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Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

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Modeling Peptide-Protein Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1561))

Abstract

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

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References

  1. Chen TS, Keating AE (2012) Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods. Protein Sci 21:949–963. doi:10.1002/pro.2096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Liu BA, Engelmann BW, Nash PD (2012) High-throughput analysis of peptide-binding modules. Proteomics 12:1527–1546. doi:10.1002/pmic.201100599

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Levin AM, Weiss GA (2006) Optimizing the affinity and specificity of proteins with molecular display. Mol Biosyst 2:49–57. doi:10.1039/b511782h

    Article  CAS  PubMed  Google Scholar 

  4. Olivos HJ, Bachhawat Sikder K, Kodadek T (2003) Quantum dots as a visual aid for screening bead‐bound combinatorial libraries. Chembiochem 4:1242–1245. doi:10.1002/cbic.200300712

    Article  CAS  PubMed  Google Scholar 

  5. Rezaei Araghi R, Ryan JA, Letai A, Keating AE (2016) Rapid optimization of Mcl‐1 inhibitors using stapled peptide libraries including non-natural side chains. ACS Chem Biol 11:1238–1244. doi:10.1021/acschembio.5b01002

  6. Goldsmith M, Tawfik DS (2013) Enzyme engineering by targeted libraries. Methods Enzymol 523:257–283. doi:10.1016/B978-0-12-394292-0.00012-6

    Article  CAS  PubMed  Google Scholar 

  7. Hou T, Li N, Li Y, Wang W (2012) Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models. J Proteome Res 11:2982–2995. doi:10.1021/pr3000688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. London N, Lamphear CL, Hougland JL, Fierke CA, Schueler-Furman O (2011) Identification of a novel class of farnesylation targets by structure-based modeling of binding specificity. PLoS Comput Biol 7:e1002170. doi:10.1371/journal.pcbi.1002170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. DeBartolo J, Taipale M, Keating AE (2014) Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins. PLoS Comput Biol 10:e1003693. doi:10.1371/journal.pcbi.1003693

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sánchez IE, Beltrao P, Stricher F, Schymkowitz J, Ferkinghoff-Borg J, Rousseau F, Serrano L (2008) Genome-wide prediction of SH2 domain targets using structural information and the FoldX algorithm. PLoS Comput Biol 4:e1000052. doi:10.1371/journal.pcbi.1000052

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fernandez-Ballester G, Beltrao P, Gonzalez JM, Song Y-H, Wilmanns M, Valencia A, Serrano L (2009) Structure-based prediction of the Saccharomyces cerevisiae SH3-ligand interactions. J Mol Biol 388:902–916. doi:10.1016/j.jmb.2009.03.038

    Article  CAS  PubMed  Google Scholar 

  12. Fu X, Apgar JR, Keating AE (2007) Modeling backbone flexibility to achieve sequence diversity: the design of novel alpha-helical ligands for Bcl-xL. J Mol Biol 371:1099–1117. doi:10.1016/j.jmb.2007.04.069

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Roberts KE, Cushing PR, Boisguerin P, Madden DR, Donald BR (2012) Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity. PLoS Comput Biol 8:e1002477. doi:10.1371/journal.pcbi.1002477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zheng F, Jewell H, Fitzpatrick J, Zhang J, Mierke DF, Grigoryan G (2015) Computational design of selective peptides to discriminate between similar PDZ domains in an oncogenic pathway. J Mol Biol 427:491–510. doi:10.1016/j.jmb.2014.10.014

    Article  CAS  PubMed  Google Scholar 

  15. Sammond DW, Bosch DE, Butterfoss GL, Purbeck C, Machius M, Siderovski DP, Kuhlman B (2011) Computational design of the sequence and structure of a protein-binding peptide. J Am Chem Soc 133:4190–4192. doi:10.1021/ja110296z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fleishman SJ, Whitehead TA, Ekiert DC, Dreyfus C, Corn JE, Strauch E-M, Wilson IA, Baker D (2011) Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332:816–821. doi:10.1126/science.1202617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Procko E, Hedman R, Hamilton K, Seetharaman J, Fleishman SJ, Su M, Aramini J, Kornhaber G, Hunt JF, Tong L, Montelione GT, Baker D (2013) Computational design of a protein-based enzyme inhibitor. J Mol Biol 425:3563–3575. doi:10.1016/j.jmb.2013.06.035

    Article  CAS  PubMed  Google Scholar 

  18. Strauch E-M, Fleishman SJ, Baker D (2014) Computational design of a pH-sensitive IgG binding protein. Proc Natl Acad Sci U S A 111:675–680. doi:10.1073/pnas.1313605111

    Article  CAS  PubMed  Google Scholar 

  19. London N, Raveh B, Schueler-Furman O (2013) Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr Opin Struct Biol 23:894–902. doi:10.1016/j.sbi.2013.07.006

    Article  CAS  PubMed  Google Scholar 

  20. Parker AS, Griswold KE, Bailey-Kellogg C (2011) Optimization of combinatorial mutagenesis. J Comput Biol 18:1743–1756. doi:10.1089/cmb.2011.0152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pantazes RJ, Saraf MC, Maranas CD (2007) Optimal protein library design using recombination or point mutations based on sequence-based scoring functions. Protein Eng Des Sel 20:361–373. doi:10.1093/protein/gzm030

    Article  CAS  PubMed  Google Scholar 

  22. Chen MMY, Snow CD, Vizcarra CL, Mayo SL, Arnold FH (2012) Comparison of random mutagenesis and semi-rational designed libraries for improved cytochrome P450 BM3-catalyzed hydroxylation of small alkanes. Protein Eng Des Sel 25:171–178. doi:10.1093/protein/gzs004

    Article  CAS  PubMed  Google Scholar 

  23. Treynor TP, Vizcarra CL, Nedelcu D, Mayo SL (2007) Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function. Proc Natl Acad Sci 104:48–53. doi:10.1073/pnas.0609647103

    Article  CAS  PubMed  Google Scholar 

  24. Verma D, Grigoryan G, Bailey-Kellogg C (2015) Structure‐based design of combinatorial mutagenesis libraries. Protein Sci 24:895–908. doi:10.1002/pro.2642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Jacobs TM, Yumerefendi H, Kuhlman B, Leaver-Fay A (2015) SwiftLib: rapid degenerate-codon-library optimization through dynamic programming. Nucleic Acids Res 43:e34–e34. doi:10.1093/nar/gku1323

    Article  PubMed  Google Scholar 

  26. Mena MA, Daugherty PS (2005) Automated design of degenerate codon libraries. Protein Eng Des Sel 18:559–561. doi:10.1093/protein/gzi061

    Article  CAS  PubMed  Google Scholar 

  27. Allen BD, Nisthal A, Mayo SL (2010) Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles. Proc Natl Acad Sci 107:19838–19843. doi:10.1073/pnas.1012985107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hayes RJ, Bentzien J, Ary ML, Hwang MY, Jacinto JM, Vielmetter J, Kundu A, Dahiyat BI (2002) Combining computational and experimental screening for rapid optimization of protein properties. Proc Natl Acad Sci U S A 99:15926–15931. doi:10.1073/pnas.212627499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Guntas G, Purbeck C, Kuhlman B (2010) Engineering a protein-protein interface using a computationally designed library. Proc Natl Acad Sci U S A 107:19296–19301. doi:10.1073/pnas.1006528107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen TS, Palacios H, Keating AE (2013) Structure-based redesign of the binding specificity of anti-apoptotic Bcl-x(L). J Mol Biol 425:171–185. doi:10.1016/j.jmb.2012.11.009

    Article  CAS  PubMed  Google Scholar 

  31. Dutta S, Chen TS, Keating AE (2013) Peptide ligands for pro-survival protein Bfl-1 from computationally guided library screening. ACS Chem Biol 8:778–788. doi:10.1021/cb300679a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Foight GW, Keating AE (2015) Locating herpesvirus Bcl-2 homologs in the specificity landscape of anti-apoptotic Bcl-2 proteins. J Mol Biol. doi:10.1016/j.jmb.2015.05.015

    PubMed  PubMed Central  Google Scholar 

  33. Boersma M, Sadowsky J, Tomita Y (2008) Hydrophile scanning as a complement to alanine scanning for exploring and manipulating protein-protein recognition: application to the Bim BH3 domain. Protein Sci 17:1232–1240. doi:10.1110/ps.032896.107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ, Baker D, Fields S (2010) High-resolution mapping of protein sequence-function relationships. Nat Methods 7:741–746. doi:10.1038/nmeth.1492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fowler DM, Fields S (2014) Deep mutational scanning: a new style of protein science. Nat Methods 11:801–807. doi:10.1038/nmeth.3027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. London N, Gullá S, Keating AE, Schueler-Furman O (2012) In silico and in vitro elucidation of BH3 binding specificity toward Bcl-2. Biochemistry 51:5841–5850. doi:10.1021/bi3003567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. DeBartolo J, Dutta S, Reich L, Keating AE (2012) Predictive Bcl-2 family binding models rooted in experiment or structure. J Mol Biol 422:124–144. doi:10.1016/j.jmb.2012.05.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Araya CL, Fowler DM, Chen W, Muniez I, Kelly JW, Fields S (2012) A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function. Proc Natl Acad Sci U S A 109:16858–16863. doi:10.1073/pnas.1209751109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Fowler DM, Araya CL, Gerard W, Fields S (2011) Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27:3430–3431. doi:10.1093/bioinformatics/btr577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Starita LM, Young DL, Islam M, Kitzman JO, Gullingsrud J, Hause RJ, Fowler DM, Parvin JD, Shendure J, Fields S (2015) Massively parallel functional analysis of BRCA1 RING domain variants. Genetics. doi:10.1534/genetics.115.175802

    PubMed  PubMed Central  Google Scholar 

  41. Sirin S, Apgar JR, Bennett EM, Keating AE (2015) AB‐Bind: antibody binding mutational database for computational affinity predictions. Protein Sci. doi:10.1002/pro.2829

    PubMed  PubMed Central  Google Scholar 

  42. Stein A, Aloy P (2008) Contextual specificity in peptide-mediated protein interactions. PLoS One 3:e2524. doi:10.1371/journal.pone.0002524

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kingsford CL, Chazelle B, Singh M (2005) Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21:1028–1036. doi:10.1093/bioinformatics/bti144

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the National Institutes of General Medical Sciences award R01 GM110048 to A.E.K.

Author information

Authors and Affiliations

  1. Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA

    Glenna Wink Foight, T. Scott Chen, Daniel Richman & Amy E. Keating

  2. Department of Chemistry, University of Washington, Seattle, WA, 98195, USA

    Glenna Wink Foight

  3. Google Inc., Mountain View, CA, 94043, USA

    T. Scott Chen

  4. Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Bldg., 68-622, Cambridge, MA, 02139, USA

    Amy E. Keating

Authors
  1. Glenna Wink Foight
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  2. T. Scott Chen
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  3. Daniel Richman
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  4. Amy E. Keating
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Corresponding author

Correspondence to Amy E. Keating .

Editor information

Editors and Affiliations

  1. Microbiology and Molecular Genetics, Hebrew University Hadassah Medical School, Jerusalem, Israel

    Ora Schueler-Furman

  2. Organic Chemistry, The Weizmann Institute of Science, Rehovot, Israel

    Nir London

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Foight, G.W., Chen, T.S., Richman, D., Keating, A.E. (2017). Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design. In: Schueler-Furman, O., London, N. (eds) Modeling Peptide-Protein Interactions. Methods in Molecular Biology, vol 1561. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6798-8_13

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  • DOI: https://doi.org/10.1007/978-1-4939-6798-8_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6796-4

  • Online ISBN: 978-1-4939-6798-8

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