Predicting network of drug-enzyme interaction based on machine learning method

Biochimica et Biophysica Acta - Proteins and Proteomics

Volumn 1844, Issue 1 PART B, 2014, Pages 214-223

  Niu, Bing ^a   Zhang, Yuchao ^b,c   Ding, Juan ^d   Lu, Yin ^e   Wang, Miao ^f   Lu, Wencong ^a   Yuan, Xiaochen ^a   Yin, Jinyuan ^a  

^a SHANGHAI UNIVERSITY   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d SCHEPENS EYE RESEARCH INSTITUTE   (United States)

^e SHANGHAI INSTITUTE OF MATERIA MEDICA   (China)

^f SHANGHAI INTERNATIONAL STUDIES UNIVERSITY   (China)

Author keywords

CfsSubset; Drug enzyme interaction; Machine learning; Molecular descriptor; Pseudo amino acid composition; Random Forest

Indexed keywords

ENZYME;

AMINO ACID COMPOSITION; ARTICLE; DRUG INTERACTION; GEOMETRY; MATHEMATICAL ANALYSIS; MATHEMATICAL PARAMETERS; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; RANDOM FOREST; STATIC ELECTRICITY;

CFSSUBSET; DRUG-ENZYME INTERACTION; MACHINE LEARNING; MOLECULAR DESCRIPTOR; PSEUDO AMINO ACID COMPOSITION; RANDOM FOREST;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DRUG DESIGN; ENZYME INHIBITORS; HUMANS; PROTEIN INTERACTION MAPS; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 84890117087     PISSN: 15709639     EISSN: 18781454     Source Type: Journal    
DOI: 10.1016/j.bbapap.2013.07.008     Document Type: Article

References (125)

1. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa Prediction of drug-target interaction networks from the integration of chemical and genomic spaces Bioinformatics 24 2008 i232 i240
2. D.B. Kitchen, H. Decornez, J.R. Furr, and J. Bajorath Docking and scoring in virtual screening for drug discovery: methods and applications Nat. Rev. Drug Discov. 3 2004 935 949 (Pubitemid 39529931)
3. J. Venhorst, A.M. ter Laak, J.N. Commandeur, Y. Funae, T. Hiroi, and N.P. Vermeulen Homology modeling of rat and human cytochrome P450 2D (CYP2D) isoforms and computational rationalization of experimental ligand-binding specificities J. Med. Chem. 46 2003 74 86 (Pubitemid 36043787)
4. P.A. Williams, J. Cosme, A. Ward, H.C. Angove, D. Matak Vinkovic, and H. Jhoti Crystal structure of human cytochrome P450 2C9 with bound warfarin Nature 424 2003 464 468 (Pubitemid 36917499)
5. B. Baudin Angiotensin I-converting enzyme gene polymorphism and drug response Clin. Chem. Lab. Med.: CCLM/FESCC 38 2000 853 856
6. J.L. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor Bioinformatics 24 2008 225 233
7. C.Z. Cai, L.Y. Han, Z.L. Ji, X. Chen, and Y.Z. Chen SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence Nucleic Acids Res. 31 2003 3692 3697 (Pubitemid 37442225)
8. M.J. de Groot, M.J. Ackland, V.A. Horne, A.A. Alex, and B.C. Jones Novel approach to predicting P450-mediated drug metabolism: development of a combined protein and pharmacophore model for CYP2D6 J. Med. Chem. 42 1999 1515 1524 (Pubitemid 29226723)
9. M.J. de Groot, A.A. Alex, and B.C. Jones Development of a combined protein and pharmacophore model for cytochrome P450 2C9 J. Med. Chem. 45 2002 1983 1993 (Pubitemid 34477712)
10. J.Y. Park, and D. Harris Construction and assessment of models of CYP2E1: predictions of metabolism from docking, molecular dynamics, and density functional theoretical calculations J. Med. Chem. 46 2003 1645 1660 (Pubitemid 36444218)
11. K. Bleakley, and Y. Yamanishi Supervised prediction of drug-target interactions using bipartite local models Bioinformatics 18 2009 2397 2403
12. M.A. Yildirim, K.I. Goh, C. ME, A.L. Barabási, and M. Vidal Drug-target network Nat. Biotechnol. 25 2007 1119 1126 (Pubitemid 47538114)
13. F. Wang, D. Liu, H. Wang, C. Luo, and M. Zheng Computational screening for active compounds targeting protein sequences: methodology and experimental validation J. Chem. Inf. Model. 2011 2821 2828
14. A.T. Sarah, C.G.R. Stuart, M.T. Janet, R. Monica, G. Julian, and C. Cyrus Small-molecule metabolism: an enzyme mosaic Trends Biotechnol. 19 2001 482 486
15. D.S. Wishart, D. Tzur, C. Knox, R. Eisner, A.C. Guo, N. Young, D. Cheng, K. Jewell, D. Arndt, S. Sawhney, C. Fung, L. Nikolai, M. Lewis, M.A. Coutouly, I. Forsythe, P. Tang, S. Shrivastava, K. Jeroncic, P. Stothard, G. Amegbey, D. Block, D.D. Hau, J. Wagner, J. Miniaci, M. Clements, M. Gebremedhin, N. Guo, Y. Zhang, G.E. Duggan, G.D. MacInnis, A.M. Weljie, R. Dowlatabadi, F. Bamforth, D. Clive, R. Greiner, L. Li, T. Marrie, B.D. Sykes, H.J. Vogel, and L. Querengesser HMDB: the human metabolome database Nucleic Acids Res. 35 2007 D521 D526
16. K.C. Chou, Y.D. Cai, and W.Z. Zhong Predicting networking couples for metabolic pathways of Arabidopsis Experi. Clin. Sci. Inter.l online J. Adv. Sci. 5 2006 55 65
17. L. Hu, Z. He, X. Shi, X. Kong, H. Li, and W. Lu A nearest neighbor algorithm based predictor for the prediction of enzyme - small molecule interaction protein Pept. Lett. 19 2012 91 98
18. W. Yu, Z. Jiang, J. Wang, and R. Tao Using feature selection technique for drug-target interaction networks prediction Curr. Med. Chem. 18 2011 5687 5693
19. M. Fernandez, A. Sarai, and S. Ahmad Recognition of drug-target interaction patterns using genetic algorithm-optimized Bayesian-regularized neural networks and support vector machines IEEE International Conference on Systems, Man and Cybernetics Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio 2009
20. Z.S. He, J. Zhang, and X.H. Shi Predicting drug-target interaction networks based on functional groups and biological features PLoS One 5 2010
21. S. Rayne Comment on "QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers" by X. Hui-Ying, Z. Jian-Wei, Y. Qing-Sen, W. Yan-Hua, Z. Jian-Ying, and J. Hai-Xiao Chemosphere 81 2007 553
22. R.A. Gupta, A.K. Gupta, L.K. Soni, and S.G. Kaskhedikar Study of physicochemical properties-antitubercular activity relationship of naphthalene-1,4-dione analogs: a QSAR approach Chem. Pap. 63 2009 723 730
23. A.K. Gupta, A. Jain, A. Jain, K. Agrawal, V. Saraswat, S. Revathi, L.K. Soni, and S.G. Kaskhedikar QSAR analysis of indazole estrogens as selective beta-estrogen receptor ligands: rationalization of physicochemical properties Med. Chem. 3 2007 347 353 (Pubitemid 47082998)
24. E. Papa, J.C. Dearden, and P. Gramatica Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors Chemosphere 67 2007 351 358 (Pubitemid 46107565)
25. H.-Y. Xu, H.-W. Zou, Q.-S. Yu, Y.-H. Wang, H.-Y. Zhang, and H.-X. Jin QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers Chemosphere 66 2007 1998 2010 (Pubitemid 44856794)
26. A.K. Gupta, S. Revathi, L.K. Soni, and S.G. Kaskhedikar QSAR analysis of indazole estrogens as selective beta-estrogen receptor ligands: rationalization of physicochemical properties J. Pharm. Pharm. 58 2006 A57
27. J.W. Zou, Y.J. Jiang, G.X. Hu, M. Zeng, S.L. Zhuang, and Q.S. Yu QSPR/QSAR studies on the physicochemical properties and biological activities of polychlorinated biphenyls Acta Phys. -Chim. Sin. 21 2005 267 272 (Pubitemid 40460626)
28. P. Riera-Fernández, R. Martín-Romalde, F.J. Prado-Prado, M. Escobar, C.R. Munteanu, R. Concu, A. Duardo-Sanchez, and H. González-Díaz From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices Curr. Top. Med. Chem. 12 2012 927 960
29. R. Concu, G. Podda, F.M. Ubeira, and H. González-Díaz Review of QSAR models for enzyme classes of drug targets: theoretical background and applications in parasites, hosts, and other organisms Curr. Pharm. Des. 16 2010 2710 2723
30. F.J. Prado-Prado, X. García-Mera, M. Escobar, E. Sobarzo-Sánchez, M. Yañez, P. Riera-Fernandez, and H. González-Díaz 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins Eur. J. Med. Chem. 46 2011 5838 5851
31. M. Hall Correlation-based Feature Selection for Machine Learning (vol. Doctor of Philosophy) 1999 University of Waikato Hamilton
32. M.A. Hall Practical feature subset selection for machine learning Proceedings of the Twenty first Australian Computer Science Conference 1998 Springer
33. H. Holland Adaption in Natural and Artificial Systems 1975 The University of Michigan Press Ann Arbor, MI
34. R.E. Korf, and D.M. Chickering Best-first minimax search Artif. Intell. 84 1996 299 337 (Pubitemid 126365328)
35. B. Niu, L. Lu, L. Liu, T.H. Gu, K.Y. Feng, W.C. Lu, and Y.D. Cai HIV-1 protease cleavage site prediction based on amino acid property J. Comput. Chem. 30 2009 33 39
36. R. Quinlan C4. 5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers San Mateo, CA
37. L. Breiman Random forests Mach. Learn. 45 2001 5 32 (Pubitemid 32933532)
38. K. Shameer, G. Pugalenthi, K. Kandaswamy, and K.R. Sowdhamini 3dswap-pred: prediction of 3D domain swapping from protein sequence using random forest approach Protein Pept. Lett. 2011 1010 1020
39. K.K. Kandaswamy, K. Chou, T. Martinetz, S. Moller, and P. Suganthan AFP-Pred: a random forest approach for predicting antifreeze proteins from sequence-derived properties J. Theor. Biol. 270 2011 56 62
40. B. Trost, and A. Kusalik Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights Bioinformatics 29 2013 686 694
41. J. Segura, P.F. Jones, and N. Fernandez-Fuentes A holistic in silico approach to predict functional sites in protein structures Bioinformatics 28 2012 1845 1850
42. B.-Q. Li, Y.-D. Cai, K.-Y. Feng, and G.-J. Zhao Prediction of protein cleavage site with feature selection by random forest PLoS One 7 2012
43. B.-Q. Li, K.-Y. Feng, L. Chen, T. Huang, and Y.-D. Cai Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS PLoS One 7 2012
44. N. Zhang, B.-Q. Li, S. Gao, J.-S. Ruan, and Y.-D. Cai Computational prediction and analysis of protein gamma-carboxylation sites based on a random forest method Mol. Biosyst. 8 2012 2946 2955
45. C. Zheng, M. Wang, K. Takemoto, T. Akutsu, Z. Zhang, and J. Song An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins PLoS One 7 2012
46. M. Wang, X.-M. Zhao, K. Takemoto, H. Xu, Y. Li, T. Akutsu, and J. Song FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model PLoS One 7 2012
47. X.-F. Wang, Z. Chen, C. Wang, R.-X. Yan, Z. Zhang, and J. Song Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach PLoS One 6 2011
48. T.K. Ho Random decision forest Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC 1995 278 282
49. T.K. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intell. 20 1998 832 844 (Pubitemid 128741345)
50. Y. Amit, and D. Geman Shape quantization and recognition with randomized trees Neural Comput. 9 1997 1545 1588 (Pubitemid 127462804)
51. B.Q. Li, L.L. Hu, L. Chen, K.Y. Feng, and Y.D. Cai Prediction of protein domain with mRMR feature selection and analysis PLoS One 7 2012 e39308 (doi:39310.31371/journal.pone.0039308)
52. C.J. Huberty Applied Discriminant Analysis 1994 John Wiley & Sons New York
53. E. Fix, and J.L. Hodges Discriminatory analysis: nonparametric discrimination: consistency properties USAF School of Aviation Medicine: Randolph Field, TX 1951 261 279
54. R.A. Johnson, and D.W. Wichern Applied Multivariate Statistical Analysis 1982 Prentice Hall Englewood Cliffs, NJ
55. T.M. Cover, and P.E. Hart Nearest neighbor pattern classification IEEE Trans. Inf. Theory 13 1967 21 27
56. J.H. Friedman, F. Baskett, and L.J. Shustek Information TheoryC IEEE Trans. 24 1975 1000 1006
57. V.N.T. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
58. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167 (Pubitemid 128695475)
59. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge, UK
60. B. Schölkopf, and A. Smola Learning with Kernels 2002 MIT Press Cambridge, MA
61. V. Vapnik Statistical Learning Theory 1998 Wiley-Interscience New York
62. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares, and D. Haussler Knowledge-based analysis of microarray gene expression data by using support vector machines Proc. Natl. Acad. Sci. U. S. A. 97 2000 262 267 (Pubitemid 30055819)
63. F. Mittag, F. Buchel, M. Saad, A. Jahn, C. Schulte, Z. Bochdanovits, J. Simon-Sanchez, M.A. Nalls, M. Keller, D.G. Hernandez, J.R. Gibbs, S. Lesage, A. Brice, P. Heutink, M. Martinez, N.W. Wood, J. Hardy, A.B. Singleton, A. Zell, T. Gasser, and M. Sharma Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities Hum. Mutat. 33 2012 1708 1718
64. B. Niu, Q. Su, X.C. Yuan, W.C. Lu, and J. Ding QSAR study on 5-lipoxygenase inhibitors based on support vector machine Med. Chem. 8 2012 1108 1116
65. X. Wang, G. Mi, C. Wang, Y. Zhang, J. Li, Y. Guo, X. Pu, M. Li, Prediction of flavin mono-nucleotide binding sites using modified PSSM profile and ensemble support vector machine, Comput. Biol. Med. 42 1053-1059.
66. W. Maolin, W. Kai, Y. Aixia, and Y. Changyuan Classification of HCV NS5B polymerase inhibitors using support vector machine J. Mol. Sci. Int. Ed. 13 2012 4033 4047
67. P. Arbelaez, B.G. Han, D. Typke, J. Lim, R.M. Glaeser, and J. Malik Experimental evaluation of support vector machine-based and correlation-based approaches to automatic particle selection J. Struct. Biol. 175 2011 319 328
68. W.S. Sanders, C.I. Johnston, S.M. Bridges, S.C. Burgess, and K.O. Willeford Prediction of cell penetrating peptides by support vector machines PLoS Comput. Biol. 7 2011 e1002101
69. C. Wen-Chi, L. Tzong-Yi, S. Dray-Ming, J.B.K. Hsu, H. Jorng-Tzong, H. Po-Chiang, W. Ting-Yuan, H. Hsien-Da, and P. Rong-Long Incorporating support vector machine for identifying protein tyrosine sulfation sites J. Comput. Chem. 30 2009 2526 25372537
70. J.A. Erickson, M.M. Mader, I.A. Watson, Y.W. Webster, R.E. Higgs, M.A. Bell, and M. Vieth Structure-guided expansion of kinase fragment libraries driven by support vector machine models BBA-Proteins Proteom. 1804 2009 642 652
71. Y.D. Cai, and S.L. Lin Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence BBA-Proteins Proteom. 1648 2003 127 133 (Pubitemid 38234614)
72. S. Goto, T. Nishioka, and M. Kanehisa LIGAND: chemical database for enzyme reactions Bioinformatics 14 1998 591 599 (Pubitemid 28461580)
73. L. Weber JChem Base - ChemAxon Chem World-Uk 5 2008 65 66
74. F. Csizmadia JChem: Java applets and modules supporting chemical database handling from web browsers J. Chem. Inf. Comput. Sci. 40 2000 323 324
75. K.-C. Chou, and H.-B. Shen Recent progress in protein subcellular location prediction Anal. Biochem. 370 2007 1 16 (Pubitemid 47404494)
76. G. Aguero-Chapin, H. Gonzalez-Diaz, R. Molina, J. Varona-Santos, E. Uriarte, and Y. Gonzalez-Diaz Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS Lett. 580 2006 723 730 (Pubitemid 43152288)
77. J. Caballero, L. Fernandez, M. Garriga, J.I. Abreu, S. Collina, and M. Fernandez Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines J. Mol. Graph. Model. 26 2007 166 178 (Pubitemid 46977255)
78. C. Chen, Y.X. Tian, X.Y. Zou, P.X. Cai, and J.Y. Mo Using pseudo-amino acid composition and support vector machine to predict protein structural class J. Theor. Biol. 243 2006 444 448 (Pubitemid 44647324)
79. C. Chen, X.B. Zhou, Y.X. Tian, X.Y. Zou, and P.X. Cai Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network Anal. Biochem. 357 2006 116 121 (Pubitemid 44331169)
80. Y.L. Chen, and Q.Z. Li Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition J. Theor. Biol. 248 2007 377 381
81. Y.L. Chen, and Q.Z. Li Prediction of the subcellular location of apoptosis proteins J. Theor. Bio. 245 2007 775 783
82. P.F. Du, and Y.D. Li Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence BMC Bioinforma. 7 2006 518
83. H. Gonzalez-Diaz, A. Perez-Bello, E. Uriarte, and Y. Gonzalez-Diaz QSAR study for mycobacterial promoters with low sequence homology Bioorg. Med. Chem. Lett. 16 2006 547 553 (Pubitemid 43014835)
84. H. Gonzalez-Diaz, S. Vilar, L. Santana, and E. Uriarte Medicinal chemistry and bioinformatics - current trends in drugs discovery with networks topological indices Curr. Top. Med. Chem. 7 2007 1015 1029 (Pubitemid 47040529)
85. L.A. Kurgan, W. Stach, and J. Ruan Novel scales based on hydrophobicity indices for secondary protein structure J. Theor. Bio. 248 2007 354 366 (Pubitemid 47284172)
86. H. Lin, and Q.Z. Li Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant Biochem. Biophys. Res. Commun. 354 2007 548 551 (Pubitemid 46161358)
87. H. Lin, and Q.Z. Li Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components J. Comput. Chem. 28 2007 1463 1466 (Pubitemid 46853785)
88. H. Liu, M. Wang, and K.C. Chou Low-frequency Fourier spectrum for predicting membrane protein types Biochem. Biophys. Res. Commun. 336 2005 737 739 (Pubitemid 41338330)
89. H. Liu, J. Yang, M. Wang, L. Xue, and K.C. Chou Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types Protein J. 24 2005 385 389 (Pubitemid 41740154)
90. S. Mondal, R. Bhavna, R.M. Babu, and S. Ramakumar Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification J. Theor. Biol. 243 2006 252 260 (Pubitemid 44548567)
91. P. Mundra, M. Kumar, K.K. Kumar, V.K. Jayaraman, and B.D. Kulkarni Using pseudo amino acid composition to predict protein subnuclear localization: approached with PSSM Pattern Recognit. Lett. 28 2007 1610 1615 (Pubitemid 47058614)
92. Y.X. Pan, Z.Z. Zhang, Z.M. Guo, G.Y. Feng, Z.D. Huang, and L. He Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach J. Protein Chem. 22 2003 395 402 (Pubitemid 37087801)
93. X. Pua, H. Guo, H. Leung, and Y.L. Lin Prediction of membrane protein types from sequences and position-specific scoring matrices J. Theor. Biol. 247 2007 259 265 (Pubitemid 46856520)
94. S.Q. Wang, J. Yang, and K.C. Chou Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition J. Theor. Biol. 242 2006 941 946 (Pubitemid 44353534)
95. S.W. Zhang, Q. Pan, H.C. Zhang, Z.C. Shao, and J.Y. Shi Prediction of protein homo-oligomer types by pseudo amino acid composition: approached with an improved feature extraction and Naive Bayes Feature Fusion Amino Acids 30 2006 461 468 (Pubitemid 43886907)
96. T.L. Zhang, Y.S. Ding, and K.C. Chou Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence Comput. Biol. Chem. 30 2006 367 371
97. T.L. Zhang, and Y.S. Ding Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes Amino Acids 33 2007 623 629
98. T.E. Creighton Proteins - Structures and Molecular Properties 2nd ed. 1993 Freeman New York
99. M.H. Mucchielli-Giorgi, S. Hazout, and P. Tuffery PredAcc: prediction of solvent accessibility Bioinformatics 15 1999 176 177 (Pubitemid 29134734)
100. G.E. Tusnady, and I. Simon Principles governing amino acid composition of integral membrane proteins: application to topology prediction J. Mol. Biol. 283 1998 489 506 (Pubitemid 28470424)
101. Y. Freund, Y. Mansour, and R.E. Schapire Generalization bounds for averaged classifiers Ann. Stat. 32 2004 1698 1722 (Pubitemid 41250282)
102. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting J. Comput. Syst. Sci. 55 1997 119 139 (Pubitemid 127433398)
103. R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee Boosting the margin: a new explanation for the effectiveness of voting methods Ann. Stat. 26 1998 1651 1686 (Pubitemid 128376902)
104. R.E. Schapire, and Y. Singer Improved boosting algorithms using confidence-rated predictions Mach. Learn. 37 1999 297 336 (Pubitemid 32210620)
105. K.V. Mardia, J.T. Kent, and J.M. Bibby Multivariate Analysis 1979 Academic Press London
106. V.P. Ananikov, R. Szilagyi, K. Morokuma, and D.G. Musaev Can steric effects induce the mechanism switch in the rhodium-catalyzed imine boration reaction? A density functional and ONIOM study Organometallics 24 2005 1938
107. F. Weinhold Chemistry. A new twist on molecular shape Nature 411 2001 539 541 (Pubitemid 32531386)
108. C.F. Nicholas, and D.R. George Steric restrictions in protein folding: an α-helix cannot be followed by a contiguous β-strand Protein Sci. 13 2004 633 639
109. R. Srinivas, K. Pradeep, D. Feng, and V. Nikolay Automated minimization of steric clashes in protein structures Proteins 79 2011 261 270
110. M.Z. Atassi Proteomics and protein-protein interactions: biology, chemistry, bioinformatics and drug design G. Waksman, Protein Reviews vol. 3 2005 Springer Netherlands 132
111. D.E. Koshland Application of a theory of enzyme specificity to protein synthesis Proc. Natl. Acad. Sci. U. S. A. 44 1958 98 104
112. A.K.H. Hirsch, F.R. Fischer, and F. Diederich Phosphate recognition in structural biology Angew. Chem. Int. Ed. 46 2007 338 352 (Pubitemid 46142237)
113. M.O. Sinnokrot, E.F. Valeev, and C.D. Sherrill Estimates of the ab initio limit for pi-pi interactions: the benzene dimer J. Am. Chem. Soc. 124 2002 10887 10893
114. G.B. McGaughey, M. Gagné, and A.K. Rappé pi-Stacking interactions. Alive and well in proteins J. Biol. Chem. 273 1998 15458 15463 (Pubitemid 28298153)
115. S. Jones, and J.M. Thornton Principles of protein-protein interactions Proc. Natl. Acad. Sci. U.S.A. 93 1996 13 20 (Pubitemid 26038096)
116. S.Y. Noskov, and C. Lim Free energy decomposition of protein-protein interactions Biophys. J. 2001 737 750 (Pubitemid 32721449)
117. S. Roy, D. Martinez, H. Platero, T. Lane, and M. Werner-Washburne Exploiting amino acid composition for predicting protein-protein interactions PLoS One 2009 e7813
118. E. Angov, C.J. Hillier, R.L. Kincaid, and J.A. Lyon Heterologous protein expression is enhanced by harmonizing the codon usage frequencies of the target gene with those of the expression host PLoS One 2008 e2189
119. Y. Zhou, Y.S. Zhou, F. He, J.N. Song, and Z. Zhang D. Can simple codon pair usage predict protein-protein interaction? Mol. Biosyst. 2012 1396 1404
120. P. Cortazzo, C. Cervenansky, M. Marin, C. Reiss, R. Ehrlich, and A. Deana Silent mutations affect in vivo protein folding in Escherichia coli Biochem. Biophys. Res. Commun. 293 2002 537 541 (Pubitemid 34694241)
121. Y.I. Hassan, and J. Zempleni Epigenetic regulation of chromatin structure and gene function by biotin J. Nutr. 136 2006 1763 1765 (Pubitemid 43973573)
122. D. Friebel, M. von der Hagen, E.R. Baumgartner, B. Fowler, G. Hahn, P. Feyh, G. Heubner, M.R. Baumgartner, and G.F. Hoffmann The first case of 3-methylcrotonyl-CoA carboxylase (MCC) deficiency responsive to biotin Neuropediatrics 37 2006 72 78 (Pubitemid 43980085)
123. N. Santoro, T. Brtva, S. Vander Roest, K. Siegel, and G.L. Waldrop A high-throughput screening assay for the carboxyltransferase subunit of acetyl-CoA carboxylase Anal. Biochem. 354 2006 70 77 (Pubitemid 43816393)
124. M. Safro, and L. Mosyak Structural similarities in the noncatalytic domains of phenylalanyl-tRNA and biotin synthetases Protein Sci. 4 1995 2429 2432 (Pubitemid 3004143)
125. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: an update SIGKDD Explor. 11 2009 10 18