Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

PLoS Computational Biology

Volumn 11, Issue 10, 2015, Pages

  Brender, Jeffrey R ^a   Zhang, Yang ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMISTRY; COARSE-GRAINED MODELING; CORRELATION METHODS; DECISION TREES; FREE ENERGY; PROTEINS;

BINDING INTERACTION; CELLULAR PROCESS; EXPERIMENTAL DETERMINATION; INTERFACE PROFILES; INTERFACE STRUCTURES; PHYSIOLOGICAL FUNCTIONS; PROTEIN BINDING AFFINITY; PROTEIN-PROTEIN BINDING; PROTEIN-PROTEIN COMPLEXES; STRUCTURE-BASED;

BINDING ENERGY;

ARTICLE; BINDING AFFINITY; CALCULATION; CLASSIFIER; COMPLEX FORMATION; CONTROLLED STUDY; CORRELATION COEFFICIENT; MEASUREMENT ACCURACY; MOLECULAR DOCKING; MUTATIONAL ANALYSIS; PREDICTION; PROTEIN PROTEIN INTERACTION; RANDOM FOREST; SCORING SYSTEM; SENSITIVITY ANALYSIS; SEQUENCE ANALYSIS; STRUCTURE ANALYSIS; AMINO ACID SEQUENCE; BINDING SITE; BIOLOGICAL MODEL; CHEMICAL MODEL; CHEMISTRY; GENETICS; MOLECULAR GENETICS; MOLECULAR MODEL; MUTATION; PROCEDURES; PROTEIN ANALYSIS; PROTEIN CONFORMATION; STRUCTURE ACTIVITY RELATION; ULTRASTRUCTURE;

PROTEIN; PROTEIN BINDING;

AMINO ACID SEQUENCE; BINDING SITES; MODELS, CHEMICAL; MODELS, GENETIC; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; MUTATION; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN INTERACTION MAPPING; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84946044243     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004494     Document Type: Article

References (66)

1. Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nature genetics. 1999; 22(3): 231–8. 10391209
2. Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G, et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature. 2001; 409(6822): 928–33. 11237013
3. Kortemme T, Baker D, Computational design of protein-protein interactions. Current opinion in chemical biology. 2004; 8(1): 91–7. 15036162
4. Leavitt S, Freire E, Direct measurement of protein binding energetics by isothermal titration calorimetry. Current opinion in structural biology. 2001; 11(5): 560–6. 11785756
5. Kastritis PL, Bonvin AM, On the binding affinity of macromolecular interactions: daring to ask why proteins interact. Journal of the Royal Society, Interface / the Royal Society. 2013; 10(79): 20120835. doi: 10.1098/rsif.2012.0835 23235262
6. Robins WP, Faruque SM, Mekalanos JJ, Coupling mutagenesis and parallel deep sequencing to probe essential residues in a genome or gene. Proceedings of the National Academy of Sciences of the United States of America. 2013; 110(9): E848–E57. doi: 10.1073/pnas.1222538110 23401533
7. Whitehead TA, Chevalier A, Song Y, Dreyfus C, Fleishman SJ, De Mattos C, et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat Biotechnol. 2012; 30(6): 543–+. doi: 10.1038/nbt.2214 22634563
8. Li M, Petukh M, Alexov E, Panchenko AR, Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity. J Chem Theory Comput. 2014; 10(4): 1770–80. 24803870
9. Dehouck Y, Kwasigroch JM, Rooman M, Gilis D, BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations. Nucleic acids research. 2013; 41(Web Server issue): W333–9. doi: 10.1093/nar/gkt450 23723246
10. Humphris EL, Kortemme T, Prediction of Protein-Protein Interface Sequence Diversity Using Flexible Backbone Computational Protein Design. Structure. 2008; 16(12): 1777–88. doi: 10.1016/j.str.2008.09.012 19081054
11. Meroueh S, Liang SD, Li LW, Computational Design of Protein Interfaces with Receptor Flexibility. Biophysical journal. 2010; 98(3): 428a–a.
12. Beard H, Cholleti A, Pearlman D, Sherman W, Loving KA, Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes. PloS one. 2013; 8(12): e82849. doi: 10.1371/journal.pone.0082849 24340062
13. Clark LA, van Vlijmen HW, A knowledge-based forcefield for protein-protein interface design. Proteins. 2008; 70(4): 1540–50. 17910054
14. Moal IH, Fernandez-Recio J, Intermolecular Contact Potentials for Protein–Protein Interactions Extracted from Binding Free Energy Changes upon Mutation. Journal of Chemical Theory and Computation. 2013; 9(8): 3715–27.
15. Moal IH, Fernandez-Recio J, Comment on 'protein-protein binding affinity prediction from amino acid sequence'. Bioinformatics. 2015; 31(4): 614–5. doi: 10.1093/bioinformatics/btu682 25381451
16. Moal IH, Fernandez-Recio J, SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models. Bioinformatics. 2012; 28(20): 2600–7. doi: 10.1093/bioinformatics/bts489 22859501
17. Folkman L, Stantic B, Sattar A, Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants. BMC bioinformatics. 2013; 14 Suppl 2S6.
18. Saunders CT, Baker D, Evaluation of structural and evolutionary contributions to deleterious mutation prediction. Journal of molecular biology. 2002; 322(4): 891–901. 12270722
19. Berliner N, Teyra J, Colak R, Garcia Lopez S, Kim PM, Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. PloS one. 2014; 9(9): e107353. doi: 10.1371/journal.pone.0107353 25243403
20. Morrow JK, Zhang SX, Computational Prediction of Protein Hot Spot Residues. Current pharmaceutical design. 2012; 18(9): 1255–65. 22316154
21. Fleishman SJ, Khare SD, Koga N, Baker D, Restricted sidechain plasticity in the structures of native proteins and complexes. Protein Science. 2011; 20(4): 753–7. doi: 10.1002/pro.604 21432939
22. Meenan NAG, Sharma A, Fleishman SJ, MacDonald CJ, Morel B, Boetzel R, et al. The structural and energetic basis for high selectivity in a high-affinity protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107(22): 10080–5. doi: 10.1073/pnas.0910756107 20479265
23. Longo L, Lee J, Blaber M, Experimental support for the foldability-function tradeoff hypothesis: segregation of the folding nucleus and functional regions in fibroblast growth factor-1. Protein science: a publication of the Protein Society. 2012; 21(12): 1911–20.
24. Jacak R, Leaver-Fay A, Kuhlman B, Computational protein design with explicit consideration of surface hydrophobic patches. Proteins-Structure Function and Bioinformatics. 2012; 80(3): 825–38.
25. Revell LJ, Harmon LJ, Collar DC, Phylogenetic signal, evolutionary process, and rate. Syst Biol. 2008; 57(4): 591–601. doi: 10.1080/10635150802302427 18709597
26. Brown CA, Brown KS, Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My! PloS one. 2010; 5(6): doi: 10.1371/journal.pone.0010779 20531955
27. Keskin O, Tsai CJ, Wolfson H, Nussinov R, A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications. Protein science: a publication of the Protein Society. 2004; 13(4): 1043–55.
28. Mitra P, Shultis D, Zhang Y, EvoDesign: De novo protein design based on structural and evolutionary profiles. Nucleic acids research. 2013; 41(Web Server issue): W273–80. doi: 10.1093/nar/gkt384 23671331
29. Mitra P, Shultis D, Brender JR, Czajka J, Marsh D, Gray F, et al. An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis. PLoS Comput Biol. 2013; 9(10): e1003298. doi: 10.1371/journal.pcbi.1003298 24204234
30. Gribskov M, McLachlan AD, Eisenberg D, Profile analysis: detection of distantly related proteins. Proceedings of the National Academy of Sciences of the United States of America. 1987; 84(13): 4355–8. 3474607
31. Zhang Y, Skolnick J, Scoring function for automated assessment of protein structure template quality. Proteins. 2004; 57702–10.
32. Xu J, Zhang Y, How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics. 2010; 26(7): 889–95. doi: 10.1093/bioinformatics/btq066 20164152
33. Gao M, Skolnick J, iAlign: a method for the structural comparison of protein-protein interfaces. Bioinformatics. 2010; 26(18): 2259–65. doi: 10.1093/bioinformatics/btq404 20624782
34. Cheng S, Zhang Y, Brooks CL, PCalign: a method to quantify physicochemical similarity of protein-protein interfaces. BMC bioinformatics. 2015; 16(1): 33.
35. Ogmen U, Keskin O, Aytuna AS, Nussinov R, Gursoy A, PRISM: protein interactions by structural matching. Nucleic acids research. 2005; 33(Web Server issue): W331–6. 15991339
36. Shulman-Peleg A, Nussinov R, Wolfson HJ, SiteEngines: recognition and comparison of binding sites and protein-protein interfaces. Nucleic acids research. 2005; 33(Web Server issue): W337–41. 15980484
37. Engin HB, Keskin O, Nussinov R, Gursoy A, A strategy based on protein-protein interface motifs may help in identifying drug off-targets. Journal of chemical information and modeling. 2012; 52(8): 2273–86. doi: 10.1021/ci300072q 22817115
38. Cukuroglu E, Gursoy A, Nussinov R, Keskin O, Non-redundant unique interface structures as templates for modeling protein interactions. PloS one. 2014; 9(1): e86738. doi: 10.1371/journal.pone.0086738 24475173
39. Lim WA, Hodel A, Sauer RT, Richards FM, The crystal structure of a mutant protein with altered but improved hydrophobic core packing. Proceedings of the National Academy of Sciences of the United States of America. 1994; 91(1): 423–7. 8278404
40. Levy ED, A simple definition of structural regions in proteins and its use in analyzing interface evolution. Journal of molecular biology. 2010; 403(4): 660–70. doi: 10.1016/j.jmb.2010.09.028 20868694
41. Marsh JA, Buried and accessible surface area control intrinsic protein flexibility. Journal of molecular biology. 2013; 425(17): 3250–63. doi: 10.1016/j.jmb.2013.06.019 23811058
42. Schymkowitz JW, Rousseau F, Martins IC, Ferkinghoff-Borg J, Stricher F, Serrano L, Prediction of water and metal binding sites and their affinities by using the Fold-X force field. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102(29): 10147–52. 16006526
43. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L, The FoldX web server: an online force field. Nucleic acids research. 2005; 33(Web Server issue): W382–8. 15980494
44. Leaver-Fay A, O'Meara MJ, Tyka M, Jacak R, Song Y, Kellogg EH, et al. Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement. In: Keating AE, editor. Methods in Protein Design. Methods in Enzymology. 5232013. p. 109–43.
45. Ravikant DV, Elber R, PIE-efficient filters and coarse grained potentials for unbound protein-protein docking. Proteins. 2010; 78(2): 400–19. doi: 10.1002/prot.22550 19768784
46. Viswanath S, Ravikant DV, Elber R, Improving ranking of models for protein complexes with side chain modeling and atomic potentials. Proteins. 2013; 81(4): 592–606. doi: 10.1002/prot.24214 23180599
47. Liu S, Zhang C, Zhou H, Zhou Y, A physical reference state unifies the structure-derived potential of mean force for protein folding and binding. Proteins. 2004; 56(1): 93–101. 15162489
48. Rykunov D, Fiser A, New statistical potential for quality assessment of protein models and a survey of energy functions. BMC bioinformatics. 2010; 11128.
49. Lawrence MC, Colman PM, Shape complementarity at protein/protein interfaces. Journal of molecular biology. 1993; 234(4): 946–50. 8263940
50. Pires DE, Ascher DB, Blundell TL, mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics. 2014; 30(3): 335–42. doi: 10.1093/bioinformatics/btt691 24281696
51. Dourado DF, Flores SC, A multiscale approach to predicting affinity changes in protein-protein interfaces. Proteins. 2014; 82(10): 2681–90. doi: 10.1002/prot.24634 24975440
52. Talavera D, Robertson DL, Lovell SC, Characterization of protein-protein interaction interfaces from a single species. PloS one. 2011; 6(6): e21053. doi: 10.1371/journal.pone.0021053 21738603
53. Andreani J, Faure G, Guerois R, Versatility and invariance in the evolution of homologous heteromeric interfaces. PLoS computational biology. 2012; 8(8): e1002677. doi: 10.1371/journal.pcbi.1002677 22952442
54. Guharoy M, Chakrabarti P, Conservation and relative importance of residues across protein-protein interfaces. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102(43): 15447–52. 16221766
55. Doyle CM, Rumfeldt JA, Broom HR, Broom A, Stathopulos PB, Vassall KA, et al. Energetics of oligomeric protein folding and association. Arch Biochem Biophys. 2013; 531(1–2): 44–64. doi: 10.1016/j.abb.2012.12.005 23246784
56. Kastritis PL, Rodrigues JP, Folkers GE, Boelens R, Bonvin AM, Proteins feel more than they see: fine-tuning of binding affinity by properties of the non-interacting surface. Journal of molecular biology. 2014; 426(14): 2632–52. doi: 10.1016/j.jmb.2014.04.017 24768922
57. Stranges PB, Kuhlman B, A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Science. 2013; 22(1): 74–82. doi: 10.1002/pro.2187 23139141
58. Benedix A, Becker CM, de Groot BL, Caflisch A, Bockmann RA, Predicting free energy changes using structural ensembles. Nature Methods. 2009; 6(1): 3–4. doi: 10.1038/nmeth0109-3 19116609
59. Li MH, Petukh M, Alexov E, Panchenko AR, Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity. Journal of Chemical Theory and Computation. 2014; 10(4): 1770–80. 24803870
60. Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ, Baker D, et al. High-resolution mapping of protein sequence-function relationships. Nature Methods. 2010; 7(9): 741–U108. doi: 10.1038/nmeth.1492 20711194
61. Fromer M, Linial M, Exposing the co-adaptive potential of protein-protein interfaces through computational sequence design. Bioinformatics. 2010; 26(18): 2266–72. doi: 10.1093/bioinformatics/btq412 20679332
62. Gao M, Skolnick J, Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107(52): 22517–22. doi: 10.1073/pnas.1012820107 21149688
63. Guerois R, Nielsen JE, Serrano L, Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations. Journal of molecular biology. 2002; 320(2): 369–87. 12079393
64. Lewis SM, Kuhlman BA, Anchored Design of Protein-Protein Interfaces. PloS one. 2011; 6(6): doi: 10.1371/journal.pone.0020872 21698112
65. Liu S, Gao Y, Vakser IA, DOCKGROUND protein-protein docking decoy set. Bioinformatics. 2008; 24(22): 2634–5. doi: 10.1093/bioinformatics/btn497 18812365
66. Mukherjee S, Zhang Y, MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming. Nucleic acids research. 2009; 37(11): e83. doi: 10.1093/nar/gkp318 19443443