Refinement of protein structure homology models via long, all-atom molecular dynamics simulations

Proteins: Structure, Function and Bioinformatics

Volumn 80, Issue 8, 2012, Pages 2071-2079

  Raval, Alpan ^a   Piana, Stefano ^a   Eastwood, Michael P ^a   Dror, Ron O ^a   Shaw, David E ^a,b  

^a D E SHAW RESEARCH   (United States)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

CHARMM22; Force fields; Protein folding; Protein structure prediction; Sampling

Indexed keywords

CASPASE 8; CASPASE 9; FAST FOLDING PROTEIN; PROTEIN; UNCLASSIFIED DRUG;

ARTICLE; ATOM; CRYSTAL STRUCTURE; FORCE; MOLECULAR DYNAMICS; PRIORITY JOURNAL; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN STRUCTURE; STATIC ELECTRICITY;

COMPUTATIONAL BIOLOGY; MODELS, MOLECULAR; MOLECULAR DYNAMICS SIMULATION; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; STRUCTURAL HOMOLOGY, PROTEIN;

EID: 84863775602     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24098     Document Type: Article

References (53)

1. Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005; 33: W89-W93.
2. Zhang Y. Protein structure prediction: when is it useful? Curr Opin Struct Biol 2009; 19: 145-155.
3. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 2010; 5: 725-738.
4. Becker OM, Dhanoa DS, Marantz Y, Chen D, Shacham S, Cheruku S, Heifetz A, Mohanty P, Fichman M, Sharadendu A, Nudelman R, Kauffman M, Noiman S. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J Med Chem 2006; 49: 3116-3135.
5. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 2007; 152: 21-37.
6. Brylinski M, Skolnick J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci USA 2008; 105: 129-134.
7. Haupt VJ, Schroeder M. Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief Bioinform 2011; 12: 312-326.
8. Moult J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol 2005; 15: 285-289.
9. Misura KM, Baker D. Progress and challenges in high-resolution refinement of protein structure models. Proteins 2005; 59: 15-29.
10. Giorgetti A, Raimondo D, Miele AE, Tramontano A. Evaluating the usefulness of protein structure models for molecular replacement. Bioinformatics 2005; 21( Suppl 2): ii72-ii76.
11. Qian B, Raman S, Das R, Bradley P, Mc Coy AJ, Read RJ, Baker B. High-resolution structure prediction and the crystallographic phase problem. Nature 2007; 450: 259-264.
12. MacCallum JL, Hua L, Schnieders MJ, Pande VS, Jacobson MP, Dill KA. Assessment of the protein-structure refinement category in CASP8. Proteins 2009; 77( Suppl 9): 66-80.
13. MacCallum JL, Perez A, Schneiders MJ, Hua L, Jacobson MP, Dill KA. Assessment of protein structure refinement in CASP9. Proteins 2011; 79( Suppl S10): 74-90.
14. Kryshtafovych A, Venclovas C, Fidelis K, Moult J. Progress over the first decade of CASP experiments. Proteins Suppl 2005; 7: 225-236.
15. Zemla A, Venclovas C, Reinhardt A, Fidelis K, Hubbard TJ. Numerical criteria for the evaluation of ab initio predictions of protein structure. Proteins 1997; Suppl 1: 140-150.
16. Jagielska A, Wroblewska L, Skolnick J. Protein model refinement using an optimized physics-based all-atom force field. Proc Natl Acad Sci USA 2008; 105: 8268-8273.
17. Lin MS, Head-Gordon T. Reliable protein structure refinement using a physical energy function. J Comput Chem 2011; 32: 709-717.
18. Summa CM, Levitt M. Near-native structure refinement using in vacuo energy minimization. Proc Natl Acad Sci USA 2007; 104: 3177-3182.
19. Fan H, Mark AE. Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Sci 2004; 13: 211-220.
20. Chen J, Brooks CL, III. Can molecular dynamics simulations provide high-resolution refinement of protein structure? Proteins 2007; 67: 922-930.
21. Ishitani R, Terada T, Shimizu K. Refinement of comparative models of protein structure by using multicanonical molecular dynamics simulations. Mol Simul 2008; 34: 327-336.
22. Kannan S, Zacharias M. Application of biasing-potential replica-exchange simulations for loop modeling and refinement of proteins in explicit solvent. Proteins 2010; 78: 2809-2819.
23. Lu H, Skolnick J. Application of statistical potentials to protein structure refinement from low resolution ab initio models. Biopolymers 2003; 70: 575-584.
24. Chopra G, Kalisman N, Levitt M. Consistent refinement of submitted models at CASP using a knowledge-based potential. Proteins 2010; 78: 2668-2678.
25. Zhu J, Fan H, Periole X, Honig B, Mark AE. Refining homology models by combining replica-exchange molecular dynamics and statistical potentials. Proteins 2008; 72: 1171-1188.
26. Han R, Leo-Macias A, Zerbino D, Bastolla U, Contreras-Moreira B, Ortiz AR. An efficient conformational sampling method for homology modeling. Proteins 2007; 71: 175-188.
27. Kim DE, Blum B, Bradley P, Baker D. Sampling bottlenecks in de novo protein structure prediction. J Mol Biol 2009; 393: 249-260.
28. Lee MR, Tsai J, Baker D, Kollman PA. Molecular dynamics in the endgame of protein structure prediction. J Mol Biol 2001; 313: 417-430.
29. Chopra G, Summa CM, Levitt M. Solvent dramatically affects protein structure refinement. Proc Natl Acad Sci USA 2008; 105: 20239-20244.
30. Chen J, Brooks CL, III, Khandogin J. Recent advances in implicit solvent-based methods for biomolecular simulations. Curr Opin Struct Biol 2008; 18: 140-148.
31. Lindorff-Larsen K, Piana S, Dror RO, Shaw DE. How fast-folding proteins fold. Science 2011; 334: 517-520.
32. Lindorff-Larsen K, Maragakis P, Piana S, Eastwood MP, Dror RO, Shaw DE. Systematic validation of protein force fields against experimental data. PLoS ONE 2012; 7: e32131.
33. MacKerrel ADJr., Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 1998; 102: 3586-3616.
34. Piana S, Lindorff-Larsen K, Shaw DE. How robust are protein folding simulations with respect to force field parameterization? Biophys J 2011; 100: L47-L49.
35. Shaw DE, Dror RO, Salmon JK, Grossman JP, Mackenzie KM, Bank JA, Young C, Deneroff MM, Batson B, Bowers KJ, Chow E, Eastwood MP, Ierardi DJ, Klepeis JL, Kuskin JS, Larson RH, Lindorff-Larsen K, Maragakis P, Moraes MA, Piana S, Shan Y, Towles B. Millisecond-scale molecular dynamics simulations on Anton. Proceedings of the Conference on High Performance Computing, Networking, Storage and Analysis (SC09). New York: ACM; 2009.
36. Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W. Atomic-level characterization of the structural dynamics of proteins. Science 2010; 330: 341-346.
37. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006; 65: 712-725.
38. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010; 78: 1950-1958.
39. Best RB, Hummer G. Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides. J Phys Chem B 2009; 113: 9004-9015.
40. Horn HW, Swope WC, Pitera JW, Madura JD, Dick TJ, Hura GL, Head-Gordon T. Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 2004; 120: 9665-9678.
41. Beauchamp KA, Lin Y-S, Das R, Pande VS. Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements. J Chem Theory Comput 2012. DOI:10.1021/ct2007814.
42. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph 1996; 14: 33-38.
43. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983; 79: 926-935.
44. Fennell J, Gezelter JD. Is the Ewald summation still necessary? Pairwise alternatives to the accepted standard for long-range electrostatics. J Chem Phys 2006; 124: 234104.
45. Shan Y, Klepeis JL, Eastwood MP, Dror RO, Shaw DE. Gaussian split Ewald: a fast Ewald mesh method for molecular simulation. J Chem Phys 2005; 122: 054101.
46. Tuckerman M, Berne BJ, Martyna GJ. Reversible multiple time scale molecular dynamics. J Chem Phys 1992; 97: 1990-2001.
47. Bowers KJ. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06). New York: ACM; 2006.
48. Nosé S. A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys 1984; 81: 511.
49. Hoover WG. Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 1985; 31: 1695-1697.
50. Martyna GJ, Klein ML, Tuckerman M. Nosé-Hoover chains: the canonical ensemble via continuous dynamics. J Chem Phys 1992; 97: 2635-2643.
51. Tu T, Rendleman CA, Borhani DW, Dror RO, Gullingsrud J, Jensen Mø, Klepeis JL, Maragakis P, Miller P, Stafford KA, Shaw DE. A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories. Proceedings of the ACM/IEEE Conference on Supercomputing (SC08). New York: ACM; 2008.
52. Heinig M, Frishman D. STRIDE: a Web server for secondary structure assignment from known atomic coordinates of proteins. Nucl Acids Res 2004; 32: W500-502.
53. Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat 1996; 5: 299-314.