Predicting the tolerated sequences for proteins and protein interfaces using rosettabackrub flexible backbone design

PLoS ONE

Volumn 6, Issue 7, 2011, Pages

  Smith, Colin A ^a,b   Kortemme, Tanja ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GROWTH HORMONE RECEPTOR; HUMAN GROWTH HORMONE; BACTERIAL PROTEIN; IGG FC BINDING PROTEIN, STREPTOCOCCUS; IGG FC-BINDING PROTEIN, STREPTOCOCCUS; PEPTIDE; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; COMMUNICATION PROTOCOL; COMPUTER MODEL; COMPUTER PREDICTION; CONTROLLED STUDY; DATA ANALYSIS SOFTWARE; GENETIC ALGORITHM; INFORMATION STORAGE; MATHEMATICAL COMPUTING; MOLECULAR MODEL; MONTE CARLO METHOD; PROCESS OPTIMIZATION; PROTEIN CONFORMATION; PROTEIN INTERACTION; PROTEIN INTERFACE; PROTEIN STABILITY; PROTEIN STRUCTURE; QUALITY CONTROL; STRUCTURE ACTIVITY RELATION; THREE DIMENSIONAL IMAGING; ALGORITHM; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; HUMAN; METABOLISM; METHODOLOGY; MOLECULAR GENETICS; PDZ DOMAIN; PROTEIN BINDING; PROTEIN SECONDARY STRUCTURE; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID SEQUENCE; BACTERIAL PROTEINS; COMPUTATIONAL BIOLOGY; HUMAN GROWTH HORMONE; HUMANS; MOLECULAR SEQUENCE DATA; PDZ DOMAINS; PEPTIDES; PROTEIN BINDING; PROTEIN STABILITY; PROTEIN STRUCTURE, SECONDARY; PROTEINS; RECEPTORS, SOMATOTROPIN; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 79960609097     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0020451     Document Type: Article

References (50)

1. Friedland GD, Kortemme T, (2010) Designing ensembles in conformational and sequence space to characterize and engineer proteins. Curr Opin Struct Biol 20: 377-384.
2. Kortemme T, Joachimiak LA, Bullock AN, Schuler AD, Stoddard BL, et al. (2004) Computational redesign of protein-protein interaction specificity. Nat Struct Mol Biol 11: 371-379.
3. Pokala N, Handel TM, (2001) Review: protein design--where we were, where we are, where we're going. J Struct Biol 134: 269-281.
4. Frey KM, Georgiev I, Donald BR, Anderson AC, (2010) Predicting resistance mutations using protein design algorithms. Proc Natl Acad Sci U S A 107: 13707-13712.
5. Bloom JD, Arnold FH, (2009) In the light of directed evolution: pathways of adaptive protein evolution. Proc Natl Acad Sci U S A 106 (Suppl 1): 9995-10000.
6. 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 U S A 104: 48-53.
7. Marini NJ, Thomas PD, Rine J, (2010) The use of orthologous sequences to predict the impact of amino acid substitutions on protein function. PLoS Genet 6.
8. Distefano MD, Zhong A, Cochran AG, (2002) Quantifying beta-sheet stability by phage display. J Mol Biol 322: 179-188.
9. Kotz JD, Bond CJ, Cochran AG, (2004) Phage-display as a tool for quantifying protein stability determinants. Eur J Biochem 271: 1623-1629.
10. Fowler DM, Araya CL, Fleishman SJ, Kellogg EH, Stephany JJ, et al. (2010) High-resolution mapping of protein sequence-function relationships. Nat Methods 7: 741-746.
11. Smith GP, (1985) Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science 228: 1315-1317.
12. Fuh G, Pisabarro MT, Li Y, Quan C, Lasky LA, et al. (2000) Analysis of PDZ domain-ligand interactions using carboxyl-terminal phage display. J Biol Chem 275: 21486-21491.
13. Laura RP, Witt AS, Held HA, Gerstner R, Deshayes K, et al. (2002) The Erbin PDZ domain binds with high affinity and specificity to the carboxyl termini of delta-catenin and ARVCF. J Biol Chem 277: 12906-12914.
14. Pál G, Kouadio JL, Artis DR, Kossiakoff AA, Sidhu SS, (2006) Comprehensive and quantitative mapping of energy landscapes for protein-protein interactions by rapid combinatorial scanning. J Biol Chem 281: 22378-22385.
15. Tonikian R, Zhang Y, Sazinsky SL, Currell B, Yeh JH, et al. (2008) A specificity map for the PDZ domain family. PLoS Biol 6: e239.
16. Ernst A, Sazinsky SL, Hui S, Currell B, Dharsee M, et al. (2009) Rapid evolution of functional complexity in a domain family. Sci Signal 2: ra50.
17. Mandell DJ, Kortemme T, (2009) Backbone flexibility in computational protein design. Curr Opin Biotechnol 20: 420-428.
18. Desjarlais JR, Handel TM, (1999) Side-chain and backbone flexibility in protein core design. J Mol Biol 290: 305-318.
19. Harbury PB, Plecs JJ, Tidor B, Alber T, Kim PS, (1998) High-resolution protein design with backbone freedom. Science 282: 1462-1467.
20. 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.
21. Larson SM, England JL, Desjarlais JR, Pande VS, (2002) Thoroughly sampling sequence space: large-scale protein design of structural ensembles. Protein Sci 11: 2804-2813.
22. Ding F, Dokholyan NV, (2006) Emergence of protein fold families through rational design. PLoS Comput Biol 2: e85.
23. Humphris EL, Kortemme T, (2008) Prediction of protein-protein interface sequence diversity using flexible backbone computational protein design. Structure 16: 1777-1788.
24. Friedland GD, Lakomek NA, Griesinger C, Meiler J, Kortemme T, (2009) A correspondence between solution-state dynamics of an individual protein and the sequence and conformational diversity of its family. PLoS Comput Biol 5: e1000393.
25. Georgiev I, Donald BR, (2007) Dead-end elimination with backbone flexibility. Bioinformatics 23: 185-194.
26. Georgiev I, Lilien RH, Donald BR, (2008) The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles.J Comput Chem 29: 1527-1542.
27. Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, et al. (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302: 1364-1368.
28. Saunders CT, Baker D, (2005) Recapitulation of protein family divergence using flexible backbone protein design. J Mol Biol 346: 631-644.
29. Ambroggio XI, Kuhlman B, (2006) Design of protein conformational switches. Curr Opin Struct Biol 16: 525-530.
30. Hu X, Wang H, Ke H, Kuhlman B, (2007) High-resolution design of a protein loop. Proc Natl Acad Sci U S A 104: 17668-17673.
31. Smith CA, Kortemme T, (2008) Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. J Mol Biol 380: 742-756.
32. Friedland GD, Linares AJ, Smith CA, Kortemme T, (2008) A simple model of backbone flexibility improves modeling of side-chain conformational variability. J Mol Biol 380: 757-774.
33. Davis IW, Arendall WB, Richardson DC, Richardson JS, (2006) The backrub motion: how protein backbone shrugs when a sidechain dances. Structure 14: 265-274.
34. Georgiev I, Keedy D, Richardson JS, Richardson DC, Donald BR, (2008) Algorithm for backrub motions in protein design. Bioinformatics 24: i196-204.
35. Smith CA, Kortemme T, (2010) Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains. J Mol Biol 402: 460-474.
36. Schmidt HL, Sperling LJ, Gao YG, Wylie BJ, Boettcher JM, et al. (2007) Crystal polymorphism of protein GB1 examined by solid-state NMR spectroscopy and X-ray diffraction. J Phys Chem B 111: 14362-14369.
37. Clackson T, Ultsch MH, Wells JA, de Vos AM, (1998) Structural and functional analysis of the 1:1 growth hormone:receptor complex reveals the molecular basis for receptor affinity. J Mol Biol 277: 1111-1128.
38. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, et al. (2011) ROSETTA3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules. Methods in Enzymology 487: 545-574.
39. Dunbrack RL, (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12: 431-440.
40. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E, (1953) Equation of State Calculations by Fast Computing Machines. J Chem Phys 21: 1087-1092.
41. Kuhlman B, Baker D, (2000) Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci U S A 97: 10383-10388.
42. Leaver-Fay A, Kuhlman B, Snoeyink J, (2005) An adaptive dynamic programming algorithm for the side chain placement problem. Pac Symp Biocomput pp. 16-27.
43. Voigt CA, Gordon DB, Mayo SL, (2000) Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design. J Mol Biol 299: 789-803.
44. Ollikainen N, Sentovich E, Coelho C, Kuehlmann A, Kortemme T, (2009) SAT-based protein design. Proceedings of the 2009 IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2009): 128-35.
45. Rohl CA, Strauss CE, Misura KM, Baker D, (2004) Protein structure prediction using Rosetta. Methods Enzymol 383: 66-93.
46. R Development Core Team (2011) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0, http://www.r-project.org/.
47. Sauer-Eriksson AE, Kleywegt GJ, Uhlén M, Jones TA, (1995) Crystal structure of the C2 fragment of streptococcal protein G in complex with the Fc domain of human IgG. Structure 3: 265-278.
48. Havranek JJ, Harbury PB, (2003) Automated design of specificity in molecular recognition. Nat Struct Biol 10: 45-52.
49. Ambroggio XI, Kuhlman B, (2006) Computational design of a single amino acid sequence that can switch between two distinct protein folds. J Am Chem Soc 128: 1154-1161.
50. Humphris EL, Kortemme T, (2007) Design of multi-specificity in protein interfaces. PLoS Comput Biol 3: e164.