High-throughput methods for identification of protein-protein interactions involving short linear motifs

   Cell Communication and Signaling 13:38.

   https://doi.org/10.1186/s12964-015-0116-8

   • PubMed
   • Google Scholar
9. 1. Buus S
   2. Rockberg J
   3. Forsström B
   4. Nilsson P
   5. Uhlen M
   6. Schafer-Nielsen C

   (2012) High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays

   Molecular & Cellular Proteomics 11:1790–1800.

   https://doi.org/10.1074/mcp.M112.020800

   • PubMed
   • Google Scholar
10. 1. Carmona SJ
    2. Nielsen M
    3. Schafer-Nielsen C
    4. Mucci J
    5. Altcheh J
    6. Balouz V
    7. Tekiel V
    8. Frasch AC
    9. Campetella O
    10. Buscaglia CA
    11. Agüero F

    (2015) Towards High-throughput immunomics for infectious diseases: use of Next-generation peptide microarrays for rapid discovery and mapping of antigenic determinants

    Molecular & Cellular Proteomics 14:1871–1884.

    https://doi.org/10.1074/mcp.M114.045906

    • PubMed
    • Google Scholar
11. 1. cBioPortal

    (2018) cBioPortal for Cancer Genomics Online

    cBioPortal for Cancer Genomics Online, http://www.cbioportal.org/.

    http://www.cbioportal.org/

    • Google Scholar
12. 1. Chen X
    2. Tan PH
    3. Zhang Y
    4. Pei D

    (2009) On-bead screening of combinatorial libraries: reduction of nonspecific binding by decreasing surface ligand density

    Journal of Combinatorial Chemistry 11:604–611.

    https://doi.org/10.1021/cc9000168

    • PubMed
    • Google Scholar
13. 1. Cherf GM
    2. Cochran JR

    (2015) Applications of yeast surface display for protein engineering

    Methods in Molecular Biology 1319:155–175.

    https://doi.org/10.1007/978-1-4939-2748-7_8

    • PubMed
    • Google Scholar
14. 1. Chow CW
    2. Dong C
    3. Flavell RA
    4. Davis RJ

    (2000) c-Jun NH(2)-terminal kinase inhibits targeting of the protein phosphatase calcineurin to NFATc1

    Molecular and Cellular Biology 20:5227–5234.

    https://doi.org/10.1128/MCB.20.14.5227-5234.2000

    • PubMed
    • Google Scholar
15. 1. Davey NE
    2. Van Roey K
    3. Weatheritt RJ
    4. Toedt G
    5. Uyar B
    6. Altenberg B
    7. Budd A
    8. Diella F
    9. Dinkel H
    10. Gibson TJ

    (2012) Attributes of short linear motifs

    Mol. BioSyst. 8:268–281.

    https://doi.org/10.1039/C1MB05231D

    • PubMed
    • Google Scholar
16. 1. Davey NE
    2. Seo MH
    3. Yadav VK
    4. Jeon J
    5. Nim S
    6. Krystkowiak I
    7. Blikstad C
    8. Dong D
    9. Markova N
    10. Kim PM
    11. Ivarsson Y

    (2017) Discovery of short linear motif-mediated interactions through phage display of intrinsically disordered regions of the human proteome

    The FEBS Journal 284:485–498.

    https://doi.org/10.1111/febs.13995

    • PubMed
    • Google Scholar
17. 1. DeJournette CJ
    2. Kim J
    3. Medlen H
    4. Li X
    5. Vincent LJ
    6. Easley CJ

    (2013) Creating biocompatible oil-water interfaces without synthesis: direct interactions between primary amines and carboxylated perfluorocarbon surfactants

    Analytical Chemistry 85:10556–10564.

    https://doi.org/10.1021/ac4026048

    • PubMed
    • Google Scholar
18. 1. Dinkel H
    2. Van Roey K
    3. Michael S
    4. Kumar M
    5. Uyar B
    6. Altenberg B
    7. Milchevskaya V
    8. Schneider M
    9. Kühn H
    10. Behrendt A
    11. Dahl SL
    12. Damerell V
    13. Diebel S
    14. Kalman S
    15. Klein S
    16. Knudsen AC
    17. Mäder C
    18. Merrill S
    19. Staudt A
    20. Thiel V
    21. Welti L
    22. Davey NE
    23. Diella F
    24. Gibson TJ

    (2016) ELM 2016--data update and new functionality of the eukaryotic linear motif resource

    Nucleic Acids Research 44:D294–D300.

    https://doi.org/10.1093/nar/gkv1291

    • PubMed
    • Google Scholar
19. 1. Dogan J
    2. Jonasson J
    3. Andersson E
    4. Jemth P

    (2015) Binding rate constants reveal distinct features of disordered protein domains

    Biochemistry 54:4741–4750.

    https://doi.org/10.1021/acs.biochem.5b00520

    • PubMed
    • Google Scholar
20. 1. Engelmann BW
    2. Kim Y
    3. Wang M
    4. Peters B
    5. Rock RS
    6. Nash PD

    (2014) The development and application of a quantitative peptide microarray based approach to protein interaction domain specificity space

    Molecular & Cellular Proteomics 13:3647–3662.

    https://doi.org/10.1074/mcp.O114.038695

    • PubMed
    • Google Scholar
21. 1. Filippakopoulos P
    2. Picaud S
    3. Mangos M
    4. Keates T
    5. Lambert JP
    6. Barsyte-Lovejoy D
    7. Felletar I
    8. Volkmer R
    9. Müller S
    10. Pawson T
    11. Gingras AC
    12. Arrowsmith CH
    13. Knapp S

    (2012) Histone recognition and large-scale structural analysis of the human bromodomain family

    Cell 149:214–231.

    https://doi.org/10.1016/j.cell.2012.02.013

    • PubMed
    • Google Scholar
22. 1. Fodor SP
    2. Read JL
    3. Pirrung MC
    4. Stryer L
    5. Lu AT
    6. Solas D

    (1991) Light-directed, spatially addressable parallel chemical synthesis

    Science 251:767–773.

    https://doi.org/10.1126/science.1990438

    • PubMed
    • Google Scholar
23. 1. Forsström B
    2. Axnäs BB
    3. Stengele KP
    4. Bühler J
    5. Albert TJ
    6. Richmond TA
    7. Hu FJ
    8. Nilsson P
    9. Hudson EP
    10. Rockberg J
    11. Uhlen M

    (2014) Proteome-wide epitope mapping of antibodies using ultra-dense peptide arrays

    Molecular & Cellular Proteomics 13:1585–1597.

    https://doi.org/10.1074/mcp.M113.033308

    • PubMed
    • Google Scholar
24. 1. Frank R
    2. Güler S
    3. Krause S
    4. Lindenmaier W

    (1990)

    Facile and rapid “spot-synthesis” of large numbers of peptides onmembrane sheets

    Peptides 2:151–152.

    • Google Scholar
25. 1. Fruman DA
    2. Klee CB
    3. Bierer BE
    4. Burakoff SJ

    (1992) Calcineurin phosphatase activity in T lymphocytes is inhibited by FK 506 and cyclosporin A

    PNAS 89:3686–3690.

    https://doi.org/10.1073/pnas.89.9.3686

    • PubMed
    • Google Scholar
26. 1. Gallicchio E
    2. Levy RM

    (2011)

    Advances in Protein Chemistry and Structural Biology

    27–80, Recent theoretical and computational advances for modeling protein–ligand binding affinities, Advances in Protein Chemistry and Structural Biology, 85, Elsevier, 10.1016/B978-0-12-386485-7.00002-8.

    • Google Scholar
27. 1. Gates ZP
    2. Vinogradov AA
    3. Quartararo AJ
    4. Bandyopadhyay A
    5. Choo ZN
    6. Evans ED
    7. Halloran KH
    8. Mijalis AJ
    9. Mong SK
    10. Simon MD
    11. Standley EA
    12. Styduhar ED
    13. Tasker SZ
    14. Touti F
    15. Weber JM
    16. Wilson JL
    17. Jamison TF
    18. Pentelute BL

    (2018) Xenoprotein engineering via synthetic libraries

    PNAS 115:E5298–E5306.

    https://doi.org/10.1073/pnas.1722633115

    • PubMed
    • Google Scholar
28. 1. Gerver RE
    2. Gómez-Sjöberg R
    3. Baxter BC
    4. Thorn KS
    5. Fordyce PM
    6. Diaz-Botia CA
    7. Helms BA
    8. DeRisi JL

    (2012) Programmable microfluidic synthesis of spectrally encoded microspheres

    Lab Chip 12:4716–4723.

    https://doi.org/10.1039/C2LC40699C

    • PubMed
    • Google Scholar
29. 1. Gfeller D
    2. Butty F
    3. Wierzbicka M
    4. Verschueren E
    5. Vanhee P
    6. Huang H
    7. Ernst A
    8. Dar N
    9. Stagljar I
    10. Serrano L
    11. Sidhu SS
    12. Bader GD
    13. Kim PM

    (2011) The multiple-specificity landscape of modular peptide recognition domains

    Molecular Systems Biology 7:484.

    https://doi.org/10.1038/msb.2011.18

    • PubMed
    • Google Scholar
30. 1. Gianni S
    2. Jemth P

    (2017) How fast is Protein-Ligand association?

    Trends in Biochemical Sciences 42:847–849.

    https://doi.org/10.1016/j.tibs.2017.08.007

    • PubMed
    • Google Scholar
31. 1. Gibson TJ
    2. Dinkel H
    3. Van Roey K
    4. Diella F

    (2015) Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad

    Cell Communication and Signaling 13:42.

    https://doi.org/10.1186/s12964-015-0121-y

    • PubMed
    • Google Scholar
32. 1. Grigoriu S
    2. Bond R
    3. Cossio P
    4. Chen JA
    5. Ly N
    6. Hummer G
    7. Page R
    8. Cyert MS
    9. Peti W

    (2013) The molecular mechanism of substrate engagement and immunosuppressant inhibition of calcineurin

    PLOS Biology 11:e1001492.

    https://doi.org/10.1371/journal.pbio.1001492

    • PubMed
    • Google Scholar
33. 1. Guerois R
    2. Nielsen JE
    3. Serrano L

    (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations

    Journal of Molecular Biology 320:369–387.

    https://doi.org/10.1016/S0022-2836(02)00442-4

    • PubMed
    • Google Scholar
34. Software

    1. Harink B

    (2018) Decoding and analysis software for mrbles (microspheres with ratiometric barcode lanthanide encoding)

    Github.

    https://github.com/FordyceLab/MRBLEs
35. 1. Harink B
    2. Nguyen H
    3. Thorn K
    4. Fordyce P

    (2019) An open-source software analysis package for microspheres with ratiometric barcode lanthanide encoding (MRBLEs)

    PLOS ONE 14:e0203725.

    https://doi.org/10.1371/journal.pone.0203725

    • PubMed
    • Google Scholar
36. 1. Hein JB
    2. Hertz EPT
    3. Garvanska DH
    4. Kruse T
    5. Nilsson J

    (2017) Distinct kinetics of serine and threonine dephosphorylation are essential for mitosis

    Nature Cell Biology 19:1433–1440.

    https://doi.org/10.1038/ncb3634

    • PubMed
    • Google Scholar
37. 1. Hintersteiner M
    2. Kimmerlin T
    3. Kalthoff F
    4. Stoeckli M
    5. Garavel G
    6. Seifert JM
    7. Meisner NC
    8. Uhl V
    9. Buehler C
    10. Weidemann T
    11. Auer M

    (2009) Single bead labeling method for combining confocal fluorescence on-bead screening and solution validation of tagged one-bead one-compound libraries

    Chemistry & Biology 16:724–735.

    https://doi.org/10.1016/j.chembiol.2009.06.011

    • PubMed
    • Google Scholar
38. 1. Huttlin EL
    2. Ting L
    3. Bruckner RJ
    4. Gebreab F
    5. Gygi MP
    6. Szpyt J
    7. Tam S
    8. Zarraga G
    9. Colby G
    10. Baltier K
    11. Dong R
    12. Guarani V
    13. Vaites LP
    14. Ordureau A
    15. Rad R
    16. Erickson BK
    17. Wühr M
    18. Chick J
    19. Zhai B
    20. Kolippakkam D
    21. Mintseris J
    22. Obar RA
    23. Harris T
    24. Artavanis-Tsakonas S
    25. Sowa ME
    26. De Camilli P
    27. Paulo JA
    28. Harper JW
    29. Gygi SP

    (2015) The BioPlex network: a systematic exploration of the human interactome

    Cell 162:425–440.

    https://doi.org/10.1016/j.cell.2015.06.043

    • PubMed
    • Google Scholar
39. 1. Huttlin EL
    2. Bruckner RJ
    3. Paulo JA
    4. Cannon JR
    5. Ting L
    6. Baltier K
    7. Colby G
    8. Gebreab F
    9. Gygi MP
    10. Parzen H
    11. Szpyt J
    12. Tam S
    13. Zarraga G
    14. Pontano-Vaites L
    15. Swarup S
    16. White AE
    17. Schweppe DK
    18. Rad R
    19. Erickson BK
    20. Obar RA
    21. Guruharsha KG
    22. Li K
    23. Artavanis-Tsakonas S
    24. Gygi SP
    25. Harper JW

    (2017) Architecture of the human interactome defines protein communities and disease networks

    Nature 545:505–509.

    https://doi.org/10.1038/nature22366

    • PubMed
    • Google Scholar
40. 1. Ivarsson Y
    2. Arnold R
    3. McLaughlin M
    4. Nim S
    5. Joshi R
    6. Ray D
    7. Liu B
    8. Teyra J
    9. Pawson T
    10. Moffat J
    11. Li SS
    12. Sidhu SS
    13. Kim PM

    (2014) Large-scale interaction profiling of PDZ domains through proteomic peptide-phage display using human and viral phage peptidomes

    PNAS 111:2542–2547.

    https://doi.org/10.1073/pnas.1312296111

    • PubMed
    • Google Scholar
41. 1. Ivarsson Y
    2. Jemth P

    (2019) Affinity and specificity of motif-based protein-protein interactions

    Current Opinion in Structural Biology 54:26–33.

    https://doi.org/10.1016/j.sbi.2018.09.009

    • PubMed
    • Google Scholar
42. 1. Jain J
    2. McCaffrey PG
    3. Miner Z
    4. Kerppola TK
    5. Lambert JN
    6. Verdine GL
    7. Curran T
    8. Rao A

    (1993) The T-cell transcription factor NFATp is a substrate for calcineurin and interacts with fos and jun

    Nature 365:352–355.

    https://doi.org/10.1038/365352a0

    • PubMed
    • Google Scholar
43. 1. Jiang L
    2. Barclay AN

    (2009) New assay to detect low-affinity interactions and characterization of leukocyte receptors for collagen including leukocyte-associated Ig-like receptor-1 (LAIR-1)

    European Journal of Immunology 39:1167–1175.

    https://doi.org/10.1002/eji.200839188

    • PubMed
    • Google Scholar
44. 1. Kaushansky A
    2. Allen JE
    3. Gordus A
    4. Stiffler MA
    5. Karp ES
    6. Chang BH
    7. MacBeath G

    (2010) Quantifying protein-protein interactions in high throughput using protein domain microarrays

    Nature Protocols 5:773–790.

    https://doi.org/10.1038/nprot.2010.36

    • PubMed
    • Google Scholar
45. 1. Kilburg D
    2. Gallicchio E

    (2016)

    Advances in Protein Chemistry and Structural Biology

    27–57, Recent advances in computational models for the study of protein–peptide interactions, Advances in Protein Chemistry and Structural Biology, 105, Elsevier.

    • Google Scholar
46. 1. Krystkowiak I
    2. Manguy J
    3. Davey NE

    (2018) PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants

    Nucleic Acids Research 46:W235–W241.

    https://doi.org/10.1093/nar/gky426

    • PubMed
    • Google Scholar
47. 1. Kumaresan PR
    2. Wang Y
    3. Saunders M
    4. Maeda Y
    5. Liu R
    6. Wang X
    7. Lam KS

    (2011) Rapid discovery of death ligands with one-bead-two-compound combinatorial library methods

    ACS Combinatorial Science 13:259–264.

    https://doi.org/10.1021/co100069t

    • PubMed
    • Google Scholar
48. 1. Lam KS
    2. Salmon SE
    3. Hersh EM
    4. Hruby VJ
    5. Kazmierski WM
    6. Knapp RJ

    (1991) A new type of synthetic peptide library for identifying ligand-binding activity

    Nature 354:82–84.

    https://doi.org/10.1038/354082a0

    • PubMed
    • Google Scholar
49. 1. Leaver-Fay A
    2. Tyka M
    3. Lewis SM
    4. Lange OF
    5. Thompson J
    6. Jacak R
    7. Kaufman KW
    8. Renfrew PD
    9. Smith CA
    10. Sheffler W

    (2011)

    Methods in Enzymology

    545–574, Rosetta3: an object-oriented software suite for the simulation and design of macromolecules, Methods in Enzymology, 487, Elsevier, 10.1016/B978-0-12-381270-4.00019-6.

    • Google Scholar
50. 1. Lee J
    2. Bisso PW
    3. Srinivas RL
    4. Kim JJ
    5. Swiston AJ
    6. Doyle PS

    (2014) Universal process-inert encoding architecture for polymer microparticles

    Nature Materials 13:524–529.

    https://doi.org/10.1038/nmat3938

    • PubMed
    • Google Scholar
51. 1. Li H
    2. Rao A
    3. Hogan PG

    (2004) Structural delineation of the calcineurin-NFAT interaction and its parallels to PP1 targeting interactions

    Journal of Molecular Biology 342:1659–1674.

    https://doi.org/10.1016/j.jmb.2004.07.068

    • PubMed
    • Google Scholar
52. 1. Li H
    2. Zhang L
    3. Rao A
    4. Harrison SC
    5. Hogan PG

    (2007) Structure of calcineurin in complex with PVIVIT peptide: portrait of a low-affinity signalling interaction

    Journal of Molecular Biology 369:1296–1306.

    https://doi.org/10.1016/j.jmb.2007.04.032

    • PubMed
    • Google Scholar
53. 1. Li H
    2. Pink MD
    3. Murphy JG
    4. Stein A
    5. Dell'Acqua ML
    6. Hogan PG

    (2012) Balanced interactions of calcineurin with AKAP79 regulate Ca2+-calcineurin-NFAT signaling

    Nature Structural & Molecular Biology 19:337–345.

    https://doi.org/10.1038/nsmb.2238

    • PubMed
    • Google Scholar
54. 1. Liu R
    2. Marik J
    3. Lam KS

    (2002) A novel peptide-based encoding system for "one-bead one-compound" peptidomimetic and small molecule combinatorial libraries

    Journal of the American Chemical Society 124:7678–7680.

    https://doi.org/10.1021/ja026421t

    • PubMed
    • Google Scholar
55. 1. Liu T
    2. Qian Z
    3. Xiao Q
    4. Pei D

    (2011) High-throughput screening of one-bead-one-compound libraries: identification of cyclic peptidyl inhibitors against calcineurin/NFAT interaction

    ACS Combinatorial Science 13:537–546.

    https://doi.org/10.1021/co200101w

    • PubMed
    • Google Scholar
56. 1. Meldal M

    (2002) The one-bead two-compound assay for solid phase screening of combinatorial libraries

    Biopolymers 66:93–100.

    https://doi.org/10.1002/bip.10229

    • PubMed
    • Google Scholar
57. 1. Mulero MC
    2. Aubareda A
    3. Orzáez M
    4. Messeguer J
    5. Serrano-Candelas E
    6. Martínez-Hoyer S
    7. Messeguer A
    8. Pérez-Payá E
    9. Pérez-Riba M

    (2009) Inhibiting the calcineurin-NFAT (nuclear factor of activated T cells) signaling pathway with a regulator of calcineurin-derived peptide without affecting general calcineurin phosphatase activity

    Journal of Biological Chemistry 284:9394–9401.

    https://doi.org/10.1074/jbc.M805889200

    • PubMed
    • Google Scholar
58. 1. Müller MR
    2. Sasaki Y
    3. Stevanovic I
    4. Lamperti ED
    5. Ghosh S
    6. Sharma S
    7. Gelinas C
    8. Rossi DJ
    9. Pipkin ME
    10. Rajewsky K
    11. Hogan PG
    12. Rao A

    (2009) Requirement for balanced ca/NFAT signaling in hematopoietic and embryonic development

    PNAS 106:7034–7039.

    https://doi.org/10.1073/pnas.0813296106

    • PubMed
    • Google Scholar
59. Patent

    1. Nakamura Y
    2. Katagiri T
    3. Inaki K

    (2007) Vivit polypeptides, therapeutic agent comprising the same, and method of screening for anti-cancer agent (WO2007013358A2)

    IFI CLAIMS Patent Services. United States.

    http://www.google.com/patents/WO2007013358A2
60. 1. Nakamura T
    2. Katagiri T
    3. Sato S
    4. Kushibiki T
    5. Hontani K
    6. Tsuchikawa T
    7. Hirano S
    8. Nakamura Y

    (2017) Overexpression of C16orf74 is involved in aggressive pancreatic cancers

    Oncotarget 8:50460–50475.

    https://doi.org/10.18632/oncotarget.10912

    • PubMed
    • Google Scholar
61. 1. Neduva V
    2. Russell RB

    (2006) Peptides mediating interaction networks: new leads at last

    Current Opinion in Biotechnology 17:465–471.

    https://doi.org/10.1016/j.copbio.2006.08.002

    • PubMed
    • Google Scholar
62. 1. Nguyen HQ
    2. Baxter BC
    3. Brower K
    4. Diaz-Botia CA
    5. DeRisi JL
    6. Fordyce PM
    7. Thorn KS

    (2017a) Programmable microfluidic synthesis of over one thousand uniquely identifiable spectral codes

    Advanced Optical Materials 5:1600548.

    https://doi.org/10.1002/adom.201600548

    • PubMed
    • Google Scholar
63. Conference

    1. Nguyen HQ
    2. Brower K
    3. Harink B
    4. Baxter B
    5. Thorn KS
    6. Fordyce PM

    (2017b) Peptide library synthesis on spectrally encoded beads for multiplexed protein/peptide bioassays

    SPIE 10061, Microfluidics, BioMEMS, and Medical Microsystems XV. 100610Z.

    https://doi.org/10.1117/12.2254908

    • Google Scholar
64. 1. Pollard TD

    (2010) A guide to simple and informative binding assays

    Molecular Biology of the Cell 21:4061–4067.

    https://doi.org/10.1091/mbc.e10-08-0683

    • PubMed
    • Google Scholar
65. 1. Price JV
    2. Tangsombatvisit S
    3. Xu G
    4. Yu J
    5. Levy D
    6. Baechler EC
    7. Gozani O
    8. Varma M
    9. Utz PJ
    10. Liu CL

    (2012) On silico peptide microarrays for high-resolution mapping of antibody epitopes and diverse protein-protein interactions

    Nature Medicine 18:1434–1440.

    https://doi.org/10.1038/nm.2913

    • PubMed
    • Google Scholar
66. 1. Rodríguez A
    2. Roy J
    3. Martínez-Martínez S
    4. López-Maderuelo MD
    5. Niño-Moreno P
    6. Ortí L
    7. Pantoja-Uceda D
    8. Pineda-Lucena A
    9. Cyert MS
    10. Redondo JM

    (2009) A conserved docking surface on calcineurin mediates interaction with substrates and immunosuppressants

    Molecular Cell 33:616–626.

    https://doi.org/10.1016/j.molcel.2009.01.030

    • PubMed
    • Google Scholar
67. 1. Rolland T
    2. Taşan M
    3. Charloteaux B
    4. Pevzner SJ
    5. Zhong Q
    6. Sahni N
    7. Yi S
    8. Lemmens I
    9. Fontanillo C
    10. Mosca R
    11. Kamburov A
    12. Ghiassian SD
    13. Yang X
    14. Ghamsari L
    15. Balcha D
    16. Begg BE
    17. Braun P
    18. Brehme M
    19. Broly MP
    20. Carvunis AR
    21. Convery-Zupan D
    22. Corominas R
    23. Coulombe-Huntington J
    24. Dann E
    25. Dreze M
    26. Dricot A
    27. Fan C
    28. Franzosa E
    29. Gebreab F
    30. Gutierrez BJ
    31. Hardy MF
    32. Jin M
    33. Kang S
    34. Kiros R
    35. Lin GN
    36. Luck K
    37. MacWilliams A
    38. Menche J
    39. Murray RR
    40. Palagi A
    41. Poulin MM
    42. Rambout X
    43. Rasla J
    44. Reichert P
    45. Romero V
    46. Ruyssinck E
    47. Sahalie JM
    48. Scholz A
    49. Shah AA
    50. Sharma A
    51. Shen Y
    52. Spirohn K
    53. Tam S
    54. Tejeda AO
    55. Trigg SA
    56. Twizere JC
    57. Vega K
    58. Walsh J
    59. Cusick ME
    60. Xia Y
    61. Barabási AL
    62. Iakoucheva LM
    63. Aloy P
    64. De Las Rivas J
    65. Tavernier J
    66. Calderwood MA
    67. Hill DE
    68. Hao T
    69. Roth FP
    70. Vidal M

    (2014) A proteome-scale map of the human interactome network

    Cell 159:1212–1226.

    https://doi.org/10.1016/j.cell.2014.10.050

    • PubMed
    • Google Scholar
68. 1. Roy J
    2. Li H
    3. Hogan PG
    4. Cyert MS

    (2007) A conserved docking site modulates substrate affinity for Cal-cineurin, signaling output, and in vivo function

    Molecular Cell 6:889–901.

    https://doi.org/10.1016/j.molcel.2007.02.014

    • Google Scholar
69. 1. Roy J
    2. Cyert MS

    (2009) Cracking the phosphatase code: docking interactions determine substrate specificity

    Science Signaling 2:re9.

    https://doi.org/10.1126/scisignal.2100re9

    • PubMed
    • Google Scholar
70. 1. Schymkowitz J
    2. Borg J
    3. Stricher F
    4. Nys R
    5. Rousseau F
    6. Serrano L

    (2005) The FoldX web server: an online force field

    Nucleic Acids Research 33:W382–W388.

    https://doi.org/10.1093/nar/gki387

    • PubMed
    • Google Scholar
71. 1. Shapiro ND
    2. Mirica KA
    3. Soh S
    4. Phillips ST
    5. Taran O
    6. Mace CR
    7. Shevkoplyas SS
    8. Whitesides GM

    (2012) Measuring binding of protein to gel-bound ligands using magnetic levitation

    Journal of the American Chemical Society 134:5637–5646.

    https://doi.org/10.1021/ja211788e

    • PubMed
    • Google Scholar
72. 1. Sheftic SR
    2. Page R
    3. Peti W

    (2016) Investigating the human calcineurin interaction network using the πɸlxvp SLiM

    Scientific Reports 6:38920.

    https://doi.org/10.1038/srep38920

    • PubMed
    • Google Scholar
73. 1. Smith CA
    2. Kortemme T

    (2010) Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains

    Journal of Molecular Biology 402:460–474.

    https://doi.org/10.1016/j.jmb.2010.07.032

    • PubMed
    • Google Scholar
74. 1. Smith CA
    2. Kortemme T

    (2011) Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design

    PLOS ONE 6:e20451.

    https://doi.org/10.1371/journal.pone.0020451

    • PubMed
    • Google Scholar
75. 1. Sundell GN
    2. Ivarsson Y

    (2014) Interaction analysis through proteomic phage display

    BioMed Research International 2014:1–9.

    https://doi.org/10.1155/2014/176172

    • Google Scholar
76. Preprint

    1. Tareen A
    2. Kinney JB

    (2019) Logomaker: beautiful sequence logos in Python

    bioRxiv.

    https://doi.org/10.1101/635029

    • Google Scholar
77. 1. Tinti M
    2. Kiemer L
    3. Costa S
    4. Miller ML
    5. Sacco F
    6. Olsen JV
    7. Carducci M
    8. Paoluzi S
    9. Langone F
    10. Workman CT
    11. Blom N
    12. Machida K
    13. Thompson CM
    14. Schutkowski M
    15. Brunak S
    16. Mann M
    17. Mayer BJ
    18. Castagnoli L
    19. Cesareni G

    (2013) The SH2 domain interaction landscape

    Cell Reports 3:1293–1305.

    https://doi.org/10.1016/j.celrep.2013.03.001

    • PubMed
    • Google Scholar
78. 1. Tompa P
    2. Davey NE
    3. Gibson TJ
    4. Babu MM

    (2014) A million peptide motifs for the molecular biologist

    Molecular Cell 55:161–169.

    https://doi.org/10.1016/j.molcel.2014.05.032

    • PubMed
    • Google Scholar
79. 1. Tonikian R
    2. Zhang Y
    3. Sazinsky SL
    4. Currell B
    5. Yeh JH
    6. Reva B
    7. Held HA
    8. Appleton BA
    9. Evangelista M
    10. Wu Y
    11. Xin X
    12. Chan AC
    13. Seshagiri S
    14. Lasky LA
    15. Sander C
    16. Boone C
    17. Bader GD
    18. Sidhu SS

    (2008) A specificity map for the PDZ domain family

    PLOS Biology 6:e239.

    https://doi.org/10.1371/journal.pbio.0060239

    • PubMed
    • Google Scholar
80. 1. Uyar B
    2. Weatheritt RJ
    3. Dinkel H
    4. Davey NE
    5. Gibson TJ

    (2014) Proteome-wide analysis of human disease mutations in short linear motifs: neglected players in Cancer?

    Molecular BioSystems 10:2626–2642.

    https://doi.org/10.1039/C4MB00290C

    • PubMed
    • Google Scholar
81. 1. Van Roey K
    2. Uyar B
    3. Weatheritt RJ
    4. Dinkel H
    5. Seiler M
    6. Budd A
    7. Gibson TJ
    8. Davey NE

    (2014) Short linear motifs: ubiquitous and functionally diverse protein interaction modules directing cell regulation

    Chemical Reviews 114:6733–6778.

    https://doi.org/10.1021/cr400585q

    • PubMed
    • Google Scholar
82. 1. Vincentelli R
    2. Luck K
    3. Poirson J
    4. Polanowska J
    5. Abdat J
    6. Blémont M
    7. Turchetto J
    8. Iv F
    9. Ricquier K
    10. Straub ML
    11. Forster A
    12. Cassonnet P
    13. Borg JP
    14. Jacob Y
    15. Masson M
    16. Nominé Y
    17. Reboul J
    18. Wolff N
    19. Charbonnier S
    20. Travé G

    (2015) Quantifying domain-ligand affinities and specificities by high-throughput holdup assay

    Nature Methods 12:787–793.

    https://doi.org/10.1038/nmeth.3438

    • PubMed
    • Google Scholar
83. 1. Vinogradov AA
    2. Gates ZP
    3. Zhang C
    4. Quartararo AJ
    5. Halloran KH
    6. Pentelute BL

    (2017) Library Design-Facilitated High-Throughput sequencing of synthetic peptide libraries

    ACS Combinatorial Science 19:694–701.

    https://doi.org/10.1021/acscombsci.7b00109

    • PubMed
    • Google Scholar
84. 1. Zhou HX

    (2012) Intrinsic disorder: signaling via highly specific but short-lived association

    Trends in Biochemical Sciences 37:43–48.

    https://doi.org/10.1016/j.tibs.2011.11.002

    • PubMed
    • Google Scholar

Thank you for submitting your work entitled "Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Toby Gibson (Reviewer #2).

Our decision has been reached after consultation within the editorial board. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife. In particular, there was uniform agreement that the issues raised by reviewer 1 are highly unlikely to be addressed in revision.

Reviewer #1:

In this study, the authors present a new technique to measure the affinity of protein-peptide interaction in parallel. They apply their method to the well-known Calcineurin-PxIxIT interaction, obtain more data on this interaction, and use it to identify high-affinity peptides. They validate some of these peptides in a cell-based assay.

This work is based around the bead-encoded peptide-synthesis and subsequent affinity measurements using the spectral encoding. This all strikes me as technically state-of-the-art and quite elegant. However, from the paper as written, it is a bit difficult to get a true sense of the advantages of the new method over existing ones, and no direct comparison is attempted in the manuscript. For instance, mapping of the affinities of substitutions (as in Figure 4) surely would be possible using SPOT (to a slightly different degree, also using Phage). The shown r² of 0.68 is pretty good, but not all that much higher than what has been claimed for SPOT (though a little higher than for phage – see e.g., Ivarsson et al., where, of course, a much higher number of peptides is measured). One should also note that this is for the same protein – incidentally, modern computational methods (such as alchemy-based ones, e.g., TI or CGI) achieve these sort of correlations, and usually perform better than rosetta. As high-quality structures are available, they would be applicable in this case, and may perform similar to the new bead-based method.

Similar things hold for the high-affinity variants obtained here (Figure 5 or later). While obtaining low-nanomolar binding peptides is really quite good, this domain is (as the authors themselves state) what phage display really is optimized for. While their new method can incorporate non-canonical amino acids, that is similarly true of novel synthetic library methods as what recently came out of the Pentelute lab.

Finally, while the authors seem to emphasize ability to detect "weak" affinities, I only see convincing data for affinities of up to maybe 50uM, which is all still detectable using techniques such as AP-MS or phage.

I should note that I do not really want to sound too negative. I see a new technique that brings substantial technical innovation that could be very exciting in this field. However, I find it difficult to ascertain its advantages over the variety of existing methods in the manuscript as presented. I would strongly suggest to include more direct comparisons with existing methods (such as SPOT, ProP-PD, hold-up, etc.) as well a more detailed discussion thereof.

A few additional points:

1) The authors state "CN binds PxIxIT peptides with weak affinities.… estimates for known substrates have varied over a wide range.…". These statements are somewhat contradictory in itself and, in any case, the affinities listed in Figure 2A seem to be all in the single digit uM, which is neither a wide range, nor particularly weak (roughly the expected range for domain-motif interactions).

2) Given the relatively length of the current paper, it would be good scholarship to discuss existing methods (including the variety of phage-display approaches, SPOT arrays and the recently developed holdup assay) – currently, this all happens in part of a single paragraph, with some of the methods I point out above not even mentioned.

3) In a similar vein, the citations (or lack thereof) are a bit puzzling. A 12-year old paper (Neduva and Russell) is cited regarding motif-interactions in Y2H/AP-MS, when most of the relevant work was published in the last decade. For Proteomic phage display, only a review paper is cited.

Reviewer #2:

This paper introduces MRBLE-pep, a medium-scale throughput protein-peptide affinity tool and applies it to calcineurin and variants of the PxIxIT docking motif. Using MRBLEs – colour-coded microbeads, each position in the original peptide can be substituted with all other amino acids. Systematic evaluation of binding determinants can then be rapidly determined using small amounts of reagents. Efficient methods for examining SLiM specificities are vey much needed given the abundance of protein:motif interactions in cell regulation and therefore MRBLE could see widespread adoption. Although the PxIxIT motif is well known from several calcineurin substrates, there has been uncertainty regarding the motif specificity determinants as well as the cooperative involvement of the second LxVP docking motif.

Weight matrices (PSSMs/HMMs) are often used to represent SLiMs in sequence searches but assume that each position in the sequence can be treated independently of the others. Regular expressions are also in widespread use but are overdetermined and insufficiently flexible regarding sequence space. The results here can be used to improve bioinformatic PxIxIT detection in candidate substrate proteins. However, the results also emphasise that positions in the PxIxIT motif are not independent and also that flanking residues affect the binding affinity (which has been seen for a number of other linear motifs too). The data from MRBLE-pep could allow a different computational approach whereby candidate motifs in protein sequences could be compared to and ranked by the total landscape affinity data.

No substantive concerns were identified.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads" for further consideration at eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below in the first review below. Normally, we try to avoid second revisions, but we feel reviewer #1's comments should be addressed and we hope you will be able to do this easily and quickly.

Reviewer #1:

Essentially, the revisions by the authors haven't changed my opinions drastically. I still see this is as an innovative technology that will likely be of interest; however, the paper is still hampered by some shortcomings that I think could be improved pretty easily. In a way I find it a bit frustrating that the authors choose to write a full two pages of rebuttal to a simple point (correlations to measured affinity - compare these R²s between SPOT and MRBLE-pep) when they could have simply provided two numbers. Also, little of these two pages of rebuttal seems to have made it to the text (or was in there to begin with). Why not provide some kind of comparison of correlation to measured affinity to SPOT in the paper? I'm reasonably certain MRBLE-pep would come out on top.

Similarly, the authors add calculations using FoldX (that I didn't ask for). Not sure why they include comparisons of FoldX to Rosetta in the main text/figure (these would be of interest in computational papers and indeed have been done a bit, but I doubt the main readership of this paper will be interested). Also, I find it a bit frustrating that while they added a many new figures, the one figure that I think would have added to the paper (again a simple direct comparison of ddGs derived from MRBLE-pep to ones derived from rosetta OR FoldX vs. measured ddGs, i.e. combining the panels of Figure 4—figure supplement 4 into one panel and comparing it to the appropriate one for MRLBLE-pep). It's fine if the authors don't want to do any TI/FEP calculations (again, this isn't a computational paper), though if they do want to make the point that FoldX/Rosetta don't perform well on solvent exposed residues (not surprising), they should also mention that this is something that TI/FEP would, at least in theory, do better at.

They did add a much more appropriate introduction with discussion of the literature.

Reviewer #2:

The original MRBLE-pep manuscript was rather let down by superficial treatment of other PPI methods and how MARBL-pep compares to them. The resubmitted manuscript is greatly improved in this regard. This is important because SLiM researchers need to be able to understand which methods are suitable for the projects they have in mind. These range from the low throughput "gold standard" ITC up to whole proteome screens of the "disorderome" with phage display to identify novel SLiM candidates. I believe that MARBL-pep will see significant adoption and will help to define many SLiM motif patterns. The data obtained for the chosen target, the Calcineurin-binding PxIxIT motif, will hopefully now be applied in the identification of novel substrates for this medically important phosphatase. I have no further concerns.

[Editors’ note: the author responses to the first round of peer review follow.]

We are writing to follow up on the recent eLife review of our manuscript “Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads”. We believe that the criticisms are readily addressed and stem largely from the fact that we didn't clearly explain critical differences between our novel assay and current approaches. To frame this discussion, we provide an overview of the reviewers' comments below and attach a detailed response to each of the comments and suggestions.

Reviewer #2 (Toby Gibson) was very positive, noting that “efficient methods for examining SLiM specificities are very much needed given the abundance of protein:motif interactions in cell regulation”, that “MRBLE could see widespread adoption”, and that “the data from MRBLE-pep could allow a different computational approach whereby candidate motifs in protein sequences could be compared to and ranked by the total landscape affinity data”. We note that Toby Gibson is a world expert in protein-SLiM interactions, and are flattered by his positive reception to the work.

Reviewer #1 stated that the work was “technically state-of-the-art and quite elegant” and that they “see a new technique that brings substantial technical innovation that could be very exciting in this field”. However, they state that they found it “difficult to ascertain its advantages over the variety of existing methods in the manuscript as presented” and would “strongly suggest to include more direct comparisons with existing methods (such as SPOT, ProP-PD, hold-up, etc)”. This helpful suggestion is easily addressed in the text of the manuscript. To briefly summarize advantages of MRBLE-pep:

1) MRBLEs have been engineered to have very slow on- and off-rates (half-lives of ~ 5 hours), allowing equilibrium measurement of even weak and transient SLiM interactions to yield quantitative thermodynamic constants (K_D and ΔΔG). This is simply not possible with SPOT arrays or ProP-PD, as off-rates for domain-SLiM interactions are on the order of seconds, much shorter than typical washing steps such that interactions with fast off-rates are preferentially lost.

2) MRBLE-pep incorporates in-line quality control to provide critical negative information about residues that ablate or reduce binding that is not available via SPOT or ProP-PD.

3) MRBLE-pep allows direct assessment of the effects of post-translational modifications (not possible with ProP-PD).

4) MRBLE-pep requires significantly less material than current low-throughput quantitative techniques (~60000x less protein and ~6000x less peptide than micro/nano ITC).

Finally, we note that we apply this new technology towards studying a system of great biological interest and reveal new information about determinants of specificity. As Toby Gibson notes, “the results here could be used to improve bioinformatic PxIxIT in candidate substrate proteins” and “emphasize that positions in the PxIxIT motif are not independent and also that flanking residues affect the binding affinity”.

Reviewer #1:

In this study, the authors present a new technique to measure the affinity of protein-peptide interaction in parallel. They apply their method to the well-known Calcineurin-PxIxIT interaction, obtain more data on this interaction, and use it to identify high-affinity peptides. They validate some of these peptides in a cell-based assay.

This work is based around the bead-encoded peptide-synthesis and subsequent affinity measurements using the spectral encoding. This all strikes me as technically state-of-the-art and quite elegant.

We appreciate the reviewer’s kind opinion that the method is “technically state-of-the-art and quite elegant”!

However, from the paper as written, it is a bit difficult to get a true sense of the advantages of the new method over existing ones, and no direct comparison is attempted in the manuscript. For instance, mapping of the affinities of substitutions (as in Figure 4) surely would be possible using SPOT (to a slightly different degree, also using Phage). The shown r² of 0.68 is pretty good, but not all that much higher than what has been claimed for SPOT (though a little higher than for phage – see e.g., Ivarsson et al., where, of course, a much higher number of peptides is measured).

The reviewer makes an excellent point. We did not clearly explain the advantages and disadvantages of MRBLE-pep relative to current techniques, and we also did not emphasize why quantitative measurements of interaction affinities are so important! We appreciate the helpful suggestions for how to improve the manuscript. In response, we have extensively revised the text in the Introduction and Discussion to include several paragraphs comparing and contrasting MRBLE-pep to current technologies.

To ensure that we clearly communicate these points, we also discuss the advantages and disadvantages of these approaches here, where we are not constrained by length.

Array-based methods:

Array-based methods provide the ability to probe for interactions between a protein of interest and 10s^-100s (SPOT arrays) or up to millions (ultra-high density arrays) peptides in a single experiment (Frank et al., 1990; Atwater et al., 2018; Buus et al., 2012; Forsstrom et al., 2014; Price et al., 2012; Carmona et al., 2015). In most cases, peptides are synthesized in situ via chemical synthesis approaches, making it possible to profile interactions between proteins and peptides containing unnatural amino acids or post-translational modifications at known positions. However, array-based methods cannot return quantitative interaction affinities (dissociation constants (K_Ds) or relative affinities (ΔΔGs)) because the measurements do not take place at thermodynamic equilibrium. Following incubation of proteins of interest with a peptide of interest, arrays must be washed extensively prior to imaging to quantify bound material; when a fluorescently-labeled antibody is used to detect bound protein, two incubations and two washing steps are required. In either case, the long washing times (minutes to hours) relative to the interaction dissociation rates (seconds) (Zhou et al., 2012; Dogan et al., 2015; and discussion in Ivarsson et al., 2019) mean that only the most persistent interactions are retained and interactions with faster off-rates are preferentially lost. Multiple comparisons between bound protein intensities and measured affinities for array-based experiments have established that while intensities typically correlate well with affinities for high-affinity interactions, arrays fail to recover weaker interactions. Finally, array-based methods are associated with high numbers of false positive and false negative measurements because it is difficult to evaluate the yield and purity of peptides present in each spot (Blikstad et al., 2015).

MRBLE-pep offers the following advantages relative to array-based methods:

1) Equilibrium measurements to yield affinities. As shown in Figure 2—figure supplements 1 and 2, we have deliberately engineered MRBLE polymer beads to have extremely slow on- and off-rates. This property makes it possible to remove unbound material and image to quantify bound protein with the washing steps taking place over a period of time that is much smaller than the half-life of the reactions, thereby approximating equilibrium and allowing direct measurement of interaction affinities.

2) In-line quality control for yield negative binding information. The use of different chemical linkers in the MRBLE core and periphery make it possible to cleave synthesized peptides from the MRBLE core only and directly assess their quality via MALDI-TOF mass spectrometry while preserving peptides presented at the periphery for binding measurements. If quality control reveals issues with synthesis, particular MRBLE-peptide combinations can simply be re-synthesized, re-tested, and added to the pool.

3) Statistical replicates. Each pooled measurement assesses binding of protein to ~ 30 beads containing the same embedded code and displayed sequence, allowing robust discrimination of even subtle differences in binding between sequences.

Display-based methods: Display-based methods have the advantage of profiling interactions between SLiM-binding proteins and large numbers (up to 10¹⁰) of candidate SLiMs. While approaches using combinatorial peptide libraries were biased towards identifying hydrophobic substrates, Ylva Ivarsson’s lab has pioneered new approaches for profiling interactions between SLiM-binding proteins and peptides tiled across intrinsically disordered regions of the proteome (Pro- PD) that remove this bias, as well as a newer approach that profiles peptides containing either serines and threonines or phosphomimetic residues at the same position to examine phosphorylation-dependent binding specificities.

However, a primary disadvantage of display-based methods is that they cannot return thermodynamic interaction affinities. Display-based techniques require multiple rounds of selection and washing, and as a result, take place out of equilibrium and provide only semi-quantitative estimates of binding strength biased towards SLiMs with the slowest dissociation rates. While computational approaches for extracting binding affinities from all rounds of selection have recently been developed for and applied towards high-throughput measurements of transcription factor-DNA affinities via SELEX approaches (e.g. No Read Left Behind, Rastogi et al., PNAS 2018), current Pro-PD and ProP-PD experiments typically consider NGS reads from a single round of enrichment (e.g. the fourth round of selection) only. In addition, display-based techniques cannot return information about the effects of post-translational modifications. While ProP-PD incorporates phosphomimetic residues in place of native serines and threonines to attempt to probe the effects of phosphorylation, we demonstrate here that phosphomimetic residues do not always fully mimic the effects of phosphorylation (here, phosphorylation at position 9 increases affinities 50-fold while phosphomimetic residues have essentially no effect). Finally, display-based techniques return a high number of false positives and false negatives and must therefore be validated via an alternate approach such as isothermal calorimetry. Finally, all selection-based methods, including Pro-PD, return only positive information (i.e. identify motifs that interact with the domain in question), but do not directly test if a particular sequence binds. Therefore, critical information about residues that prevent binding is not identified.

In fact, MRBLE-pep is a perfect complement to Pro-PD. After identifying selected sequences via Pro-PD, MRBLE-pep could be used to validate binding to identified peptides, determine binding affinities and test effects of amino substitutions on affinity for comprehensive motif characterization. We have modified the manuscript to make this point more explicit, and appreciate the reviewer’s suggestion.

Other methods:

Y2H and AP-MS: A recent large-scale yeast-two-hybrid (Y2H) screen identified 14000 potential binary protein-protein interactions to reveal the human interactome network, representing a milestone in knowledge of this network (Rolland et al., 2014). Over the following years, other groups have expanded the knowledge of this network via AP-MS assays to detect interactions (Huttlin et al., 2015; Huttlin et al., 2017), revealing ~23,000 and 56,000 interactions, respectively. However, interactions between SLiMs and SLiM-binding proteins remain particularly underrepresented (Van Roey et al., 2014; Davey et al., 2017). Underrepresentation in AP-MS is likely due to the low affinities and fast dissociation rates for SLiM-mediated interactions and the need for washing steps in this technique. In addition, neither Y2H or AP-MS methods claim to return quantitative interaction affinities. Moreover, AP-MS reports only on the presence of an interaction between 2 protein components, without information about whether this interaction is binary or what protein fragment/motif is responsible.

Hold-up assay: The hold-up assay represents the most direct competitor to the work presented here. This method was first described in an elegant and thorough Nature Methods paper from 2015 by Vincentelli and colleagues in which they profiled interactions between 210 PDZ-peptide pairs with known affinities and then mapped the specificity landscape between 2 viral PDZ-binding motifs from human papillomavirus E6 oncoproteins and 209 PDZ domains. While this assay detects complexes binding at equilibrium (in contrast to other high-throughput methods mentioned above), in its standard form, the assay returns only ranked lists of likely interaction strengths (not actual affinities). Deriving quantitative affinity information for complexes with K_D > 8 µM requires repetition of the assay at higher protein concentrations in order to ensure that available soluble protein is not significantly depleted within the unbound fraction. As a result, the material requirements for the hold-up assay significantly exceed those for the assay presented here. Finally, we note that the hold- up assay has never been cited in subsequent experimental work, suggesting that there are significant technical barriers to widespread adoption.

Because most high-throughput technologies take place out of thermodynamic equilibrium and therefore cannot measure quantitative interaction affinities (equilibrium constants), the vast majority of SLiMs have been characterized by low- throughput experimentation (see Davey et al., 2017; Dinkel et al., 2016; Toniian et al., 2008). Rather than viewing high-throughput screening methods as competing technologies for MRBLE-pep, we believe that the two approaches are highly complementary. The large sequence spaces accessible to display-based methods make it possible to scan large sequence spaces for candidate interaction motifs. After such a scan, MRBLE-pep can be used to: (1) quickly and efficiently scan through thousands of potential SLiMs to validate true hits, (2) directly measure SLiM interaction affinities, and then (3) systematically perturb identified motifs to map the surrounding binding affinity landscape, assess binding specificity, and determine a position specific affinity matrix that can be used to more accurately predict candidate downstream targets across the proteome. MRBLE-pep has the potential to streamline tedious downstream validation and affinity measurement process relative to traditional quantitative biotechniques such as fluorescence polarization, surface plasmon resonance, and isothermal calorimetry. We have amended the Discussion to more clearly emphasize that we view MRBLE-pep as a method that is complementary to (rather than competitive with) high-throughput discovery techniques, and hope that this addresses this reviewer’s concerns.

One should also note that this is for the same protein – incidentally, modern computational methods (such as alchemy-based ones, e.g., TI or CGI) achieve these sort of correlations, and usually perform better than rosetta. As high-quality structures are available, they would be applicable in this case, and may perform similar to the new bead-based method.

We agree with the reviewer that including other computational methods will likely improve the ability to accurately predict the energetic effects of SLiM mutations. In particular, the reviewer suggests that alchemical free energy calculations may improve these predictions. However, a previous paper (Aldeghi et al., 2018) directly compared performance between these calculations and Rosetta flex_ddG and concluded:

“We show that both the free energy calculations and Rosetta are able to quantitatively predict changes in ligand binding affinity upon protein mutations, yet the best predictions are the result of combining the estimates of both methods.”

They also note the difference in computational cost:

“It is important to put the performances of the calculations in the context of their computational cost. With the free energy calculations, each ΔΔG estimate took between 2 and 5 days on a node with 20 CPU threads and 1 GPU (Intel Xeon E5-2630 v4; GTX 1080 Ti), depending on the size of the system (from ~30000 to ~100000 atoms). With the flex_ddG protocol, each ΔΔG estimate took up to a day on a single CPU core.”

To address the reviewer’s suggestion, we worked with Naomi Latorroca from Ron Dror’s lab at Stanford to use the FoldX server to predict the likely change in binding energy upon mutating different residues within PxIxIT peptides on calcineurin- PxIxIT binding affinities (see Figure 4 and associated supplementary figures and data sets). In many cases, the FoldX predictions outperformed those of the Rosetta flex_ddG protocol when assessing the ability to classify predicted substitutions as either stabilizing (ΔΔG < 0) or destabilizing (ΔΔG > 0), and we think that the manuscript has been improved as a result. We note, however, that the prediction accuracy remains somewhat low, consistent with the prevailing sentiment that predicting mutational effects for protein-peptide interactions remains a difficult problem due to the large number of possible conformations of the peptide when free in solution. MRBLE-pep therefore provides a unique opportunity to iterate quickly between computational predictions and experimental measurements, as high-throughput peptide synthesizers allow synthesis of up to 576 peptides on MRBLEs in parallel in about a week and downstream binding assays require only a few additional days. The inclusion of this analysis has substantially improved the paper and we appreciate the reviewer’s suggestion!

Similar things hold for the high-affinity variants obtained here (Figure 5 or later). While obtaining low-nanomolar binding peptides is really quite good, this domain is (as the authors themselves state) what phage display really is optimized for.

We appreciate the reviewer’s point that identifying super-binders is easily accomplished via a variety of other methods, including phage display, that allow searching of extremely large combinatorial libraries (up to 1010). However, the current approach demonstrated the ability to identify peptides with affinities 2 orders of magnitude stronger than those previously identified via phage display using only 400 measurements. This brings up an interesting question: what is the most efficient way to scan a potential sequence space? For peptides that bind as linear motifs, the results presented here suggest that systematically testing a single mutation at each position and then combining those results may provide an alternative method to obtain the same results via a much smaller number of experimental measurements.

While their new method can incorporate non-canonical amino acids, that is similarly true of novel synthetic library methods as what recently came out of the Pentelute lab.

We appreciate the discussion of the exciting new technologies that have emerged from the Pentelute lab. We regret failing to cite these in our initial submission and have amended this by including several citations in the current text. Again, however, we emphasize that these technologies are screening technologies that, while able to test a wide variety of potential interactions (including a wide variety of unnatural and modified amino acids), do not return quantitative interaction affinities. As with phage display, we view the current MRBLE-pep technology as complementary to, rather than competitive with, these screening technologies. We have amended the text to include references to 2 recent relevant publications from the Pentelute lab.

Finally, while the authors seem to emphasize ability to detect "weak" affinities, I only see convincing data for affinities of up to maybe 50uM, which is all still detectable using techniques such as AP-MS or phage.

We respectfully disagree with the reviewer’s suggestion that affinities of up to 50 µM are detectable using AP-MS, as prior analyses have demonstrated that low-affinity SLiM-mediated interactions are significantly underrepresented within these datasets, as described above. However, even if these interactions were not underrepresented within AP-MS datasets, there would still be a need to directly and quantitatively measure the interaction affinities to predict biological responses and downstream outputs, as well as to acquire in vitro data to distinguish between direct and indirect binding interactions. For this reason, we again suggest that MRBLE-pep may represent an ideal companion technology to quantify affinities for candidate AP-MS interactions to decode cellular signaling interaction networks and for use in whole-cell modeling efforts.

I should note that I do not really want to sound too negative. I see a new technique that brings substantial technical innovation that could be very exciting in this field. However, I find it difficult to ascertain its advantages over the variety of existing methods in the manuscript as presented. I would strongly suggest to include more direct comparisons with existing methods (such as SPOT, ProP-PD, hold-up, etc.) as well a more detailed discussion thereof.

We appreciate the reviewer’s encouraging comment that the review is not meant to sound too negative, as well as the sentiment that this is “a new technique that brings substantial technical innovation that could be very exciting in this field”! We believe that the reviewer’s suggestions to include more direct comparisons between MRBLE-pep and these existing techniques as well as to explore additional computational methods for predicting mutational effects on protein-peptide affinities have strengthened the paper considerably. We have significantly revised the Introduction in response to these helpful suggestions.

A few additional points:

1) The authors state "CN binds PxIxIT peptides with weak affinities.… estimates for known substrates have varied over a wide range.…". These statements are somewhat contradictory in itself and, in any case, the affinities listed in Figure 2A seem to be all in the single digit uM, which is neither a wide range, nor particularly weak (roughly the expected range for domain-motif interactions).

We agree with the reviewer that single digit µM Kd values are typical for SLiM-mediated binding interactions. However, SLiM-mediated binding interactions are typically referred to as moderate-to-weak affinity and transient (for example, please see Davey et al., 2012). To address this suggestion, we have amended the text to read “CN binds PxIxIT peptides with moderate-to-weak affinities…”. When we suggest that estimates for known substrates have varied over a wide range, we mean to say that published estimates for the same specific interaction between CN and a particular SLiM have varied over a wide range. To clarify this, we have amended the text to read: “… prior literature affinity estimates for a given substrate have varied over 50-fold.”

2) Given the relatively length of the current paper, it would be good scholarship to discuss existing methods (including the variety of phage-display approaches, SPOT arrays and the recently developed holdup assay) – currently, this all happens in part of a single paragraph, with some of the methods I point out above not even mentioned.

We appreciate the suggestion and have revised the Introduction extensively to include a more detailed discussion of other methods, as detailed above. We sincerely regret the oversight in the initial submission.

3) In a similar vein, the citations (or lack thereof) are a bit puzzling. A 12-year old paper (Neduva and Russell) is cited regarding motif-interactions in Y2H/AP-MS, when most of the relevant work was published in the last decade. For Proteomic phage display, only a review paper is cited.

The reviewer makes an excellent point. In re-reading the paper, it’s clear that we were overly focused on presenting the data and did an embarrassingly poor job of referencing much of the recent literature. We again regret this oversight and have extensively rewritten the Introduction and amended the paper to include a much more comprehensive discussion of recently published work. This is another case where we feel that the reviewer’s suggestions have significantly improved the paper by making comparisons to other methods more clear!

Reviewer #2:

[…] Weight matrices (PSSMs/HMMs) are often used to represent SLiMs in sequence searches but assume that each position in the sequence can be treated independently of the others. Regular expressions are also in widespread use but are overdetermined and insufficiently flexible regarding sequence space. The results here can be used to improve bioinformatic PxIxIT detection in candidate substrate proteins. However, the results also emphasise that positions in the PxIxIT motif are not independent and also that flanking residues affect the binding affinity (which has been seen for a number of other linear motifs too). The data from MRBLE-pep could allow a different computational approach whereby candidate motifs in protein sequences could be compared to and ranked by the total landscape affinity data.

No substantive concerns were identified.

We appreciate the reviewer’s kind words and positive assessment of the work! We particularly appreciate that the reviewer correctly noted the ability of MRBLE-pep to provide quantitative information about interaction epistasis. We have added an additional sentence to the manuscript to emphasize this point.

[Editors’ note: the author responses to the re-review follow.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below in the first review below. Normally, we try to avoid second revisions, but we feel reviewer #1's comments should be addressed and we hope you will be able to do this easily and quickly.

Reviewer #1:

Essentially, the revisions by the authors haven't changed my opinions drastically. I still see this is as an innovative technology that will likely be of interest; however, the paper is still hampered by some shortcomings that I think could be improved pretty easily. In a way I find it a bit frustrating that the authors choose to write a full two pages of rebuttal to a simple point (correlations to measured affinity – compare these R²s between SPOT and MRBLE-pep) when they could have simply provided two numbers. Also, little of these two pages of rebuttal seems to have made it to the text (or was in there to begin with). Why not provide some kind of comparison of correlation to measured affinity to SPOT in the paper? I'm reasonably certain MRBLE-pep would come out on top.

The reviewer makes an excellent suggestion, and we fully agree that this would be a very interesting and worthwhile comparison. Unfortunately, this is simply not possible with current published data, largely due to the technical challenges associated with measuring quantitative binding affinities. Prior literature includes estimates of measured affinities for only a handful of calcineurin-PxIxIT interactions, all 8 of which we also measured via MRBLE-pep (Figure 2C). The strong agreement between MRBLE-pep affinities and these 8 prior measurements is shown in Figure 2D (r2 = 0.68), with only NFATc2 (PRIEIT) showing significant deviation. To clarify that this data set represents all known prior published affinities, we have modified a sentence in the “MRBLE-pep yield quantitative measurements of calcineurin binding affinities” to read: “These 10 peptides (the “Triplicate low” dataset) represent all calcineurinbinding SLiMs for which affinities have been measured, including the known NFATc1, NFATc2, AKAP79, and RCAN1 natural CN-interacting PxIxIT binding site (citations), a set of 5 PVIVIT peptide mutants previously characterized via competitive fluorescence polarization assays (citations), and a scrambled negative control sequence.”

These peptides have not been measured via SPOT arrays, preventing the desired comparison. The only published paper using arrays to quantify binding to calcineurin assessed binding of tiled calcineurin fragments to a single PxIxIT motif (the NFATc2 SLiM SGPSPRIEITPSH) and was unable to detect any binding between calcineurin fragments and the known PVIVIT substrate (Erdmann et al., ChemBioChem 2018). To fully address the reviewer’s request without relying on previously published literature, we attempted to perform ITC for a series of calcineurin-peptide interactions for approximately 3 months but were ultimately unsuccessful.

Similarly, the authors add calculations using FoldX (that I didn't ask for). Not sure why they include comparisons of FoldX to Rosetta in the main text/figure (these would be of interest in computational papers and indeed have been done a bit, but I doubt the main readership of this paper will be interested). Also, I find it a bit frustrating that while they added a many new figures, the one figure that I think would have added to the paper (again a simple direct comparison of ddGs derived from MRBLE-pep to ones derived from rosetta OR FoldX vs. measured ddGs, i.e. combining the panels of Figure 4—figure supplement 4 into one panel and comparing it to the appropriate one for MRLBLE-pep).

Figure 4—figure supplement 5 and Figure 4—figure supplement 6 compare MRBLE-pep measurements to the predictions of FoldX and the Rosetta flex_ddG protocol, respectively. Of the prior published measurements of calcineurin- PxIxIT affinities, only a single study considered how systematic substitutions within the PVIVIT site affect measured affinities by performing fluorescence polarization measurements in the presence of competing unlabeled peptides to estimate affinities (Li and Hogan, 2007). The FoldX and Rosetta computational analyses estimate effects on affinity for all possible single-site substitutions within the PVIVITGPH site, and therefore require orthogonal measurements of single-site substitutions for comparison. The Li and Hogan experiments measure relative affinities for only the wildtype PVIVIT peptide and 2 examples of single-site substitutions from the PVIVIT motif (PVIVVT and PVIVIN); the other measurement includes 2 amino acid mutations (PVIAVT).

The only possible comparison between MRBLE-pep, FoldX, and Rosetta and prior published results therefore assesses their performance on 2 WT-like peptides (PVIVIT and PVIVVT) and one weak affinity variant (PVIVIN), resulting in a 3-point comparison for which 2 points are experimentally identical. The MRBLE-pep measurements alone (and not the FoldX and Rosetta computational predictions) correctly recapitulate that: (1) PVIVIT and PVIVVT have the same affinities) and (2) the PVIVIN substitution substantially reduces binding.

In response to the reviewer suggestion, we have included this figure as Figure 4—figure supplement 7, and also added the following to the text: “Comparisons of MRBLE-pep measurements and Rosetta and FoldX predictions of the energetic effects of single site substitutions with orthogonal measurements are complicated by a lack of previously published affinity data. However, a direct comparison between MRBLE-pep measurements and computational predictions for 2 single-site substitutions in the PVIVIT motif reveal that only MRBLE-pep correctly discerns that a PVIVVT substitution has little effect while a PVIVIN substitution reduces binding.”

It's fine if the authors don't want to do any TI/FEP calculations (again, this isn't a computational paper), though if they do want to make the point that FoldX/Rosetta don't perform well on solvent exposed residues (not surprising), they should also mention that this is something that TI/FEP would, at least in theory, do better at.

We thank the reviewer for this suggestion and have added the following sentence to the section “Comparing experimental measurements with results from structure-based computational predictions”: “Thermodynamic Integration and Free Energy Perturbation (TI/FEP) methods would likely enhance the ability to predict effects of mutating solvent-exposed residues, but are significantly more computationally expensive (Kilburg and Gallichio, 2016; Gallichio and Levy, 2011).”

They did add a much more appropriate Introduction with Discussion of the literature.

We thank the reviewer for this suggestion, agree that the discussion of the literature is much improved, and apologize for the initial oversight.

Author details

1. Huy Quoc Nguyen

   Department of Genetics, Stanford University, Stanford, United States

   Present address

   Genentech, South San Francisco, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5252-0062

2. Jagoree Roy

   Department of Biology, Stanford University, Stanford, United States

   Contribution

   Resources, Investigation, Writing—original draft, Writing—review and editing

   Contributed equally with

   Björn Harink and Nikhil P Damle

   Competing interests

   No competing interests declared

3. Björn Harink

   Department of Genetics, Stanford University, Stanford, United States

   Contribution

   Data curation, Software, Formal analysis, Visualization

   Contributed equally with

   Jagoree Roy and Nikhil P Damle

   Competing interests

   No competing interests declared

4. Nikhil P Damle

   Department of Biology, Stanford University, Stanford, United States

   Present address

   OSTHUS GmbH, Eisenbahnweg, Aachen, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation

   Contributed equally with

   Jagoree Roy and Björn Harink

   Competing interests

   No competing interests declared

5. Naomi R Latorraca

   Biophysics Program, Stanford University, Stanford, United States

   Contribution

   Formal analysis, Performed all FoldX analysis

   Competing interests

   No competing interests declared

6. Brian C Baxter

   Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Synthesized all lanthanide nanophosphors

   Competing interests

   No competing interests declared

7. Kara Brower

   Department of Bioengineering, Stanford University, Stanford, United States

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

8. Scott A Longwell

   Department of Bioengineering, Stanford University, Stanford, United States

   Contribution

   Resources, Expressed and purified tagged calcineurin

   Competing interests

   No competing interests declared

9. Tanja Kortemme

   1. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States
   2. Chan Zuckerberg Biohub, San Francisco, United States

   Contribution

   Software, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

10. Kurt S Thorn

    Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States

    Present address

    Zymergen, Inc, Emeryville, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Project administration

    Competing interests

    No competing interests declared

11. Martha S Cyert

    Department of Biology, Stanford University, Stanford, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3825-7437

12. Polly Morrell Fordyce

    1. Department of Genetics, Stanford University, Stanford, United States
    2. Department of Bioengineering, Stanford University, Stanford, United States
    3. Chan Zuckerberg Biohub, San Francisco, United States
    4. ChEM-H Institute, Stanford University, Stanford, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    pfordyce@stanford.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9505-0638

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