SVMCRYS: An SVM approach for the prediction of protein crystallization propensity from protein sequence

Protein and Peptide Letters

Volumn 17, Issue 4, 2010, Pages 423-430

  Kandaswamy, Krishna Kumar ^a   Pugalenthi, Ganesan ^b   Suganthan, P N ^b   Gangal, Rajeev ^c  

^a UNIVERSITY OF LÜBECK   (Germany)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c Intertek India Pvt Ltd   (India)

Author keywords

Crystallization targets; Feature selection; Nuclear magnetic resonance (NMR); Protein crystallization propensity; Recalcitrant; Support vector machine (SVM); X ray crystallography

Indexed keywords

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; METABOLISM; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE; PROTEIN DATABASE; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; STRUCTURE ACTIVITY RELATION; X RAY CRYSTALLOGRAPHY;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; CRYSTALLOGRAPHY, X-RAY; DATABASES, PROTEIN; NUCLEAR MAGNETIC RESONANCE, BIOMOLECULAR; PROTEINS; REPRODUCIBILITY OF RESULTS; ROC CURVE; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77950100747     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986610790963726     Document Type: Article

References (38)

1. Brenner, S. E. A tour of structural genomics. Nat. Rev. Genet., 2001, 2, 801-809.
2. Burley, S.K. An overview of structural genomics. Nat. Struct. Biol., 2000, 7, 932-934.
3. Berman, H.; Henrick, K.; Nakamura, H.; Markley, J. L. The worldwide Protein Data Bank (wwPDB): ensuring a single, uni- form archive of PDB data. Nucleic Acids Res., 2007, 35, D301-D303.
4. Vekilov, P.G.; Chernov, A.A. The Physics of Protein Crystallization, in Solid State Physics, Academic Press, New York, 2002.
5. McPherson, A. Crystallization of biological macromolecules, Cold Spring Harbor Laboratory Press, New York, 1999.
6. Yee, A.; Pardee, K.; Christendat, D.; Savchenko, A.; Edwards, A. M.; Arrowsmith, C. H. Structural proteomics: toward high- throughput structural biology as a tool in functional genomics. Acc. Chem. Res., 2003, 36, 183-189.
7. Derewenda, Z. S. Rational Protein Crystallization by Mutational Surface Engineering. Structure, 2004, 12, 529-535.
8. Goldschmidt, L.; Cooper, D.R.; Derewenda, Z.S.; Eisenberg, D. Toward rational protein crystallization: A Web server for the de- sign of crystallizable protein variants. Protein Sci., 2007, 16(8), 1569-1576.
9. Smialowski, P.; Schmidt, T.; Cox, J.; Kirschner, A.; Frishman, D. Will my protein crystallize? A sequence-based predictor. Proteins, 2006, 62(2), 343-355.
10. Chen, K.; Kurgan, L.; Rahbari, M. Prediction of protein crystalliza- tion using collocation of amino acid pairs. Biochem. Biophys. Res. Commun., 2007, 355, 764.
11. Overton, I. M.; Barton, G. J. A normalised scale for structural genomics target ranking: The OB-Score. FEBS Lett., 2006, 580, 4005.
12. Overton, I. M.; Padovani, G.; Girolami, M.A.; Barton, G. J. ParCrys: A Parzen Window Density Estimation Approach to Protein Crystallisation Propensity Prediction. Bioinformatics, 2008, 24(7), 901-907.
13. Slabinski, L.; Jaroszewski, L.; Rychlewski, L.; Wilson, I.A.; Les- ley, S.A.; Godzik, A. XtalPred: a web server for prediction of pro- tein crystallizability. Bioinformatics, 2007, 23(24), 3403-3405.
14. Pugalenthi, G.; Kumar, K. K.; Suganthan, P. N.; Gangal, R. Identi- fication of catalytic residues from protein structure using support vector machine with sequence and structural features. Biochem. Biophys. Res. Commun., 2008, 367, 630-634.
15. McGuffin, L. J.; Bryson, K.; Jones, D. T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, 16(4), 404.
16. Pugalenthi, G.; Tang, K.; Suganthan, P.N.; Chakrabarti, S. Identifi- cation of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble. Bioinformatics, 2009, 25(2), 204-210.
17. Pugalenthi, G.; Tang, K.; Suganthan, P.N.; Archunan, G.; Sowd- hamini, R. A Machine Learning Approach for the Identification of Odorant binding Proteins from Sequence-derived Properties. BMC Bioinformatics, 2007, 8, 351.
18. Slabinski, L.; Jaroszewski, L.; Rodrigues, A.P.; Rychlewski, L.; Wilson, I.A.; Lesley, S.A.; Godzik, A. The challenge of protein structure determination- lessons from structural genomics. Protein Sci., 2007, 16(11), 2472-2482.
19. Saridakis, E.; Chayen, N.E. Towards a 'universal' nucleant for protein crystallization. Trends Biotechnol., 2009, 27(2), 99-106.
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, D202-5.
21. Vapnik, V. The nature of statistical learning theory, Springer, New York, 1995.
22. Müller, K.-R.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B. An introduction to kernel-based learning algorithms. IEEE Trans. Neu- ral. Netw, 2001, 2, 181-201.
23. Chou, K.C.; Cai, Y.D. Using functional-domain composition and support vector machines for prediction of protein subcellular location. J Biol. Chem., 2002, 29, 45765-45769.
24. Ding, Y. S.; Zhang, T. L.; Chou, K. C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett., 2007, 14, 811-815.
25. Chen, J.; Liu, H.;Yang, J.; Chou, K. C. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 2007, 33(3), 423-428.
26. Du, Q. S.; Wei, Y. T.; Pang, Z. W.; Chou, K. C.; Huang, R. B. Predicting the affinity of epitope-peptides with class I MHC mole- cule HLA-A*0201: an application of amino acid-based peptide prediction. Protein Eng. Des. Sel., 2007, 20(9), 417-23.
27. Chang, C. C.; Lin, C. J. LIBSVM: a library for support vector machines, 2001 www.csie.ntu.edu.tw/_cjlin/libsvm.
28. Frank, E.; Hall, M.; Trigg, L.; Holmes, G.; Witten, I. H Data min- ing in bioinformatics using Weka. Bioinformatics, 2004, 20, 2479-2481.
29. Steinrauf, L.K. Preliminary X-ray data for some new crystalline forms of [beta]-lactoglobulin and hen-egg-white lysozyme. Acta Crystallographica, 1959, 12, 77-79.
30. Sedzik, J.; Kirschner, D. A. Is myelin basic protein crystallizable? Neurochem. Res., 1992, 17(2), 157-166.
31. Terribilini, M.; Lee, J.H.; Yan, C.; Jernigan, R. L.; Carpenter, S.; Honavar, V.; Dobbs, D. Identifying interaction sites in recalcitrant proteins: predicted protein and rna binding sites in HIV-1 and EIAV agree with experimental data. Pacific Symp. Biocomput., 2006, 11, 415-426.
32. Sonnhammer, L.; Eddy, S.R.; Durbin, R. Pfam: a Comprehensive Database of Protein Domain Families Based on Seed Alignments. Proteins, 1997, 28, 405-420.
33. Malawski, G.A.; Hillig, R.C.; Monteclaro, F.; Eberspaecher, U.; Schmitz, A.A.; Crusius, K.; Huber, U.; Egner, M.; Donner, P.; Müller-Tiemann, B. Identifying protein construct variants with in- creased crystallization propensity - A case study. Protein Sci., 2006, 15, 2718-2728.
34. Goh, C.S.; Lan, N.; Douglas, S.M.; Wu, B.; Echols, N.; Smith, A.; Milburn, D.; Montelione, G.T.; Zhao, H.; Gerstein, M. Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis. J. Mol. Biol., 2004, 336, 115-130.
35. Smialowski, P.; Martin-Galiano, A. J.; Cox, J.; Frishman, D. Predicting experimental properties of proteins from sequence by ma- chine learning techniques. Curr. Protein Pept. Sci., 2007, 8, 121-133.
36. Fisher, H. F. A Limiting Law Relating the Size and Shape of Protein Molecules to their Composition. Proc. Natl. Acad. Sci. USA, 1964, 51, 1285-1291.
37. Dill, K. A. Theory for the folding and stability of globular proteins. Biochemistry, 1985, 24, 1501-1509.
38. Yamada, H.; Tamada, T.; Kosaka, M.; Miyata, K.; Fujiki, S.; Tano, M.; Moriya, M.; Yamanishi, M.; Honjo, E.; Tada, H.; Ino, T.; Ya- maguchi, H.; Futami, J., Seno, M.; Nomoto, T.; Hirata, T.; Yoshi- mura, M.; Kuroki, R. Crystal lattice engineering,' an approach to engineer protein crystal contacts by creating intermolecular symmetry: crystallization and structure determination of a mutant human RNase 1 with a hydrophobic interface of leucines. Protein Sci., 2007, 16, 1389-1397.