Methods for predicting protein–ligand binding sites

Methods in Molecular Biology

Volumn 1215, Issue , 2015, Pages 383-398

  Xie, Zhong Ru ^a   Hwang, Ming Jing ^a  

^a NONE

Author keywords

Bioinformatics software and servers; Ligand binding site prediction; Molecular probe; Protein surface grid; Protein ligand interaction; Structural bioinformatics; Surface pocket and cavity

Indexed keywords

ANALYTIC METHOD; ARTICLE; BINDING SITE; LIGAND BINDING; MOLECULAR PROBE; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN INTERACTION; STRUCTURAL BIOINFORMATICS; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; METABOLISM; PROCEDURES; PROPENSITY SCORE; THERMODYNAMICS;

LIGAND; PROTEIN; PROTEIN BINDING;

BINDING SITES; COMPUTATIONAL BIOLOGY; LIGANDS; PROPENSITY SCORE; PROTEIN BINDING; PROTEINS; SOFTWARE; THERMODYNAMICS;

EID: 84954614235     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-1465-4_17     Document Type: Article

References (95)

1. Leis S, Schneider S, Zacharias M (2010) In silico prediction of binding sites on proteins. Curr Med Chem 17(15):1550-1562
2. Laurie AT, Jackson RM (2006) Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr Protein Pept Sci 7(5):395-406
3. Perot S, Sperandio O, Miteva MA, Camproux AC, Villoutreix BO (2010) Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 15(15-16):656-667
4. Henrich S, Salo-Ahen OM, Huang B, Rippmann FF, Cruciani G, Wade RC (2010) Computational approaches to identifying and characterizing protein binding sites for ligand design. J Mol Recognit 23(2):209-219
5. Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8(12):995-1005
6. Wass MN, Kelley LA, Sternberg MJ (2010) 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res 38(Web Server issue):W469-W473
7. Brylinski M, Skolnick J (2008) A threadingbased method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci U S A 105(1):129-134
8. Lopez G, Maietta P, Rodriguez JM, Valencia A, Tress ML (2011) Firestar-advances in the prediction of functionally important residues. Nucleic Acids Res 39(Web Server issue):W235-W241
9. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unifi ed platform for automated protein structure and function prediction. Nat Protoc 5(4):725-738
10. Oh M, Joo K, Lee J (2009) Protein-binding site prediction based on three-dimensional protein modeling. Proteins 77(Suppl 9): 152-156
11. Roche DB, Tetchner SJ, McGuffi n LJ (2011) FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC Bioinformatics 12:160
12. Brylinski M, Feinstein WP (2013) eFindSite: improved prediction of ligand binding sites inprotein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des 27 (6):551-567
13. Roy A, Zhang Y (2012) Recognizing protein-ligand binding sites by global structural alignment and local geometry refi nement. Structure 20(6):987-997
14. Lopez G, Ezkurdia I, Tress ML (2009) Assessment of ligand binding residue predictions in CASP8. Proteins 77(Suppl 9): 138-146
15. Schmidt T, Haas J, Gallo Cassarino T, Schwede T (2011) Assessment of ligand-binding residue predictions in CASP9. Proteins 79(Suppl 10):126-136
16. Wass MN, Sternberg MJ (2009) Prediction of ligand binding sites using homologous structures and conservation at CASP8. Proteins 77(Suppl 9):147-151
17. Zhang Y (2008) Progress and challenges in protein structure prediction. Curr Opin Struct Biol 18(3):342-348
18. Liu J, Rost B (2001) Comparing function and structure between entire proteomes. Protein Sci 10(10):1970-1979
19. Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S et al (2013) The RCSB Protein Data Bank: new resources for research and education. Nucleic Acids Res 41(Database issue):D475-D482
20. Illergard K, Ardell DH, Elofsson A (2009) Structure is three to ten times more conserved than sequence-a study of structural response in protein cores. Proteins 77(3):499-508
21. Konc J, Janezic D (2010) ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment. Bioinformatics 26(9):1160-1168
22. Keskin O, Tsai CJ, Wolfson H, Nussinov R (2004) A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications. Protein Sci 13(4): 1043-1055
23. Keskin O, Nussinov R (2005) Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways. Protein Eng Des Sel 18(1):11-24
24. Gherardini PF, Wass MN, Helmer-Citterich M, Sternberg MJ (2007) Convergent evolution of enzyme active sites is not a rare phenomenon. J Mol Biol 372(3):817-845
25. Totrov M (2011) Ligand binding site superposition and comparison based on atomic property fi elds: identifi cation of distant homologues, convergent evolution and PDB-wide clustering of binding sites. BMC Bioinformatics 12(Suppl 1):S35
26. Xie L, Bourne PE (2008) Detecting evolutionary relationships across existing fold space, using sequence order-independent profi le-profi le alignments. Proc Natl Acad Sci U S A 105(14):5441-5446
27. Lee HS, Im W (2013) Ligand binding site detection by local structure alignment and its performance complementarity. J Chem Inf Model 53(9):2462-2470
28. Konc J, Janezic D (2010) ProBiS: a web server for detection of structurally similar protein binding sites. Nucleic Acids Res 38(Web Server issue):W436-W440
29. Huang B, Schroeder M (2006) LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 6:19
30. Weisel M, Proschak E, Schneider G (2007) PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Cent J 1:7
31. Tripathi A, Kellogg GE (2010) A novel and effi cient tool for locating and characterizing protein cavities and binding sites. Proteins 78(4):825-842
32. Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and effi cient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15(6):359-363, 389
33. Brady GP Jr, Stouten PF (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14(4):383-401
34. Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13(5):323-330, 307-308
35. Nayal M, Honig B (2006) On the nature of cavities on protein surfaces: application to the identifi cation of drug-binding sites. Proteins 63(4):892-906
36. Yu J, Zhou Y, Tanaka I, Yao M (2010) Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. Bioinformatics 26(1):46-52
37. Liang J, Edelsbrunner H, Woodward C (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 7(9): 1884-1897
38. Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res 31(13): 3352-3355
39. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping offunctionally annotated residues. Nucleic Acids Res 34(Web Server issue):W116-W118
40. Zhu H, Pisabarro MT (2011) MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets. Bioinformatics 27(3):351-358
41. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168
42. Schmidtke P, Le Guilloux V, Maupetit J, Tuffery P (2010) fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38(Web Server issue): W582-W589
43. Yang LW, Bahar I (2005) Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes. Structure 13(6):893-904
44. Shih CH, Chang CM, Lin YS, Lo WC, Hwang JK (2012) Evolutionary information hidden in a single protein structure. Proteins 80(6): 1647-1657
45. Glaser F, Morris RJ, Najmanovich RJ, Laskowski RA, Thornton JM (2006) A method for localizing ligand binding pockets in protein structures. Proteins 62(2):479-488
46. Ghersi D, Sanchez R (2009) EasyMIFS and SiteHound: a toolkit for the identifi cation of ligand-binding sites in protein structures. Bioinformatics 25(23):3185-3186
47. Silberstein M, Dennis S, Brown L, Kortvelyesi T, Clodfelter K, Vajda S (2003) Identifi cation of substrate binding sites in enzymes by computational solvent mapping. J Mol Biol 332(5):1095-1113
48. Laurie AT, Jackson RM (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21(9):1908-1916
49. Morita M, Nakamura S, Shimizu K (2008) Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures. Proteins 73(2):468-479
50. Ngan CH, Hall DR, Zerbe B, Grove LE, Kozakov D, Vajda S (2012) FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28(2):286-287
51. An J, Totrov M, Abagyan R (2004) Comprehensive identifi cation of “druggable” protein ligand binding sites. Genome Inform 15(2):31-41
52. Cheng G, Qian B, Samudrala R, Baker D (2005) Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design. Nucleic Acids Res 33(18):5861-5867
53. Hernandez M, Ghersi D, Sanchez R (2009) SITEHOUND-web: a server for ligand binding site identifi cation in protein structures. Nucleic Acids Res 37(Web Server issue):W413-W416
54. Soga S, Shirai H, Kobori M, Hirayama N (2007) Use of amino acid composition to predict ligand-binding sites. J Chem Inf Model 47(2):400-406
55. Edelsbrunner H, Facello M, Fu R, Liang J (1995) Measuring proteins and voids in proteins. In proceedings of the twenty-eighth Hawaii international conference on system sciences, Vol. 5: Biotechnology Computing, IEEE Computer Society Press, Los Alamitos, CA. pp 256-264
56. Mehio W, Kemp GJ, Taylor P, Walkinshaw MD (2010) Identifi cation of protein binding surfaces using surface triplet propensities. Bioinformatics 26(20):2549-2555
57. Xie ZR, Hwang MJ (2012) Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles. Bioinformatics 28(12):1579-1585
58. Xie ZR, Liu CK, Hsiao FC, Yao A, Hwang MJ (2013) LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites. Nucleic Acids Res 41(Web Server issue):W292-W296
59. Xie ZR, Hwang MJ (2010) An interactionmotif-based scoring function for protein-ligand docking. BMC Bioinformatics 11:298
60. Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA (2009) Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol 5(12):e1000585
61. An J, Totrov M, Abagyan R (2005) Pocketome via comprehensive identifi cation and classifi cation of ligand binding envelopes. Mol Cell Proteomics 4(6):752-761
62. Zhang Z, Li Y, Lin B, Schroeder M, Huang B (2011) Identifi cation of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics 27(15):2083-2088
63. Huang B (2009) MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS 13(4):325-330
64. Kawabata T (2010) Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins 78(5):1195-1211
65. Chang DT, Oyang YJ, Lin JH (2005) MEDock: a web server for effi cient prediction of ligand binding sites based on a novel optimization algorithm. Nucleic Acids Res 33(Web Server issue):W233-W238
66. Fukunishi Y, Nakamura H (2011) Prediction of ligand-binding sites of proteins by moleculardocking calculation for a random ligand library. Protein Sci 20(1):95-106
67. Gutteridge A, Bartlett GJ, Thornton JM (2003) Using a neural network and spatial clustering to predict the location of active sites in enzymes. J Mol Biol 330(4): 719-734
68. Laskowski RA, Luscombe NM, Swindells MB, Thornton JM (1996) Protein clefts in molecular recognition and function. Protein Sci 5(12):2438-2452
69. Liang X, Zhao J, Hajivandi M, Wu R, Tao J, Amshey JW et al (2006) Quantifi cation of membrane and membrane-bound proteins in normal and malignant breast cancer cells isolated from the same patient with primary breast carcinoma. J Proteome Res 5(10):2632-2641
70. Cole ST (2002) Comparative mycobacterial genomics as a tool for drug target and antigen discovery. Eur Respir J Suppl 36:78s-86s
71. Almen MS, Nordstrom KJ, Fredriksson R, Schioth HB (2009) Mapping the human membrane proteome: a majority of the human membrane proteins can be classifi ed according to function and evolutionary origin. BMC Biol 7:50
72. Laskowski RA (2001) PDBsum: summaries and analyses of PDB structures. Nucleic Acids Res 29(1):221-222
73. Singla N, Goldgur Y, Xu K, Paavilainen S, Nikolov DB, Himanen JP (2010) Crystal structure of the ligand-binding domain of the promiscuous EphA4 receptor reveals two distinct conformations. Biochem Biophys Res Commun 399(4):555-559
74. Ekins S, Kortagere S, Iyer M, Reschly EJ, Lill MA, Redinbo MR et al (2009) Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR. PLoS Comput Biol 5(12):e1000594
75. Burris TP, Montrose C, Houck KA, Osborne HE, Bocchinfuso WP, Yaden BC et al (2005) The hypolipidemic natural product guggulsterone is a promiscuous steroid receptor ligand. Mol Pharmacol 67(3):948-954
76. von Eichborn J, Murgueitio MS, Dunkel M, Koerner S, Bourne PE, Preissner R (2011) PROMISCUOUS: a database for networkbased drug-repositioning. Nucleic Acids Res 39(Database issue):D1060-D1066
77. Chiu YY, Lin CT, Huang JW, Hsu KC, Tseng JH, You SR et al (2013) KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms. Nucleic Acids Res 41(Database issue):D430-D440
78. van Linden OP, Kooistra AJ, Leurs R, de Esch IJ, de Graaf C (2013) KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space. J Med Chem 57(2):249-277
79. Skolnick J, Brylinski M (2009) FINDSITE: a combined evolution/structure-based approach to protein function prediction. Brief Bioinform 10(4):378-391
80. Gandhi NS, Mancera RL (2012) Prediction of heparin binding sites in bone morphogenetic proteins (BMPs). Biochim Biophys Acta 1824(12):1374-1381
81. Krick R, Busse RA, Scacioc A, Stephan M, Janshoff A, Thumm M et al (2012) Structural and functional characterization of the two phosphoinositide binding sites of PROPPINs, a beta-propeller protein family. Proc Natl Acad Sci U S A 109(30):E2042-E2049
82. Yu DJ, Hu J, Huang Y, Shen HB, Qi Y, Tang ZM et al (2013) TargetATPsite: a templatefree method for ATP-binding sites prediction with residue evolution image sparse representation and classifi er ensemble. J Comput Chem 34(11):974-985
83. Khare H, Ratnaparkhi V, Chavan S, Jayraman V (2012) Prediction of protein-mannose binding sites using random forest. Bioinformation 8(24):1202-1205
84. Gandhi NS, Freeman C, Parish CR, Mancera RL (2012) Computational analyses of the catalytic and heparin-binding sites and their interactions with glycosaminoglycans in glycoside hydrolase family 79 endo-beta-d-glucuronidase (heparanase). Glycobiology 22(1):35-55
85. Jmol: an open-source Java viewer for chemical structure s in 3D. http://www.jmol.lorg
86. Lopez G, Valencia A, Tress ML (2007) Firestar-prediction of functionally important residues using structural templates and alignment reliability. Nucleic Acids Res 35(Web Server issue):W573-W577
87. Zhang Y (2009) I-TASSER: fully automated protein structure prediction in CASP8. Proteins 77(Suppl 9):100-113
88. Zhang Y (2007) Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69(Suppl 8):108-117
89. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40
90. Roche DB, Buenavista MT, Tetchner SJ, McGuffi n LJ (2011) The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res 39(Web Server issue):W171-W176
91. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E et al (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31(1):365-370
92. McKusick VA (1998) On the naming of clinical disorders, with particular reference to eponyms. Medicine (Baltimore) 77(1):1-2
93. Kalidas Y, Chandra N (2008) PocketDepth: a new depth based algorithm for identifi cation of ligand binding sites in proteins. J Struct Biol 161(1):31-42
94. Tan KP, Varadarajan R, Madhusudhan MS (2011) DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins. Nucleic Acids Res 39(Web Server issue):W242-W248
95. Volkamer A, Kuhn D, Rippmann F, Rarey M (2012) DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28(15): 2074-2075