3dswap-pred: Prediction of 3D domain swapping from protein sequence using random forest approach

Protein and Peptide Letters

Volumn 18, Issue 10, 2011, Pages 1010-1020

  Shameer, Khader ^a   Pugalenthi, Ganesan ^b   Kandaswamy, Krishna Kumar ^c   Sowdhamini, Ramanathan ^a  

^a UNIVERSITY OF AGRICULTURAL SCIENCES   (India)

^b GENOME INSTITUTE OF SINGAPORE   (Singapore)

^c UNIVERSITY OF LÜBECK   (Germany)

Author keywords

3D domain swapping; Hinge region; Machine learning; Prediction algorithm; Protein oligomer; Random forest; Swapped region

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; COMPUTER PREDICTION; COMPUTER PROGRAM; DATA MINING; PROTEIN ASSEMBLY; PROTEIN DATABASE; PROTEIN STRUCTURE; RANDOM FOREST; STATISTICAL ANALYSIS; THREE DIMENSIONAL DOMAIN SWAPPING; VALIDITY;

ALGORITHMS; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS;

EID: 79960575553     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986611796378729     Document Type: Article

References (115)

1. Bennett, M. J.; Choe, S.; Eisenberg, D. Domain swapping: Entangling alliances between proteins. Proc. Natl. Acad. Sci. U S A 1994, 91(8), 3127-3131. (Pubitemid 24130188)
2. Bennett, M. J.; Choe, S.; Eisenberg, D. Refined structure of dimeric diphtheria toxin at 2.0 A resolution. Protein Sci. 1994, 3(9), 1444-63. (Pubitemid 24314464)
3. Bennett, M. J.; Schlunegger, M. P.; Eisenberg, D. 3D domain swapping: A mechanism for oligomer assembly. Protein Sci., 1995, 4(12), 2455-68.
4. Bennett, M. J.; Eisenberg, D. The evolving role of 3D domain swapping in proteins. Structure, 2004, 12(8), 1339-41. (Pubitemid 39092078)
5. Vitagliano, L.; Adinolfi, S.; Sica, F.; Merlino, A.; Zagari, A.; Mazzarella, L. A potential allosteric subsite generated by domain swapping in bovine seminal ribonuclease. J. Mol. Biol., 1999, 293(3), 569-577. (Pubitemid 29512057)
6. Berman, H.; Henrick, K.; Nakamura, H.; Markley, J. L., The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res., 2007, 35(Database issue), D301-3.
7. Liu, S.; Lu, Z.; Han, Y.; Melamud, E.; Dunaway-Mariano, D.; Herzberg, O. Crystal structures of 2-methylisocitrate lyase in complex with product and with isocitrate inhibitor provide insight into lyase substrate specificity, catalysis and evolution. Biochemistry, 2005, 44(8), 2949-2962. (Pubitemid 40293670)
8. Schymkowitz, J. W.; Rousseau, F.; Wilkinson, H. R.; Friedler, A.; Itzhaki, L. S. Observation of signal transduction in threedimensional domain swapping. Nat. Struct. Biol., 2001, 8(10), 888-892. (Pubitemid 32923610)
9. Sanejouand, Y. H. Domain swapping of CD4 upon dimerization. Proteins, 2004, 57(1), 205-212.
10. Maekawa, A.; Schmidt, B.; Fazekas de St Groth, B.; Sanejouand, Y. H.; Hogg, P. J. Evidence for a domain-swapped CD4 dimer as the coreceptor for binding to class II MHC. J. Immunol., 2006, 176(11), 6873-8. (Pubitemid 43787868)
11. Botos, I.; O'Keefe, B. R.; Shenoy, S. R.; Cartner, L. K.; Ratner, D. M.; Seeberger, P. H.; Boyd, M. R.; Wlodawer, A. Structures of the complexes of a potent anti-HIV protein cyanovirin-N and high mannose oligosaccharides. J. Biol. Chem., 2002, 277(37), 34336-42.
12. Paavilainen, V. O.; Hellman, M.; Helfer, E.; Bovellan, M.; Annila, A.; Carlier, M. F.; Permi, P.; Lappalainen, P. Structural basis and evolutionary origin of actin filament capping by twinfilin. Proc. Natl. Acad. Sci. U S A, 2007, 104(9), 3113-8. (Pubitemid 46364122)
13. Guo, Z.; Eisenberg, D., Runaway domain swapping in amyloid-like fibrils of T7 endonuclease I. Proc. Natl. Acad. Sci. U S A, 2006, 103(21), 8042-7. (Pubitemid 43801049)
14. Nilsson, M.; Wang, X.; Rodziewicz-Motowidlo, S.; Janowski, R.; Lindstrom, V.; Onnerfjord, P.; Westermark, G.; Grzonka, Z.; Jaskolski, M.; Grubb, A. Prevention of domain swapping inhibits dimerization and amyloid fibril formation of cystatin C: Use of engineered disulfide bridges, antibodies, and carboxymethylpapain to stabilize the monomeric form of cystatin C. J. Biol. Chem., 2004, 279(23), 24236-45. (Pubitemid 38725285)
15. Liu, C.; Sawaya, M. R.; Eisenberg, D. beta(2)-microglobulin forms three-dimensional domain-swapped amyloid fibrils with disulfide linkages. Nat. Struct. Mol. Biol., 2010.
16. Orlikowska, M.; Jankowska, E.; Kolodziejczyk, R.; Jaskolski, M.; Szymanska, A. Hinge-loop mutation can be used to control 3D domain swapping and amyloidogenesis of human cystatin C. J. Struct. Biol., 2010.
17. Yang, S.; Levine, H.; Onuchic, J. N.; Cox, D. L. Structure of infectious prions: Stabilization by domain swapping. FASEB J., 2005, 19(13), 1778-82. (Pubitemid 41598761)
18. Liu, H. L.; Lin, Y. M.; Zhao, J. H.; Hsieh, M. C.; Lin, H. Y.; Huang, C. H.; Fang, H. W.; Ho, Y.; Chen, W. Y. Molecular dynamics simulations of human cystatin C and its L68Q varient to investigate the domain swapping mechanism. J. Biomol. Struct. Dyn., 2007, 25(2), 135-144. (Pubitemid 47450965)
19. Lin, Y. M.; Liu, H. L.; Zhao, J. H.; Huang, C. H.; Fang, H. W.; Ho, Y.; Chen, W. Y. Molecular dynamics simulations to investigate the domain swapping mechanism of human cystatin C. Biotechnol. Prog., 2007, 23(3), 577-584. (Pubitemid 46911181)
20. Liu, Y.; Eisenberg, D. 3D domain swapping: As domains continue to swap. Protein Sci., 2002, 11(6), 1285-1299. (Pubitemid 34547201)
21. Bennett, M. J.; Sawaya, M. R.; Eisenberg, D. Deposition diseases and 3D domain swapping. Structure, 2006, 14(5), 811-824. (Pubitemid 43674098)
22. Jaskolski, M. 3D domain swapping, protein oligomerization, and amyloid formation. Acta Biochim. Pol., 2001, 48(4), 807-827. (Pubitemid 33602339)
23. Nagradova, N. K., Three-dimensional domain swapping in homooligomeric proteins and its functional significance. Biochemistry (Mosc), 2002, 67(8), 839-849. (Pubitemid 35208637)
24. Newcomer, M. E., Protein folding and three-dimensional domain swapping: A strained relationship? Curr. Opin. Struct. Biol., 2002, 12(1), 48-53. (Pubitemid 34142720)
25. Khare, S. D.; Dokholyan, N. V. Molecular mechanisms of polypeptide aggregation in human diseases. Curr. Protein Pept. Sci., 2007, 8(6), 573-579. (Pubitemid 351266990)
26. Carey, J.; Lindman, S.; Bauer, M.; Linse, S. Protein reconstitution and three-dimensional domain swapping: Benefits and constraints of covalency. Protein Sci., 2007, 16(11), 2317-33. (Pubitemid 350036739)
27. Gronenborn, A. M., Protein acrobatics in pairs-dimerization via domain swapping. Curr. Opin. Struct. Biol., 2009, 19(1), 39-49.
28. Milburn, M. V.; Hassell, A. M.; Lambert, M. H.; Jordan, S. R.; Proudfoot, A. E.; Graber, P.; Wells, T. N. A novel dimer configuration revealed by the crystal structure at 2.4 A resolution of human interleukin-5. Nature, 1993, 363(6425), 172-176. (Pubitemid 23147017)
29. Devos, R.; Plaetinck, G.; Cornelis, S.; Guisez, Y.; Van der Heyden, J.; Tavernier, J., Interleukin-5 and its receptor: A drug target for eosinophilia associated with chronic allergic disease. J Leukoc Biol 1995, 57 (6), 813-9.
30. Bewley, C. A.; Gustafson, K. R.; Boyd, M. R.; Covell, D. G.; Bax, A.; Clore, G. M.; Gronenborn, A. M. Solution structure of cyanovirin-N, a potent HIV-inactivating protein. Nat. Struct. Biol., 1998, 5(7), 571-578.
31. Matei, E.; Furey, W.; Gronenborn, A. M., Solution and crystal structures of a sugar binding site mutant of cyanovirin-N: No evidence of domain swapping. Structure, 2008, 16(8), 1183-1194.
32. Yang, F.; Bewley, C. A.; Louis, J. M.; Gustafson, K. R.; Boyd, M. R.; Gronenborn, A. M.; Clore, G. M.; Wlodawer, A. Crystal structure of cyanovirin-N, a potent HIV-inactivating protein, shows unexpected domain swapping. J. Mol. Biol., 1999, 288(3), 403-712.
33. Nagarkar, R. P.; Hule, R. A.; Pochan, D. J.; Schneider, J. P. Domain swapping in materials design. Biopolymers, 94(1), 141-155.
34. He, Z.; Zhang, J.; Shi, X. H.; Hu, L. L.; Kong, X.; Cai, Y. D.; Chou, K. C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One, 2010, 5(3), e9603.
35. Huang, T.; Shi, X. H.; Wang, P.; He, Z.; Feng, K. Y.; Hu, L.; Kong, X.; Li, Y. X.; Cai, Y. D.; Chou, K. C. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS One, 2010, 5(6), e10972.
36. Chou, K. C.; Shen, H. B. Plant-mPLoc: A top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One, 2010, 5(6), e11335.
37. Chen, L.; Huang, T.; Shi, X. H.; Cai, Y. D.; Chou, K. C. Analysis of protein pathway networks using hybrid properties. Molecules, 2010, 15(11), 8177-8192.
38. Chou, K. C., Prediction of tight turns and their types in proteins. Anal. Biochem., 2000, 286(1), 1-16.
39. Chen, L.; Feng, K. Y.; Cai, Y. D.; Chou, K. C.; Li, H. P. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics, 2010, 11, 293.
40. Chen, K.; Kurgan, L. A.; Ruan, J. Prediction of protein structural class using novel evolutionary collocation-based sequence representation. J. Comput. Chem., 2008, 29(10), 1596-1604.
41. Chou, K. C. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins, 1995, 21(4), 319-344.
42. Kedarisetti, K. D.; Kurgan, L.; Dick, S. Classifier ensembles for protein structural class prediction with varying homology. Biochem. Biophys. Res. Commun., 2006, 348 (3), 981-988. (Pubitemid 44233512)
43. Xiao, X.; Wang, P.; Chou, K. C. Quat-2L: A web-server for predicting protein quaternary structural attributes. Mol. Divers., 2010.
44. Chou, K. C.; Shen, H. B., MemType-2L: A web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun., 2007, 360(2), 339-45.
45. Shen, H. B.; Chou, K. C. EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem. Biophys. Res. Commun., 2007, 364(1), 53-9.
46. Xiao, X.; Wang, P.; Chou, K. C. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J. Comput. Chem., 2009, 30(9), 1414-23.
47. Xiao, X.; Wang, P.; Chou, K. C. GPCR-2L: Predicting G proteincoupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol. Biosyst., 2010.
48. Chou, K. C.; Shen, H. B. ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Commun., 2008, 376(2), 321-325.
49. Chou, K. C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J. Biol. Chem., 1993, 268(23), 16938-16948.
50. Chou, K. C. Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal. Biochem., 1996, 233(1), 1-14.
51. Chou, K. C.; Shen, H. B. Signal-CF: A subsite-coupled and window-fusing approach for predicting signal peptides. Biochem. Biophys. Res. Commun., 2007, 357(3), 633-640.
52. Chou, K. C., Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11(16), 2105-2134.
53. DeLano, W. L. The PyMOL Molecular Graphics System. http://www.pymol.org/. (Accessed November, 1, 2010).
54. Bernstein, H. J. Recent changes to RasMol, recombining the variants. Trends Biochem. Sci., 2000, 25(9), 453-455. (Pubitemid 30662414)
55. Sayle, R. A.; Milner-White, E. J. RASMOL: Biomolecular graphics for all. Trends Biochem. Sci., 1995, 20(9), 374.
56. Sowdhamini, R.; Blundell, T. L. An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins. Protein Sci., 1995, 4(3), 506-520.
57. Pugalenthi, G.; Archunan, G.; Sowdhamini, R. DIAL: A web-based server for the automatic identification of structural domains in proteins. Nucleic Acids Res., 2005, 33(Web Server issue), W130-2.
58. Laskowski, R. A. Enhancing the functional annotation of PDB structures in PDBsum using key figures extracted from the literature. Bioinformatics, 2007, 23(14), 1824-1827. (Pubitemid 47250316)
59. Li, W.; Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006, 22(13), 1658-1659. (Pubitemid 43985301)
60. Chou, K. C.; Shen, H. B. Recent progress in protein subcellular location prediction. Anal. Biochem., 2007, 370(1), 1-16.
61. Chou, K. C.; Shen, H. B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One, 2010, 5(4), e9931.
62. Pugalenthi, G.; Kumar, K. K.; Suganthan, P. N.; Gangal, R. Identification of catalytic residues from protein structure using support vector machine with sequence and structural features. Biochem. Biophys. Res. Commun., 2008, 367(3), 630-634.
63. Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 1999, 292(2), 195-202. (Pubitemid 29435759)
64. McGuffin, L. J.; Bryson, K.; Jones, D. T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, 16(4), 404-405. (Pubitemid 30417087)
65. Dudoit, S.; Fridlyand, J.; Speed, T. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. J.Am. Stat. Assoc., 2002, 97(457), 77-87.
66. Wu, B.; Abbott, T.; Fishman, D.; McMurray, W.; Mor, G.; Stone, K.; Ward, D.; Williams, K.; Zhao, H. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics, 2003, 19(13), 1636-1643. (Pubitemid 37121151)
67. Lee, J. W.; Lee, J. B.; Park, M.; Song, S. H. An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis, 2005, 48(4), 869-885.
68. Qi, Y.; Klein-Seetharaman, J.; Bar-Joseph, Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pac. Symp. Biocomput., 2005, 531-542.
69. Diaz-Uriarte, R.; Alvarez de Andres, S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 2006, 7, 3.
70. Statnikov, A.; Wang, L.; Aliferis, C. F. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics, 2008, 9, 319.
71. Jiang, P.; Wu, H.; Wang, W.; Ma, W.; Sun, X.; Lu, Z. MiPred: Classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res., 2007, 35(Web Server issue), W339-44.
72. Chen, X. W.; Liu, M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics, 2005, 21(24), 4394-4400. (Pubitemid 41782930)
73. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: A classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci., 2003, 43(6), 1947-1958.
74. Ho, T. K. A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors. Pattern Analysis & Applications, 2002, 5(2), 102-112. (Pubitemid 40830124)
75. Breiman, L. Random Forests. Machine Learning, 2001, 45(1), 5-32. (Pubitemid 32933532)
76. Andy, L.; Matthew, W. Classification and Regression by random Forest. R News, 2002, 2(3), 18-22.
77. Ho, T. K. The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20(8), 832-844. (Pubitemid 128741345)
78. Mitchell, T. M. Machine Learning McGraw-Hill: 1997.
79. Frank, E.; Hall, M.; Trigg, L.; Holmes, G.; Witten, I. H. Data mining in bioinformatics using Weka. Bioinformatics, 2004, 20(15), 2479-2481. (Pubitemid 39429925)
80. Chou, K. C.; Shen, H. B. Euk-mPLoc: A fusion classifier for largescale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res., 2007, 6(5), 1728-1734.
81. Zhou, G. P. An intriguing controversy over protein structural class prediction. J. Protein Chem., 1998, 17(8), 729-738.
82. Zhou, G. P.; Assa-Munt, N. Some insights into protein structural class prediction. Proteins, 2001, 44(1), 57-59. (Pubitemid 32510584)
83. Zhou, G. P.; Doctor, K. Subcellular location prediction of apoptosis proteins. Proteins, 2003, 50(1), 44-48. (Pubitemid 36090607)
84. Zhou, X. B.; Chen, C.; Li, Z. C.; Zou, X. Y. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol., 2007, 248(3), 546-51. (Pubitemid 47368372)
85. Chen, C.; Chen, L.; Zou, X.; Cai, P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein Pept. Lett., 2009, 16(1), 27-31.
86. Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein Pept. Lett., 2009, 16(4), 351-5.
87. Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol., 2010, 263(2), 203-209.
88. Georgiou, D. N.; Karakasidis, T. E.; Nieto, J. J.; Torres, A. Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. J. Theor. Biol., 2009, 257(1), 17-26.
89. Jiang, X.; Wei, R.; Zhang, T.; Gu, Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: An approach by approximate entropy. Protein Pept. Lett., 2008, 15(4), 392-396. (Pubitemid 351679342)
90. Li, F. M.; Li, Q. Z. Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett., 2008, 15(6), 612-616.
91. Lin, H.; Ding, H.; Guo, F. B.; Zhang, A. Y.; Huang, J. Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein Pept. Lett., 2008, 15(7), 739-744.
92. Mohabatkar, H. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein Pept. Lett., 2010, 17(10), 1207-1214.
93. Qiu, J. D.; Huang, J. H.; Shi, S. P.; Liang, R. P. Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: An approach with support vector machine based on discrete wavelet transform. Protein Pept. Lett., 2010, 17(6), 715-722.
94. Joshi, R. R.; Sekharan, S. Characteristic peptides of protein secondary structural motifs. Protein Pept. Lett., 2010, 17(10), 1198-1206.
95. Li, S.; Li, H.; Li, M.; Shyr, Y.; Xie, L.; Li, Y. Improved prediction of lysine acetylation by support vector machines. Protein Pept. Lett., 2009, 16(8), 977-983.
96. Lin, H. The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252(2), 350-356.
97. Liu, T.; Zheng, X.; Wang, C.; Wang, J. Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: An approach from auto covariance transformation. Protein Pept. Lett., 2010, 17(10), 1263-1269.
98. Lu, J.; Niu, B.; Liu, L.; Lu, W. C.; Cai, Y. D. Prediction of small molecules' metabolic pathways based on functional group composition. Protein Pept. Lett., 2009, 16(8), 969-976.
99. Nanni, L.; Lumini, A. A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease. Protein Pept. Lett., 2009, 16(2), 163-167.
100. Zeng, Y. H.; Guo, Y. Z.; Xiao, R. Q.; Yang, L.; Yu, L. Z.; Li, M. L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J. Theor. Biol., 2009, 259(2), 366-372.
101. Zhang, G. Y.; Fang, B. S. Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. J. Theor. Biol., 2008, 253(2), 310-315.
102. Chen, C.; Chen, L. X.; Zou, X. Y.; Cai, P. X. Predicting protein structural class based on multi-features fusion. J. Theor. Biol., 2008, 253(2), 388-392.
103. Du, P.; Li, Y. Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information. J. Theor. Biol., 2008, 253(3), 579-586.
104. Kandaswamy, K. K.; Pugalenthi, G.; Moller, S.; Hartmann, E.; Kalies, K. U.; Suganthan, P. N.; Martinetz, T. Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino Acid composition. Protein Pept. Lett., 2010, 17(12), 1473-1479.
105. Liu, T.; Jia, C. A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. J. Theor. Biol., 2010, 267(3), 272-275.
106. Masso, M.; Vaisman, II, Knowledge-based computational mutagenesis for predicting the disease potential of human nonsynonymous single nucleotide polymorphisms. J. Theor. Biol., 2010, 266(4), 560-568.
107. Shi, M. G.; Huang, D. S.; Li, X. L. A protein interaction network analysis for yeast integral membrane protein. Protein Pept. Lett., 2008, 15(7), 692-699.
108. Tian, F.; Lv, F.; Zhou, P.; Yang, Q.; Jalbout, A. F. Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-affinity relationship approach. Protein Pept. Lett., 2008, 15(10), 1033-1043.
109. Vilar, S.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E. A network- QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J. Theor. Biol., 2009, 261(3), 449-458.
110. Wang, T.; Xia, T.; Hu, X. M. Geometry preserving projections algorithm for predicting membrane protein types. J. Theor. Biol., 2010, 262(2), 208-213.
111. Yang, X. Y.; Shi, X. H.; Meng, X.; Li, X. L.; Lin, K.; Qian, Z. L.; Feng, K. Y.; Kong, X. Y.; Cai, Y. D. Classification of transcription factors using protein primary structure. Protein Pept. Lett., 2010, 17(7), 899-908.
112. Kandaswamy, K. K.; Chou, K. C.; Martinetz, T.; Moller, S.; Suganthan, P. N.; Sridharan, S.; Pugalenthi, G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol., 2010.
113. Kandaswamy, K. K.; Pugalenthi, G.; Hartmann, E.; Kalies, K. U.; Moller, S.; Suganthan, P. N.; Martinetz, T. SPRED: A machine learning approach for the identification of classical and nonclassical secretory proteins in mammalian genomes. Biochem. Biophys. Res. Commun., 2010, 391(3), 1306-1311.
114. Brylinski, M.; Skolnick, J. FINDSITE-metal: Integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level. Proteins, 2010.
115. Shameer, K.; Pugalenthi, G.; Kandaswamy, K. K.; Suganthan, P. N.; Archunan, G.; Sowdhamini, R. Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach. Bioinformatics and Biology Insights, 2010, 4(4), 10.