The scoring of poses in protein-protein docking: Current capabilities and future directions

BMC Bioinformatics

Volumn 14, Issue 1, 2013, Pages

  Moal, Iain H ^a   Torchala, Mieczyslaw ^b   Bates, Paul A ^b   Fernández Recio, Juan ^a  

^a INSTITUTE FOR RESEARCH IN BIOMEDICINE   (Spain)

^b IMPERIAL CANCER RESEARCH FUND   (United Kingdom)

Author keywords

Binding energy; Docking; Ranking; Scoring functions; SwarmDock

Indexed keywords

HIER-ARCHICAL CLUSTERING; PROTEIN-PROTEIN COMPLEXES; PROTEIN-PROTEIN DOCKING; RANKING; SCORING FUNCTIONS; SET-THEORETIC APPROACH; STRUCTURAL BIOINFORMATICS; SWARMDOCK;

BINDING ENERGY; BIOINFORMATICS; DOCKING; MOLECULAR DYNAMICS;

PROTEINS;

LIGAND; PROTEIN;

ARTICLE; BIOLOGY; CHEMISTRY; CLUSTER ANALYSIS; METABOLISM; METHODOLOGY; MOLECULAR DOCKING; PROTEIN BINDING;

CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEINS;

EID: 84884761289     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-286     Document Type: Article

References (179)

1. Schreiber G, Haran G, Zhou HX. Fundamental aspects of protein-protein association kinetics. Chem Rev 2009, 109(3):839-860. 10.1021/cr800373w, 2880639, 19196002.
2. Gabdoulline RR, Wade RC. Biomolecular diffusional association. Curr Opin Struct Biol 2002, 12(2):204-213. 10.1016/S0959-440X(02)00311-1, 11959498.
3. Elcock AH, Sept D, McCammon JA. Computer simulation of protein-protein interactions. J Phys Chem B 2001, 105(8):1504-1518.
4. Mereghetti P, Kokh D, McCammon JA, Wade RC. Diffusion and association processes in biological systems: theory, computation and experiment. BMC Biophys 2011, 4:2. 10.1186/2046-1682-4-2, 3093674, 21595997.
5. Moal IH, Bates PA. Kinetic rate constant prediction supports the conformational selection mechanism of protein binding. PLOS Comput Biol 2012, 8:e1002351. 10.1371/journal.pcbi.1002351, 3257286, 22253587.
6. Vreven T, Hwang H, Pierce BG, Weng Z. Prediction of protein-protein binding free energies. Protein Sci 2012, 21(3):396-404. 10.1002/pro.2027, 3375440, 22238219.
7. Moal IH, Agius R, Bates PA. Protein-protein binding affinity prediction on a diverse set of structures. Bioinformatics 2011, 27(21):3002-3009. 10.1093/bioinformatics/btr513, 21903632.
8. Tian F, Lv Y, Yang L. Structure-based prediction of protein-protein binding affinity with consideration of allosteric effect. Amino Acids 2012, 43(2):531-543. 10.1007/s00726-011-1101-1, 22089882.
9. Audie J, Swanson J. Advances in the prediction of protein-peptide binding affinities: implications for peptide-based drug discovery. Chem Biol Drug Des 2013, 81:50-60. 10.1111/cbdd.12076, 23066895.
10. Zhou P, Wang C, Tian F, Ren Y, Yang C, Huang J. Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity. J Comput Aided Mol Des 2013, 27:67-78. 10.1007/s10822-012-9625-3, 23306464.
11. Bhardwaj N, Abyzov A, Clarke D, Shou C, Gerstein MB. Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions. Protein Sci 2011, 20(10):1745-1754. 10.1002/pro.710, 3218368, 21826754.
12. Clarke D, Bhardwaj N, Gerstein MB. Novel insights through the integration of structural and functional genomics data with protein networks. J Struct Biol 2012, 179(3):320-326. 10.1016/j.jsb.2012.02.001, 22343087.
13. Tuncbag N, Kar G, Gursoy A, Keskin O, Nussinov R. Towards inferring time dimensionality in protein-protein interaction networks by integrating structures: the p53 example. Mol Biosyst 2009, 5(12):1770-1778. 10.1039/b905661k, 2898629, 19585003.
14. Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol 2012, 30(2):159-164. 10.1038/nbt.2106, 3708476, 22252508.
15. Zhong Q, Simonis N, Li QR, Charloteaux B, Heuze F, Klitgord N, Tam S, Yu H, Venkatesan K, Mou D, Swearingen V, Yildirim MA, Yan H, Dricot A, Szeto D, Lin C, Hao T, Fan C, Milstein S, Dupuy D, Brasseur R, Hill DE, Cusick ME, Vidal M. Edgetic perturbation models of human inherited disorders. Mol Syst Biol 2009, 5:321. 2795474, 19888216.
16. Moretti R, Fleishman SJ, Agius R, Torchala M, Bates PA, Kastritis PL, Rodrigues JP, Trellet M, Bonvin AM, Cui M, Rooman M, Gillis D, Dehouck Y, Moal I, Romero-Durana M, Perez-Cano L, Pallara C, Jimenez B, Fernandez-Recio J, Flores S, Pacella M, Praneeth Kilambi K, Gray JJ, Popov P, Grudinin S, Esquivel-Rodriguez J, Kihara D, Zhao N, Korkin D, Zhu X, et al. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins 2013, doi: 10.1002/prot.24356.
17. Kamisetty H, Ramanathan A, Bailey-Kellogg C, Langmead CJ. Accounting for conformational entropy in predicting binding free energies of protein-protein interactions. Proteins 2011, 79(2):444-462. 10.1002/prot.22894, 21120864.
18. Benedix A, Becker CM, de Groot BL, Caflisch A, Bockmann RA. Predicting free energy changes using structural ensembles. Nat Methods 2009, 6:3-4. 10.1038/nmeth0109-3, 19116609.
19. Brandsdal BO, Smalas AO. Evaluation of protein-protein association energies by free energy perturbation calculations. Protein Eng 2000, 13(4):239-245. 10.1093/protein/13.4.239, 10810154.
20. Almlof M, Aqvist J, Smalas AO, Brandsdal BO. Probing the effect of point mutations at protein-protein interfaces with free energy calculations. Biophys J 2006, 90(2):433-442. 10.1529/biophysj.105.073239, 1367050, 16272444.
21. Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 2002, 320(2):369-387. 10.1016/S0022-2836(02)00442-4, 12079393.
22. Kortemme T, Baker D. Computational design of protein-protein interactions. Curr Opin Chem Biol 2004, 8:91-97. 10.1016/j.cbpa.2003.12.008, 15036162.
23. Chen TS, Keating AE. Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods. Protein Sci 2012, 21(7):949-963. 10.1002/pro.2096, 3403433, 22593041.
24. Baker D. Prediction and design of macromolecular structures and interactions. Philos Trans R Soc Lond B Biol Sci 2006, 361(1467):459-463. 10.1098/rstb.2005.1803, 1609347, 16524834.
25. Lippow SM, Tidor B. Progress in computational protein design. Curr Opin Biotechnol 2007, 18(4):305-311. 10.1016/j.copbio.2007.04.009, 3495006, 17644370.
26. Fleishman SJ, Whitehead TA, Strauch EM, Corn JE, Qin S, Zhou HX, Mitchell JC, Demerdash ON, Takeda-Shitaka M, Terashi G, Moal IH, Li X, Bates PA, Zacharias M, Park H, Ko JS, Lee H, Seok C, Bourquard T, Bernauer J, Poupon A, Aze J, Soner S, Ovali SK, Ozbek P, Tal NB, Haliloglu T, Hwang H, Vreven T, Pierce BG, et al. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J Mol Biol 2011, 414(2):289-302. 10.1016/j.jmb.2011.09.031, 22001016.
27. Mandell DJ, Kortemme T. Computer-aided design of functional protein interactions. Nat Chem Biol 2009, 5(11):797-807. 10.1038/nchembio.251, 19841629.
28. Ahmad JN, Li J, Biedermannova L, Kuchar M, Sipova H, Semeradtova A, Cerny J, Petrokova H, Mikulecky P, Polinek J, Stanek O, Vondrasek J, Homola J, Maly J, Osicka R, Sebo P, Maly P. Novel high-affinity binders of human interferon gamma derived from albumin-binding domain of protein G. Proteins 2012, 80(3):774-789. 10.1002/prot.23234, 22113774.
29. Sammond DW, Eletr ZM, Purbeck C, Kimple RJ, Siderovski DP, Kuhlman B. Structure-based protocol for identifying mutations that enhance protein-protein binding affinities. J Mol Biol 2007, 371(5):1392-1404. 10.1016/j.jmb.2007.05.096, 2682327, 17603074.
30. Domene C, Illingworth CJ. Effects of point mutations in pVHL on the binding of HIF-1α. Proteins 2012, 80(3):733-746. 10.1002/prot.23230, 22105711.
31. Metz A, Ciglia E, Gohlke H. Modulating protein-protein interactions: from structural determinants of binding to druggability prediction to application. Curr Pharm Des 2012, 18(30):4630-4647. 10.2174/138161212802651553, 22650257.
32. Gonzalez-Ruiz D, Gohlke H. Targeting protein-protein interactions with small molecules: challenges and perspectives for computational binding epitope detection and ligand finding. Curr Med Chem 2006, 13(22):2607-2625. 10.2174/092986706778201530, 17017914.
33. Nisius B, Sha F, Gohlke H. Structure-based computational analysis of protein binding sites for function and druggability prediction. J Biotechnol 2012, 159(3):123-134. 10.1016/j.jbiotec.2011.12.005, 22197384.
34. Zhou P, Wang C, Ren Y, Yang C, Tian F. Computational peptidology: a New and promising approach to therapeutic peptide design. Curr Med Chem 2013, 20(15):1985-1996. 10.2174/0929867311320150005, 23317161.
35. Szymkowski DE. Creating the next generation of protein therapeutics through rational drug design. Curr Opin Drug Discov Devel 2005, 8(5):590-600.
36. Hwang I, Park S. Computational design of protein therapeutics. Drug Discov Today Technol 2008, 5(2-3):e43-e48.
37. Wanner J, Fry DC, Peng Z, Roberts J. Druggability assessment of protein-protein interfaces. Future Med Chem 2011, 3(16):2021-2038. 10.4155/fmc.11.156, 22098351.
38. Kiel C, Serrano L. Structural data in synthetic biology approaches for studying general design principles of cellular signaling networks. Structure 2012, 20(11):1806-1813. 10.1016/j.str.2012.10.002, 23141693.
39. Aloy P, Russell RB. Structural systems biology: modelling protein interactions. Nat Rev Mol Cell Biol 2006, 7(3):188-197. 10.1038/nrm1859, 16496021.
40. Kiel C, Beltrao P, Serrano L. Analyzing protein interaction networks using structural information. Annu Rev Biochem 2008, 77:415-441. 10.1146/annurev.biochem.77.062706.133317, 18304007.
41. Stein A, Mosca R, Aloy P. Three-dimensional modeling of protein interactions and complexes is going 'omics. Curr Opin Struct Biol 2011, 21(2):200-208. 10.1016/j.sbi.2011.01.005, 21320770.
42. Beltrao P, Kiel C, Serrano L. Structures in systems biology. Curr Opin Struct Biol 2007, 17(3):378-384. 10.1016/j.sbi.2007.05.005, 17574836.
43. Stein M, Gabdoulline RR, Wade RC. Bridging from molecular simulation to biochemical networks. Curr Opin Struct Biol 2007, 17(2):166-172. 10.1016/j.sbi.2007.03.014, 17395455.
44. Dell'Orco D. Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology. Mol Biosyst 2009, 5(4):323-334. 10.1039/b821580d, 19396368.
45. Melquiond ASJ, Karaca E, Kastritis PL, Bonvin AMJJ. Next challenges in protein-protein docking: from proteome to interactome and beyond. WIREs Comput Mol Sci 2012, 2(4):642-651.
46. Zhou P, Tian F, Shang Z. 2D depiction of nonbonding interactions for protein complexes. J Comput Chem 2009, 30(6):940-951. 10.1002/jcc.21109, 18942722.
47. Zhou P, Shang Z. 2D molecular graphics: a flattened world of chemistry and biology. Brief. Bioinformatics 2009, 10(3):247-258.
48. Kastritis PL, Moal IH, Hwang H, Weng Z, Bates PA, Bonvin AM, Janin J. A structure-based benchmark for protein-protein binding affinity. Protein Sci 2011, 20(3):482-491. 10.1002/pro.580, 3064828, 21213247.
49. 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):2600-2607. 10.1093/bioinformatics/bts489, 22859501.
50. Moal IH, Moretti R, Baker D, Fernandez-Recio J. Scoring functions for protein-protein interactions. Curr Opin Struct Biol 2013, doi: 10.1016/j.sbi.2013.06.017.
51. Kastritis PL, Bonvin AM. Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. J Proteome Res 2010, 9(5):2216-2225. 10.1021/pr9009854, 20329755.
52. Pallara C, Jimenez-Garcia B, Perez-Cano L, Romero M, Solernou A, Grosdidier S, Pons C, Moal IH, Fernandez-Recio J. Expanding the frontiers of protein-protein modelling: From docking and scoring to binding affinity predictions and other challenges. Proteins 2013, doi: 10.1002/prot.24387.
53. Demir-Kavuk O, Krull F, Chae MH, Knapp EW. Predicting protein complex geometries with linear scoring functions. Genome Inform 2010, 24:21-30.
54. Cheng TM, Blundell TL, Fernandez-Recio J. pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking. Proteins 2007, 68(2):503-515. 10.1002/prot.21419, 17444519.
55. Lyskov S, Gray JJ. The RosettaDock server for local protein-protein docking. Nucleic Acids Res 2008, 36(Web Server issue):W233-238. 2447798, 18442991.
56. Dominguez C, Boelens R, Bonvin AM. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 2003, 125(7):1731-1737. 10.1021/ja026939x, 12580598.
57. Pierce B, Weng Z. ZRANK: reranking protein docking predictions with an optimized energy function. Proteins 2007, 67(4):1078-1086. 10.1002/prot.21373, 17373710.
58. Pierce B, Weng Z. A combination of rescoring and refinement significantly improves protein docking performance. Proteins 2008, 72:270-279. 10.1002/prot.21920, 2696687, 18214977.
59. Andrusier N, Nussinov R, Wolfson HJ. FireDock: fast interaction refinement in molecular docking. Proteins 2007, 69:139-159. 10.1002/prot.21495, 17598144.
60. Mashiach E, Nussinov R, Wolfson HJ. FiberDock: Flexible induced-fit backbone refinement in molecular docking. Proteins 2010, 78(6):1503-1519.
61. Zacharias M. Protein-protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci 2003, 12(6):1271-1282. 10.1110/ps.0239303, 2323887, 12761398.
62. Tovchigrechko A, Vakser IA. Development and testing of an automated approach to protein docking. Proteins 2005, 60(2):296-301. 10.1002/prot.20573, 15981259.
63. Liang S, Liu S, Zhang C, Zhou Y. A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys. Proteins 2007, 69(2):244-253. 10.1002/prot.21498, 2673351, 17623864.
64. Murphy J, Gatchell DW, Prasad JC, Vajda S. Combination of scoring functions improves discrimination in protein-protein docking. Proteins 2003, 53(4):840-854. 10.1002/prot.10473, 14635126.
65. Mitra P, Pal D. Using correlated parameters for improved ranking of protein-protein docking decoys. J Comput Chem 2011, 32(5):787-796. 10.1002/jcc.21657, 20941737.
66. Huang SY, Zou X. An iterative knowledge-based scoring function for protein-protein recognition. Proteins 2008, 72(2):557-579. 10.1002/prot.21949, 18247354.
67. Pons C, Talavera D, de la Cruz X, Orozco M, Fernandez-Recio J. Scoring by intermolecular pairwise propensities of exposed residues (SIPPER): a new efficient potential for protein-protein docking. J Chem Inf Model 2011, 51(2):370-377. 10.1021/ci100353e, 21214199.
68. Moont G, Gabb HA, Sternberg MJ. Use of pair potentials across protein interfaces in screening predicted docked complexes. Proteins 1999, 35(3):364-373. 10.1002/(SICI)1097-0134(19990515)35:3<364::AID-PROT11>3.0.CO;2-4, 10328272.
69. Feliu E, Aloy P, Oliva B. On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking. Protein Sci 2011, 20(3):529-541. 10.1002/pro.585, 3064832, 21432933.
70. Geppert T, Proschak E, Schneider G. Protein-protein docking by shape-complementarity and property matching. J Comput Chem 2010, 31(9):1919-1928.
71. Muller W, Sticht H. A protein-specifically adapted scoring function for the reranking of docking solutions. Proteins 2007, 67:98-111. 10.1002/prot.21310, 17243180.
72. Liu S, Vakser IA. DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking. BMC Bioinformatics 2011, 12:280. 10.1186/1471-2105-12-280, 3145612, 21745398.
73. Chuang GY, Kozakov D, Brenke R, Comeau SR, Vajda S. DARS (Decoys As the Reference State) potentials for protein-protein docking. Biophys J 2008, 95(9):4217-4227. 10.1529/biophysj.108.135814, 2567923, 18676649.
74. Tobi D. Designing coarse grained-and atom based-potentials for protein-protein docking. BMC Struct Biol 2010, 10:40. 10.1186/1472-6807-10-40, 2996388, 21078143.
75. Tobi D, Bahar I. Optimal design of protein docking potentials: efficiency and limitations. Proteins 2006, 62(4):970-981.
76. Moal IH, Fernandez-Recio J. Intermolecular contact potentials for protein-protein interactions extracted from binding free energy changes upon mutation. J Chem Theory Comput 2013, 9(8):3715-3727.
77. Chang S, Jiao X, Li CH, Gong XQ, Chen WZ, Wang CX. Amino acid network and its scoring application in protein-protein docking. Biophys Chem 2008, 134(3):111-118. 10.1016/j.bpc.2007.12.005, 18329160.
78. Khashan R, Zheng W, Tropsha A. Scoring protein interaction decoys using exposed residues (SPIDER): a novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues. Proteins 2012, 80(9):2207-2217. 10.1002/prot.24110, 3409293, 22581643.
79. Mitra P, Pal D. New measures for estimating surface complementarity and packing at protein-protein interfaces. FEBS Lett 2010, 584(6):1163-1168. 10.1016/j.febslet.2010.02.021, 20153323.
80. Pons C, Glaser F, Fernandez-Recio J. Prediction of protein-binding areas by small-world residue networks and application to docking. BMC Bioinformatics 2011, 12:378. 10.1186/1471-2105-12-378, 3189935, 21943333.
81. Duhovny D, Nussinov R, Wolfson H. Efficient Unbound Docking of Rigid Molecules. Lecture Notes in Computer Science, Volume 2452: Algorithms in Bioinformatics 2002, 185-200. Berlin, Heidelberg: Springer, Guigó R, Gusfield D.
82. Shentu Z, Al Hasan M, Bystroff C, Zaki MJ. Context shapes: Efficient complementary shape matching for protein-protein docking. Proteins 2008, 70(3):1056-1073.
83. Chen R, Li L, Weng Z. ZDOCK: an initial-stage protein-docking algorithm. Proteins 2003, 52:80-87. 10.1002/prot.10389, 12784371.
84. Axenopoulos A, Daras P, Papadopoulos GE, Houstis EN. SP-dock: protein-protein docking using shape and physicochemical complementarity. IEEE/ACM Trans Comput Biol Bioinform 2013, 10:135-150.
85. Gu S, Koehl P, Hass J, Amenta N. Surface-histogram: a new shape descriptor for protein-protein docking. Proteins 2012, 80:221-238. 10.1002/prot.23192, 3240741, 22072544.
86. Venkatraman V, Yang YD, Sael L, Kihara D. Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinformatics 2009, 10:407. 10.1186/1471-2105-10-407, 2800122, 20003235.
87. Ritchie DW, Kemp GJ. Protein docking using spherical polar Fourier correlations. Proteins 2000, 39(2):178-194. 10.1002/(SICI)1097-0134(20000501)39:2<178::AID-PROT8>3.0.CO;2-6, 10737939.
88. Gabb HA, Jackson RM, Sternberg MJ. Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 1997, 272:106-120. 10.1006/jmbi.1997.1203, 9299341.
89. Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem AA, Aflalo C, Vakser IA. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci USA 1992, 89(6):2195-2199. 10.1073/pnas.89.6.2195, 48623, 1549581.
90. Mandell JG, Roberts VA, Pique ME, Kotlovyi V, Mitchell JC, Nelson E, Tsigelny I, Ten Eyck LF. Protein docking using continuum electrostatics and geometric fit. Protein Eng 2001, 14(2):105-113. 10.1093/protein/14.2.105, 11297668.
91. Chae MH, Krull F, Lorenzen S, Knapp EW. Predicting protein complex geometries with a neural network. Proteins 2010, 78(4):1026-1039. 10.1002/prot.22626, 19938153.
92. Palma PN, Krippahl L, Wampler JE, Moura JJ. BiGGER: a new (soft) docking algorithm for predicting protein interactions. Proteins 2000, 39(4):372-384. 10.1002/(SICI)1097-0134(20000601)39:4<372::AID-PROT100>3.0.CO;2-Q, 10813819.
93. Aze J, Bourquard T, Hamel S, Poupon A, Ritchie DW. Using Kendall-τ Meta-Bagging to Improve Protein-Protein Docking Predictions. Lecture Notes in Computer Science, Volume 7036: Pattern Recognition in Bioinformatics 2011, 284-295. Berlin, Heidelberg: Springer, Loog M, Wessels L, Reinders M, Ridder D.
94. Bourquard T, Bernauer J, Aze J, Poupon A. A collaborative filtering approach for protein-protein docking scoring functions. PLoS ONE 2011, 6(4):e18541. 10.1371/journal.pone.0018541, 3081294, 21526112.
95. Bernauer J, Aze J, Janin J, Poupon A. A new protein-protein docking scoring function based on interface residue properties. Bioinformatics 2007, 23(5):555-562. 10.1093/bioinformatics/btl654, 17237048.
96. Fink F, Hochrein J, Wolowski V, Merkl R, Gronwald W. PROCOS: computational analysis of protein-protein complexes. J Comput Chem 2011, 32(12):2575-2586. 10.1002/jcc.21837, 21630291.
97. Bordner AJ, Gorin AA. Protein docking using surface matching and supervised machine learning. Proteins 2007, 68(2):488-502. 10.1002/prot.21406, 17444516.
98. Kozakov D, Schueler-Furman O, Vajda S. Discrimination of near-native structures in protein-protein docking by testing the stability of local minima. Proteins 2008, 72(3):993-1004. 10.1002/prot.21997, 2823634, 18300245.
99. Torchala M, Moal IH, Chaleil RA, Agius R, Bates PA. A Markov-chain model description of binding funnels to enhance the ranking of docked solutions. Proteins 2013, doi: 10.1002/prot.24369.
100. London N, Schueler-Furman O. FunHunt: model selection based on energy landscape characteristics. Biochem Soc Trans 2008, 36:1418-1421. 10.1042/BST0361418, 19021567.
101. London N, Schueler-Furman O. Funnel hunting in a rough terrain: learning and discriminating native energy funnels. Structure 2008, 16(2):269-279. 10.1016/j.str.2007.11.013, 18275818.
102. London N, Schueler-Furman O. Assessing the energy landscape of CAPRI targets by FunHunt. Proteins 2007, 69(4):809-815. 10.1002/prot.21736, 17803233.
103. Ravikumar KM, Huang W, Yang S. Coarse-grained simulations of protein-protein association: an energy landscape perspective. Biophys J 2012, 103(4):837-845. 10.1016/j.bpj.2012.07.013, 3443792, 22947945.
104. Zheng W, Schafer NP, Davtyan A, Papoian GA, Wolynes PG. Predictive energy landscapes for protein-protein association. Proc Natl Acad Sci USA 2012, 109(47):19244-19249. 10.1073/pnas.1216215109, 3511104, 23129648.
105. 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, 56:93-101. 10.1002/prot.20019, 15162489.
106. Hwang H, Vreven T, Janin J, Weng Z. Protein-protein docking benchmark version 4.0. Proteins 2010, 78(15):3111-3114. 10.1002/prot.22830, 2958056, 20806234.
107. Moal IH, Bates PA. SwarmDock and the Use of normal modes in protein-protein docking. Int J Mol Sci 2010, 11(10):3623-3648. 10.3390/ijms11103623, 2996808, 21152290.
108. Li X, Moal IH, Bates PA. Detection and refinement of encounter complexes for protein-protein docking: taking account of macromolecular crowding. Proteins 2010, 78(15):3189-3196. 10.1002/prot.22770, 20552581.
109. Torchala M, Moal IH, Chaleil RA, Fernandez-Recio J, Bates PA. SwarmDock: a server for flexible protein-protein docking. Bioinformatics 2013, 29(6):807-809. 10.1093/bioinformatics/btt038, 23343604.
110. Viswanath S, Ravikant DV, Elber R. Improving ranking of models for protein complexes with side chain modeling and atomic potentials. Proteins 2012, 81(4):592-606.
111. Lu H, Lu L, Skolnick J. Development of unified statistical potentials describing protein-protein interactions. Biophys J 2003, 84(3):1895-1901. 10.1016/S0006-3495(03)74997-2, 1302758, 12609891.
112. Chaudhury S, Lyskov S, Gray JJ. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 2010, 26(5):689-691. 10.1093/bioinformatics/btq007, 2828115, 20061306.
113. Lu M, Dousis AD, Ma J. OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. J Mol Biol 2008, 376:288-301. 10.1016/j.jmb.2007.11.033, 2669442, 18177896.
114. Miyazawa S, Jernigan RL. Self-consistent estimation of inter-residue protein contact energies based on an equilibrium mixture approximation of residues. Proteins 1999, 34:49-68. 10.1002/(SICI)1097-0134(19990101)34:1<49::AID-PROT5>3.0.CO;2-L, 10336383.
115. Camacho CJ, Vajda S. Protein docking along smooth association pathways. Proc Natl Acad Sci USA 2001, 98(19):10636-10641. 10.1073/pnas.181147798, 58518, 11517309.
116. Bornot A, Etchebest C, de Brevern AG. Predicting protein flexibility through the prediction of local structures. Proteins 2011, 79(3):839-852. 10.1002/prot.22922, 3317885, 21287616.
117. Pandey BP, Zhang C, Yuan X, Zi J, Zhou Y. Protein flexibility prediction by an all-atom mean-field statistical theory. Protein Sci 2005, 14(7):1772-1777. 10.1110/ps.041311005, 2253361, 15987905.
118. Gunasekaran K, Nussinov R. How different are structurally flexible and rigid binding sites? Sequence and structural features discriminating proteins that do and do not undergo conformational change upon ligand binding. J Mol Biol 2007, 365:257-273. 10.1016/j.jmb.2006.09.062, 17059826.
119. Dobbins SE, Lesk VI, Sternberg MJ. Insights into protein flexibility: The relationship between normal modes and conformational change upon protein-protein docking. Proc Natl Acad Sci USA 2008, 105(30):10390-10395. 10.1073/pnas.0802496105, 2475499, 18641126.
120. Karaca E, Bonvin AM. A multidomain flexible docking approach to deal with large conformational changes in the modeling of biomolecular complexes. Structure 2011, 19(4):555-565. 10.1016/j.str.2011.01.014, 21481778.
121. Fernandez-Recio J, Totrov M, Skorodumov C, Abagyan R. Optimal docking area: a new method for predicting protein-protein interaction sites. Proteins 2005, 58:134-143.
122. Arafat Y, Kamruzzaman J, Karmakar GC, Fernandez-Recio J. Predicting protein-protein interfaces as clusters of optimal docking area points. Int J Data Min Bioinform 2009, 3:55-67. 10.1504/IJDMB.2009.023884, 19432376.
123. Feng Y, Kloczkowski A, Jernigan RL. Potentials 'R' Us web-server for protein energy estimations with coarse-grained knowledge-based potentials. BMC Bioinformatics 2010, 11:92. 10.1186/1471-2105-11-92, 3098114, 20163737.
124. Vajda S, Hall DR, Kozakov D. Sampling and scoring: A marriage made in heaven. Proteins 2013, doi: 10.1002/prot.24343.
125. Cossock D, Zhang T. Subset ranking using regression. Proceedings of the 19th annual conference on Learning Theory, COLT'06: 22-25 June 2006; Pittsburgh 2006, 605-619. Berlin, Heidelberg: Springer, Simons HU, Lugosi G.
126. Li P, Burges C, Wu Q, Platt JC, Koller D, Singer Y, Roweis S. McRank: Learning to Rank Using Multiple Classification and Gradient Boosting. Proceedings of the 21st Annual Conference on Neural Information Processing Systems: 3-6 December 2007; Vancouver 2008, 1176-1183. New York: Curran Associates Inc, Platt JC, Koller D, Singer Y, Roweis ST.
127. Crammer K, Singer Y. Pranking with Ranking. Advances in Neural Information Processing Systems 14 2001, 641-647. Cambridge: MIT Press, Dietterich TG, Becker S, Thrun S, Obermayer K.
128. Shashua A, Levin A. Ranking with large margin principle: Two approaches. Advances in Neural Information Processing Systems 15 2003, 937-944. Cambridge: MIT Press, Becker S, Thrun S, Obermayer K.
129. Herbrich R, Graepel T, Obermayer K. Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers 2000, 115-132. Cambridge: MIT Press, Smola AJ, Bartlett PL, Scholkopf B, Schuurmans D.
130. Freund Y, Iyer R, Schapire RE, Singer Y. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 2003, 4:933-969.
131. Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G. Learning to rank using gradient descent. Proceedings of the 22nd international conference on Machine learning, ICML '05: 7-11 August 2005; Bonn 2005, 89-96. New York: ACM, De Raedt L, Wrobel S.
132. Zheng Z, Zha H, Zhang T, Chapelle O, Chen K, Sun G. A General Boosting Method and its Application to Learning Ranking Functions for Web Search Neur. Proceedings of the 21st Annual Conference on Neural Information Processing Systems: 3-6 December 2007; Vancouver 2008, 1697-1704. New York: Curran Associates Inc, Platt JC, Koller D, Singer Y, Roweis ST.
133. Cao Y, Xu J, Yan Liu T, Li H, Huang Y, Wuen Hon H. Adapting ranking SVM to document retrieval. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '06: 6-10 August 2007; Seattle 2006, 186-193. New York: ACM, Efthimiadis EN, Dumais ST, Hawking D, Järvelin K.
134. Burges CJC, Ragno R, Le QV. Learning to Rank with Nonsmooth Cost Functions. Proceedings of the 20th Annual Conference on Neural Information Processing Systems: 4-7 December 2006; Vancouver 2006, 193-200. New York: Curran Associates Inc, Schölkopf B, Platt JC, Hoffman T.
135. Wu Q, Burges CJ, Svore KM, Gao J. Adapting boosting for information retrieval measures. Inf. Retr. 2010, 13(3):254-270.
136. Li H. A short introduction to learning to rank. IEICE Transactions 2011, 94-D(10):1854-1862.
137. Cao Z, Qin T, Liu TY, Tsai MF, Li H. Learning to rank: from pairwise approach to listwise approach. Proceedings of the 24th international conference on Machine learning, ICML '07: 20-24 June 2007; Corvalis 2007, 129-136. New York: ACM, Zoubin G.
138. Xia F, Liu TY, Wang J, Zhang W, Li H. Listwise approach to learning to rank: theory and algorithm. Proceedings of the 25th international conference on Machine learning, ICML '08: 5-9 July; Helsinki 2008, 1192-1199. New York: ACM, Cohen W.
139. Xu J, Li H. AdaRank: a boosting algorithm for information retrieval. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07: 23-27 July; Amsterdam 2007, 391-398. New York: ACM, Wessel K, de Vries AP.
140. Yue Y, Finley T, Radlinski F, Joachims T. A support vector method for optimizing average precision. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07: 23-27 July; Amsterdam 2007, 271-278. New York: ACM, Wessel K, de Vries AP.
141. Taylor M, Guiver J, Robertson S, Minka T. SoftRank: optimizing non-smooth rank metrics. Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM '08: 11-12 February; Palo Alto 2008, 77-86. New York: ACM, Najork M.
142. Xu J, Yan Liu T, Lu M, Li H, Ying Ma W. Directly optimizing evaluation measures in learning to rank. Proceedings of the 31th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08: 20-24 July; Singapore 2008, 107-114. New York: ACM, Chua TS, Leong M.
143. Chapelle O, Chang Y, Liu TY. Future directions in learning to rank. J Mach Learn Res 2011, 14:91-100.
144. Janin J. Assessing predictions of protein-protein interaction: the CAPRI experiment. Protein Sci 2005, 14(2):278-283. 10.1110/ps.041081905, 2253420, 15659362.
145. Lensink MF, Mendez R, Wodak SJ. Docking and scoring protein complexes: CAPRI 3rd Edition. Proteins 2007, 69(4):704-718. 10.1002/prot.21804, 17918726.
146. Rajgaria R, McAllister SR, Floudas CA. A novel high resolution Cα-Cα distance dependent force field based on a high quality decoy set. Proteins 2006, 65(3):726-741. 10.1002/prot.21149, 16981202.
147. Rajgaria R, McAllister SR, Floudas CA. Distance dependent centroid to centroid force fields using high resolution decoys. Proteins 2008, 70(3):950-970.
148. Bastolla U, Farwer J, Knapp EW, Vendruscolo M. How to guarantee optimal stability for most representative structures in the Protein Data Bank. Proteins 2001, 44(2):79-96. 10.1002/prot.1075, 11391771.
149. Pokarowski P, Kloczkowski A, Jernigan RL, Kothari NS, Pokarowska M, Kolinski A. Inferring ideal amino acid interaction forms from statistical protein contact potentials. Proteins 2005, 59:49-57. 10.1002/prot.20380, 15688450.
150. Bryant SH, Lawrence CE. An empirical energy function for threading protein sequence through the folding motif. Proteins 1993, 16:92-112. 10.1002/prot.340160110, 8497488.
151. Betancourt MR, Thirumalai D. Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 1999, 8(2):361-369. 2144252, 10048329.
152. Godzik A, Kolinski A, Skolnick J. Are proteins ideal mixtures of amino acids? Analysis of energy parameter sets. Protein Sci 1995, 4(10):2107-2117. 10.1002/pro.5560041016, 2142984, 8535247.
153. Park B, Levitt M. Energy functions that discriminate X-ray and near native folds from well-constructed decoys. J Mol Biol 1996, 258(2):367-392. 10.1006/jmbi.1996.0256, 8627632.
154. Miyazawa S, Jernigan RL. Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 1985, 18(3):534-552.
155. Miyazawa S, Jernigan RL. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 1996, 256(3):623-644. 10.1006/jmbi.1996.0114, 8604144.
156. Mirny LA, Shakhnovich EI. How to derive a protein folding potential? A new approach to an old problem. J Mol Biol 1996, 264(5):1164-1179. 10.1006/jmbi.1996.0704, 9000638.
157. Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997, 268:209-225. 10.1006/jmbi.1997.0959, 9149153.
158. Simons KT, Ruczinski I, Kooperberg C, Fox BA, Bystroff C, Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 1999, 34:82-95. 10.1002/(SICI)1097-0134(19990101)34:1<82::AID-PROT7>3.0.CO;2-A, 10336385.
159. Boniecki M, Rotkiewicz P, Skolnick J, Kolinski A. Protein fragment reconstruction using various modeling techniques. J Comput Aided Mol Des 2003, 17(11):725-738.
160. Robson B, Osguthorpe DJ. Refined models for computer simulation of protein folding. Applications to the study of conserved secondary structure and flexible hinge points during the folding of pancreatic trypsin inhibitor. J Mol Biol 1979, 132:19-51. 10.1016/0022-2836(79)90494-7, 513136.
161. Skolnick J, Jaroszewski L, Kolinski A, Godzik A. Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?. Protein Sci 1997, 6(3):676-688. 2143667, 9070450.
162. Skolnick J, Kolinski A, Ortiz A. Derivation of protein-specific pair potentials based on weak sequence fragment similarity. Proteins 2000, 38:3-16. 10.1002/(SICI)1097-0134(20000101)38:1<3::AID-PROT2>3.0.CO;2-S, 10651034.
163. Thomas PD, Dill KA. An iterative method for extracting energy-like quantities from protein structures. Proc Natl Acad Sci USA 1996, 93(21):11628-11633. 10.1073/pnas.93.21.11628, 38109, 8876187.
164. Tobi D, Shafran G, Linial N, Elber R. On the design and analysis of protein folding potentials. Proteins 2000, 40:71-85. 10.1002/(SICI)1097-0134(20000701)40:1<71::AID-PROT90>3.0.CO;2-3, 10813832.
165. Tanaka S, Scheraga HA. Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules 1976, 9(6):945-950. 10.1021/ma60054a013, 1004017.
166. Vendruscolo M, Domany E. Pairwise contact potentials are unsuitable for protein folding. J Chem Phys 1998, 109:11101-11108.
167. Yang Y, Zhou Y. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins 2008, 72(2):793-803. 10.1002/prot.21968, 18260109.
168. Yang Y, Zhou Y. Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions. Protein Sci 2008, 17(7):1212-1219. 10.1110/ps.033480.107, 2442011, 18469178.
169. Shen MY, Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci 2006, 15(11):2507-2524. 10.1110/ps.062416606, 2242414, 17075131.
170. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A. Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 2007, 2:2.9.
171. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2006, 5:5.6.
172. Zhang C, Vasmatzis G, Cornette JL, DeLisi C. Determination of atomic desolvation energies from the structures of crystallized proteins. J Mol Biol 1997, 267(3):707-726. 10.1006/jmbi.1996.0859, 9126848.
173. Mintseris J, Pierce B, Wiehe K, Anderson R, Chen R, Weng Z. Integrating statistical pair potentials into protein complex prediction. Proteins 2007, 69(3):511-520. 10.1002/prot.21502, 17623839.
174. Zhang J, Zhang Y. A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS ONE 2010, 5(10):e15386. 10.1371/journal.pone.0015386, 2965178, 21060880.
175. Zhou H, Skolnick J. GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophys J 2011, 101(8):2043-2052. 10.1016/j.bpj.2011.09.012, 3192975, 22004759.
176. Saladin A, Fiorucci S, Poulain P, Prevost C, Zacharias M. PTools: an opensource molecular docking library. BMC Struct. Biol. 2009, 9:27. 10.1186/1472-6807-9-27, 2685806, 19409097.
177. Fernandez-Recio J, Totrov M, Abagyan R. Identification of protein-protein interaction sites from docking energy landscapes. J Mol Biol 2004, 335(3):843-865. 10.1016/j.jmb.2003.10.069, 14687579.
178. Lazaridis T, Karplus M. Effective energy function for proteins in solution. Proteins 1999, 35(2):133-152. 10.1002/(SICI)1097-0134(19990501)35:2<133::AID-PROT1>3.0.CO;2-N, 10223287.
179. Feig M, Karanicolas J, Brooks CL. MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology. J Mol Graph Model 2004, 22(5):377-395. 10.1016/j.jmgm.2003.12.005, 15099834.