Sequence-based predictor of ATP-binding residues using random forest and mRMR-IFS feature selection

Journal of Theoretical Biology

Volumn 360, Issue , 2014, Pages 59-66

  Ma, Xin ^a   Sun, Xiao ^b  

^a NANJING AUDIT UNIVERSITY   (China)

^b SOUTHEAST UNIVERSITY   (China)

Author keywords

Binding propensity; Physicochemical property; Position specific scoring matrix

Indexed keywords

ADENOSINE TRIPHOSPHATE; PROTEIN; PROTEIN BINDING;

CHEMICAL BINDING; FOREST COVER; HYBRID; PHYSICOCHEMICAL PROPERTY; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CLINICAL EFFECTIVENESS; CORRELATION COEFFICIENT; INCREMENTAL FEATURE SELECTION; MACHINE LEARNING; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; MINIMUM REDUNDANCY MAXIMUM RELEVANCE; PHYSICAL CHEMISTRY; POSITION WEIGHT MATRIX; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN PROTEIN INTERACTION; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; BIOLOGICAL MODEL; GENETICS; METABOLISM; PROTEIN SECONDARY STRUCTURE;

ADENOSINE TRIPHOSPHATE; ALGORITHMS; MODELS, BIOLOGICAL; PROTEIN BINDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS;

EID: 84904503198     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2014.06.037     Document Type: Article

References (40)

1. Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25:3389-3402.
2. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E. The protein data bank. Nucleic Acids Res. 2000, 28:235-242.
3. Bracht K., Liebeke M., Ritter C.A., Grunert R., Bednarski P.J. Correlations between the activities of 19 standard anticancer agents, antioxidative enzyme activities and the expression of ATP-binding cassette transporters: comparison with the National Cancer Institute data. Anticancer Drugs 2007, 18:389-404.
4. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
5. Buechler C., Bauer S. ATP binding cassette transporter A1 (ABCA1) associated proteins: potential drug targets in the metabolic syndrome and atherosclerotic disease?. Curr. Pharm. Biotechnol. 2011, 13:319-330.
6. Bustamante C., Chemla Y.R., Forde N.R., Izhaky D. Mechanical processes in biochemistry. Annu. Rev. Biochem. 2004, 73:705-748.
7. Chauhan J.S., Mishra N.K., Raghava G.P. Identification of ATP binding residues of a protein from its primary sequence. BMC Bioinf. 2009, 10:434.
8. Chen K., Kurgan L. Investigation of atomic level patterns in protein-small ligand interactions. PLoS One 2009, 4:e4473.
9. Chen K., Mizianty M.J., Kurgan L. ATPsite: sequence-based prediction of ATP-binding residues. Proteome Sci. 2011, 9(Suppl. 1):S4.
10. Crecelius A.R., Kirby B.S., Richards J.C., Dinenno F.A. Mechanical effects of muscle contraction increase intravascular ATP draining quiescent and active skeletal muscle in humans. J. Appl. Physiol. 2013, 114:1085-1093.
11. Faraggi E., Xue B., Zhou Y. Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 2009, 74:847-856.
12. Fasman G.D. Protein conformational prediction. Trends Biochem. Sci. 1989, 14:295-299.
13. Frishman D., Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins 1997, 27:329-335.
14. Gao Y.F., Li B.Q., Cai Y.D., Feng K.Y., Li Z.D., Jiang Y. Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection. Mol. Biosyst. 2013, 9:61-69.
15. Grantham R. Amino acid difference formula to help explain protein evolution. Science 1974, 185:862-864.
16. Gyoneva S., Orr A.G., Traynelis S.F. Differential regulation of microglial motility by ATP/ADP and adenosine. Park. Relat. Disord. 2009, 15(Suppl. 3):S195-S199.
17. Hirokawa N., Takemura R. Biochemical and molecular characterization of diseases linked to motor proteins. Trends Biochem. Sci. 2003, 28:558-565.
18. Huang T., Cui W., Hu L., Feng K., Li Y.X., Cai Y.D. Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles. PLoS One 2009, 4:e8126.
19. Janin J., Chothia C. Role of hydrophobicity in the binding of coenzymes. Appendix. Translational and rotational contribution to the free energy of dissociation. Biochemistry 1978, 17:2943-2948.
20. Kawashima S., Pokarowski P., Pokarowska M., Kolinski A., Katayama T., Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 2008, 36:D202-D205.
21. Klein P., Kanehisa M., DeLisi C. Prediction of protein function from sequence properties. Discriminant analysis of a data base. Biochim. Biophys. Acta 1984, 787:221-226.
22. Kuiper J.W., van Horssen R., Oerlemans F., Peters W., van Dommelen M.M., te Lindert M.M., ten Hagen T.L., Janssen E., Fransen J.A., Wieringa B. Local ATP generation by brain-type creatine kinase (CK-B) facilitates cell motility. PLoS One 2009, 4:e5030.
23. Levitt M. A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 1976, 104:59-107.
24. Li B.Q., Feng K.Y., Chen L., Huang T., Cai Y.D. Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS. PLoS One 2012, 7:e43927.
25. Liaw A., Weiner M. Classification and regression by random forest. R News 2002, 2(3):18-22.
26. Ma X., Guo J., Liu H.D., Xie J.M., Sun X. Sequence-based prediction of DNA-binding residues in proteins with conservation and correlation information. IEEE/ACM Trans. Comput. Biol. Bioinform. 2012, 9:1766-1775.
27. Ma X., Guo J., Wu J., Liu H., Yu J., Xie J., Sun X. Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature. Proteins 2011, 79:1230-1239.
28. Murakami Y., Spriggs R.V., Nakamura H., Jones S. PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences. Nucleic Acids Res. 2010, 38:W412-W416.
29. Niu S., Hu L.L., Zheng L.L., Huang T., Feng K.Y., Cai Y.D., Li H.P., Li Y.X., Chou K.C. Predicting protein oxidation sites with feature selection and analysis approach. J. Biomol. Struct. Dyn. 2012, 29:650-658.
30. Peng H., Long F., Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27:1226-1238.
31. Poitry S., van Bever L., Coppex F., Roatti A., Baertschi A.J. Differential sensitivity of atrial and ventricular K(ATP) channels to metabolic inhibition. Cardiovasc. Res. 2003, 57:468-476.
32. Regnier M., Lee D.M., Homsher E. ATP analogs and muscle contraction: mechanics and kinetics of nucleoside triphosphate binding and hydrolysis. Biophys. J. 1998, 74:3044-3058.
33. Shen J., Zhang J., Luo X., Zhu W., Yu K., Chen K., Li Y., Jiang H. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. USA 2007, 104:4337-4341.
34. Skedelj V., Tomasic T., Masic L.P., Zega A. ATP-binding site of bacterial enzymes as a target for antibacterial drug design. J. Med. Chem. 2011, 54:915-929.
35. Takano K., Yutani K. A new scale for side-chain contribution to protein stability based on the empirical stability analysis of mutant proteins. Protein Eng. 2001, 14:525-528.
36. Vapnik V.N. Statisical Learning Theory 1998, Wiley, New York.
37. Wang L., Huang C., Yang M.Q., Yang J.Y. BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst. Biol. 2010, 4(Suppl. 1):S3.
38. Wu J., Liu H., Duan X., Ding Y., Wu H., Bai Y., Sun X. Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics 2009, 25:30-35.
39. Xiong Y., Liu J., Zhang W., Zeng T. Prediction of heme binding residues from protein sequences with integrative sequence profiles. Proteome Sci. 2012, 10(Suppl. 1):S20.
40. Xue Y., Zhou F., Zhu M., Ahmed K., Chen G., Yao X. GPS: a comprehensive www server for phosphorylation sites prediction. Nucleic Acids Res. 2005, 33:W184-W187.