Predicting enzymatic function from global binding site descriptors

Proteins: Structure, Function and Bioinformatics

Volumn 81, Issue 3, 2013, Pages 479-489

  Volkamer, Andrea ^a   Kuhn, Daniel ^b   Rippmann, Friedrich ^b   Rarey, Matthias ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

^b Merck Serono GmbH   (Germany)

Author keywords

Descriptor based; DoGSite; EC number; Enzyme classification; Function prediction; Pocket prediction; Structure based; Support vector machine

Indexed keywords

ARTICLE; BINDING SITE; ENZYME ACTIVITY; ENZYME SPECIFICITY; LIGAND BINDING; PRIORITY JOURNAL; PROTEIN FUNCTION; PROTEIN STRUCTURE; SUPPORT VECTOR MACHINE;

ALGORITHMS; BACTERIA; BACTERIAL PROTEINS; CATALYTIC DOMAIN; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; ENZYME ACTIVATION; LIGANDS; MOLECULAR SEQUENCE ANNOTATION; PHOSPHOTRANSFERASES; STRUCTURE-ACTIVITY RELATIONSHIP; SUBSTRATE SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 84873091814     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24205     Document Type: Article

References (88)

1. Brown S, Gerlt J, Seffernick J, Babbitt P. A gold standard set of mechanistically diverse enzyme superfamilies. Genome Biol 2006; 7: R8.
2. Koonin E, Tatusov R, Galperin M. Beyond complete genomes: from sequence to structure and function. Curr Opin Struct Biol 1998; 8: 355-363.
3. Rost B. Enzyme function less conserved than anticipated. J Mol Biol 2002; 318: 595-608.
4. Ringe D, Wei Y, Boino K, Ondrechen M. Protein structure to function: insights from computation. Cell Mol Life Sci 2004; 61: 387-392.
5. Watson J, Laskowski R, Thornton J. Predicting protein function from sequence and structural data. Curr Opin Struct Biol 2005; 15: 275-284.
6. Godzik A, Jambon M, Friedberg I. Computational protein function prediction: are we making progress? Cell Mol Life Sci 2007; 64: 2505-2511.
7. Hawkins T, Kihara D. Function prediction of uncharacterized proteins. J Bioinform Comput Biol 2007; 5: 1-30.
8. Lee D, Redfern O, Orengo C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 2007; 8: 995-1005.
9. Hawkins T, Chitale M, Kihara D. New paradigm in protein function prediction for large scale omics analysis. Mol Biosyst 2008; 4: 223-231.
10. Rentzsch R, Orengo C. Protein function prediction-the power of multiplicity. Trends Biotechnol 2009; 27: 210-219.
11. Pierri C, Parisi G, Porcelli V. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. Biochim Biophys Acta 2010; 1804: 1695-1712.
12. Brylinski M, Skolnick J. Comparison of structure-based and threading-based approaches to protein functional annotation. Proteins 2010; 78: 118-134.
13. Sleator R, Walsh P. An overview of in silico protein function prediction. Arch Microbiol 2010; 192: 151-155.
14. Xin F, Radivojac P. Computational methods for identification of functional residues in protein structures. Curr Protein Peptide Sci 2011; 12: 456-469.
15. Sael L, Chitale M, Kihara D. Structure- and sequence-based function prediction for non-homologous proteins. J Struct Funct Geonmics 2012; 13: 111-123.
16. Nisius B, Sha F, Gohlke H. Structure-based computational analysis of protein binding sites for function and druggability prediction. J Biotechnol 2012; 159: 123-134.
17. Schnoes A, Brown S, Dodevski I, Babbitt P. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput Biol 2009; 5: e1000605.
18. Benson D, Karsch-Mizrachi I, Clark K, Lipman D, Ostell J, Sayers E. Genbank. Nucleic Acids Res 2012; 40: D48-D53.
19. Bernstein F, Koetzle T, Williams G, Meyer E, Brice M, Rodgers J, Kennard O, Shimanouchi T, Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 1977; 112: 535-542.
20. Parasuram R, Lee J, Yin P, Somarowthu S, Ondrechen M. Functional classification of protein 3D structures from predicted local interaction sites. J Bioinform Comput Biol 2010; 8( Suppl 1): 1-15.
21. Devos D, Valencia A. Practical limits of function prediction. Proteins 2000; 41: 98-107.
22. Pires D, de Melo-Minardi R, dos Santos M, da Silveira C, Santoro M, Meira W. Cutoff scanning matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics 2011; 12 ( Suppl 4), S12.
23. Gold N, Jackson R. A searchable database for comparing protein-ligand binding sites for the analysis of structure-function relationships. J Chem Inf Model 2006; 46: 736-742.
24. Perot S, Sperandio O, Miteva M, Camproux A-C, Villoutreix B. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 2010; 15: 656-667.
25. Schmitt S, Kuhn D, Klebe G. A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol 2002; 323: 387-406.
26. Apweiler R, Bairoch A, Wu C, Barker W, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin M, Natale D, O'Donovan C, Redaschi N, Yeh L-S. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004; 32: D115-D119.
27. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman D. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25: 3389-3402.
28. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer ELL, Eddy SR, Bateman A, Finn RD. The Pfam protein families database. Nucleic Acids Res 2012; 40: D290-D301.
29. Henikoff J, Greene E, Pietrokovski S, Henikoff S. Increased coverage of protein families with the blocks database servers. Nucleic Acids Res 2000; 28: 228-230.
30. Wallace A, Borkakoti N, Thornton J. TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites. Protein Sci 1997; 6: 2308-2323.
31. Sigrist C, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, Bairoch A, Bucher P. PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform 2002; 3: 265-274.
32. Almonacid D, Yera E, Mitchell J, Babbitt P. Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function. PLoS Comput Biol 2010; 6: e1000700.
33. Enault F, Suhre K, Claverie J-M. Phydbac Gene function predictor: a gene annotation tool based on genomic context analysis. BMC Bioinform 2005; 6: 247.
34. Engelhardt B, Jordan M, Muratore K, Brenner S. Protein molecular function prediction by Bayesian phylogenomics. PLoS Comput Biol 2005; 1: e45.
35. Krishnamurthy N, Brown D, Sjolander K. FlowerPower: clustering proteins into domain architecture classes for phylogenomic inference of protein function. BMC Evol Biol 2007; 7 ( Suppl 1): S12.
36. Khan S, Situ G, Decker K, Schmidt C. GoFigure: automated gene ontology annotation. Bioinformatics 2003; 19: 2484-2485.
37. Zehetner G. OntoBlast function: from sequence similarities directly to potential functional annotations by ontology terms. Nucleic Acids Res 2003; 31: 3799-3803.
38. Chitale M, Hawkins T, Park C, Kihara D. ESG: extended similarity group method for automated protein function prediction. Bioinfor 2009; 25: 1739-1745.
39. Wass M, Sternberg M. ConFunc-functional annotation in the twilight zone. Bioinform 2008; 24: 798-806.
40. Hubbard T, Murzin A, Brenner S, Chothia C. SCOP: a structural classification of proteins database. Nucleic Acids Res 1997; 25: 236-239.
41. Orengo C, Michie A, Jones S, Jones D, Swindells M, Thornton J. CATH-a hierarchic classification of protein domain structures. Structure 1997; 5: 1093-1109.
42. Holm L, Sander C. The FSSP database: fold classification based on structure-structure alignment of proteins. Nucleic Acids Res 1996; 24: 206-209.
43. Krissinel E, Henrick K. Protein structure comparison service fold at European Bioinformatics Institute. Available at http://www.ebi.ac.uk/msd-srv/ssm, accessed on July 9, 2012.
44. Galperin M, Walker D, Koonin E. Analogous enzymes: independent inventions in enzyme evolution. Genome Res 1998; 8: 779-
45. Illergard K, Ardell D, Elofsson A. Structure is three to ten times more conserved than sequence-a study of structural response in protein cores. Proteins 2009; 77: 499-508.
46. Pieper U, Webb BM, Barkan DT, Schneidman-Duhovny D, Schlessinger A, Braberg H, Yang Z, Meng EC, Pettersen EF, Huang CC, Datta RS, Sampathkumar P, Madhusudhan MS, Sjölander K, Ferrin TE, Burley SK, Sali A. ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 2011; 39: D465-D474.
47. Ye Y, Godzik A. Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics 2003; 19 ( Suppl 2): ii246-ii255.
48. Taubig H, Buchner A, Griebsch J. PAST: fast structure-based searching in the PDB. Nucleic Acids Res 2006; 34: W20-W23.
49. Gibrat J, Madej T, Bryant S. Surprising similarities in structure comparison. Curr Opi Struct Biol 1996; 6: 377-385.
50. Wang S, Peng J, Xu J. Alignment of distantly related protein structures: algorithm, bound and implications to homology modeling. Bioinformatics 2011; 27: 2537-2545.
51. Wallace A, Laskowski R, Thornton J. Derivation of 3D coordinate templates for searching structural databases: application to Ser-His-Asp catalytic triads in the serine proteinases and lipases. Protein Sci 1996; 5: 1001-1013.
52. Kellenberger E, Schalon C, Rognan D. How to measure the similarity between protein ligand-binding sites? Curr ComputAided Drug Des 2008; 4: 209-220.
53. Shulman-Peleg A, Nussinov R, Wolfson H. Recognition of functional sites in protein structures. J Mol Biol 2004; 339: 607-633.
54. Milik M, Szalma S, Olszewski K. Common structural cliques: a tool for protein structure and function analysis. Protein Eng 2003; 16: 543-552.
55. Kuhn D, Weskamp N, Schmitt S, Hullermeier E, Klebe G. From the similarity analysis of protein cavities to the functional classification of protein families using cavbase. J Mol Biol 2006; 359: 1023-1044.
56. Konc J, Janezic D. ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment. Bioinformatics 2010; 26: 1160-1168.
57. Kinoshita K, Furui J, Nakamura H. Identification of protein functions from a molecular surface database, eF-site. J Struct Funct Genomics 2002; 2: 9-22.
58. Kinoshita K, Murakami Y, Nakamura H. eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape. Nucleic Acids Res 2007; 35: W398-W402.
59. Fetrow J, Godzik A, Skolnick J. Functional analysis of the Escherichia coli genome using the sequence-to-structure-to-function paradigm: identification of proteins exhibiting the glutaredoxin/thioredoxin disulfide oxidoreductase activity. J Mol Biol 1998; 282: 703-711.
60. Fetrow J. Active site profiling to identify protein functional sites in sequences and structures using the Deacon Active Site Profiler (DASP). Curr Protoc Bioinformatics 2006;Chapter 8, Unit 8.10.
61. Binkowski T, Joachimiak A. Protein functional surfaces: global shape matching and local spatial alignments of ligand binding sites. BMC Struct Biol 2008; 8: 45.
62. Schalon C, Surgand J-S, Kellenberger E, Rognan DA simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins 2008; 71: 1755-1778.
63. Weill N, Rognan D. Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites. J Chem Info Model 2010; 50: 123-135.
64. Kontoyianni M, Rosnick C. Functional prediction of binding pockets. J Chem Info Model 2012; 52; 824-833.
65. Stawiski E, Baucom A, Lohr S, Gregoret L. Predicting protein function from structure: unique structural features of proteases. Proc Natl Acad Sci USA 2000; 97: 3954-3958.
66. Dobson P, Doig A. Distinguishing enzyme structures from non-enzymes without alignments. J Mol Biol 2003; 330: 771-783.
67. Dobson P, Doig A. Predicting enzyme class from protein structure without alignments. J Mol Biol 2005; 345: 187-199.
68. Cai C, Han L, Ji Z, Chen Y. Enzyme family classification by support vector machines. Proteins 2004; 55; 66-76.
69. Strombergsson H, Kleywegt G. A chemogenomics view on protein-ligand spaces. BMC Bioinformatics 2009; 10( Suppl 6): S13.
70. Davidson N, Wang X. Non-alignment features based enzyme/non-Enzyme classification using an ensemble method. Proceedings of the International Conference on Machine Learning and Applications, Washington, DC, USA, 2010; 546-551.
71. Izrailev S, Farnum M. Enzyme classification by ligand binding. Proteins 2004; 57: 711-724.
72. Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Sohngen C, Stelzer M, Thiele J, Schomburg D. BRENDA, the enzyme information system in 2011. Nucleic Acids Res 2011; 39: D670-D676.
73. Hermann J, Marti-Arbona R, Fedorov A, Fedorov E, Almo S, Shoichet B, Raushel F. Structure-based activity prediction for an enzyme of unknown function. Nature 2007; 448: 775-779.
74. Gutteridge A, Bartlett G, Thornton J. Using a neural network and spatial clustering to predict the location of active sites in enzymes. J Mol Biol 2003; 330: 719-734.
75. Petrova N, Wu C. Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties. BMC Bioinformatics 2006; 7: 312.
76. Youn E, Peters B, Radivojac P, Mooney S. Evaluation of features for catalytic residue prediction in novel folds. Protein Sci 2007; 16: 216-226.
77. Alterovitz R, Arvey A, Sankararaman S, Dallett C, Freund Y, Sjolander K. ResBoost: characterizing and predicting catalytic residues in enzymes. BMC Bioinformatics 2009; 10: 197.
78. Laskowski R, Watson J, Thornton J. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005; 33: W89-W93.
79. Pal D, Eisenberg D. Inference of protein function from protein structure. Structure 2005; 13: 121-130.
80. Gerlt J, Allen K, Almo S, Armstrong R, Babbitt P, Cronan J, Dunaway-Mariano D, Imker H, Jacobson M, Minor W, Poulter C, Raushel F, Sali A, Shoichet B, Sweedler J. The enzyme function initiative. Biochemistry 2011; 50: 9950-9962.
81. Pegg S-H, Brown S, Ojha S, Seffernick J, Meng E, Morris J, Chang P, Huang C, Ferrin T, Babbitt P. Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database. Biochemistry 2006; 45: 2545-2555.
82. Webb EC. Enzyme nomenclature 1992: recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of enzymes. Academic Press; San Diego: 1992, ISBN 0-12-227164-5.
83. Volkamer A, Kuhn D, Grombacher T, Rippmann F, Rarey M. Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 2012; 52: 360-372.
84. Volkamer A, Griewel A, Grombacher T, Rarey M. Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model 2010; 50: 2041-2052.
85. Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011; 2: 27:1-27:27. Available at: http://www.csie.ntu.edu.tw/cjlin/libsvm, accessed on June 29, 2012.
86. Bray T, Doig A, Warwicker J. Sequence and structural features of enzymes and their active sites by EC class. J Mol Biol 2009; 386; 1423-1436.
87. Konc J, Janezic D. ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins. Nucleic Acids Res 2012; 40: W214-W221.
88. Zarembinski T, Hung L, Mueller-Dieckmann H, Kim K, Yokota H, Kim R, Kim S. Structure-based assignment of the biochemical function of a hypothetical protein: a test case of structural genomics. Proc Nat Acad Sci USA 1998; 95: 15189-15193.