Learning to translate sequence and structure to function: Identifying DNA binding and membrane binding proteins

Annals of Biomedical Engineering

Volumn 35, Issue 6, 2007, Pages 1043-1052

  Langlois, Robert E ^a   Carson, Matthew B ^a   Bhardwaj, Nitin ^a   Lu, Hui ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

AdaBoost; C4.5; Feature interactions; Support vector machines

Indexed keywords

BIOINFORMATICS; BIOLOGICAL MEMBRANES; BIOMOLECULES; DNA SEQUENCES; SUPPORT VECTOR MACHINES;

ADABOOST; ANNOTATION APPROACH; FEATURE INTERACTIONS;

PROTEINS;

DNA BINDING PROTEIN; MEMBRANE PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; CLASSIFICATION; COMPUTER SIMULATION; EVALUATION; METHODOLOGY; MOLECULAR GENETICS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; STRUCTURE ACTIVITY RELATION; ULTRASTRUCTURE;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DNA-BINDING PROTEINS; MEMBRANE PROTEINS; MODELS, CHEMICAL; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 34249666548     PISSN: 00906964     EISSN: 15739686     Source Type: Journal    
DOI: 10.1007/s10439-007-9312-z     Document Type: Conference Paper

References (47)

1. Ahmad, S., M. M. Gromiha, and A. Sarai. Prediction of DNA binding in proteins from composition, sequence and structure. Genome Inf. 13:308-309, 2002.
2. Ahmad, S., M. M. Gromiha, and A. Sarai. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics 20:477-486, 2004.
3. Ahmad, S., and A. Sarai. Moment-based prediction of DNA-binding proteins. J. Mol. Biol. 341:65-71, 2004.
4. Bhardwaj, N., and H. Lu. Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions. FEBS Lett. 581:1058-1066, 2007.
5. Bhardwaj, N., R. Langlois, G. Zhao, and H. Lu. Structure based prediction of binding residues on DNA-binding proteins. In: Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China.
6. Bhardwaj, N., R. E. Langlois, G. Zhao, and H. Lu. Kernel-based machine learning protocol for predicting DNA-binding proteins. Nucleic Acids Res. 33:6486-6493, 2005.
7. Bhardwaj, N., R. V. Stahelin, R. E. Langlois, W. Cho, and H. Lu. Structural bioinformatics prediction of membrane-binding proteins. J. Mol. Biol. 359:486-495, 2006.
8. Brooks, B., R. E. Bruccoleri, B. Olafson, D. States, S. Swaminathan, and M. Karplus. Charmm: a program for macromolecular energy, minimization, and dynamics Calculations. J. Comput. Chem. 4:187-217, 1983.
9. Buck, M. J., and J. D. Lieb. Chip-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 83:349-360, 2004.
10. Burges, C. J. C. A tutorial on support vector machines. Data Min. Knowl. Dine. 2:121-167, 1998.
11. Burley, S. K., S. C. Almo, J. B. Bonanno, M. Capel, M. R. Chance, T. Gaasterland, D. Lin, A. Sali, F. W. Studier, and S. Swaminathan. Structural genomics: beyond the human genome project. Nat. Genet. 23:151-157, 1999.
12. Cajone, F., M. Salina, and A. Benelli-Zazzera. 4-Hydroxynonenal induces a DNA-binding protein similar to the heat-shock factor. Biochem. J. 262:977-979, 1989.
13. Chang, C.-C., and C.-J. Lin. Libsvm: a library for support vector machines, 2003. Available from: http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm. pdf.
14. Cho, W., and R. V. Stahelin. Membrane-protein interactions in cell signaling and membrane trafficking. Annu. Rev. Biophys. Biomol. Struct. 34:119-151, 2005.
15. Chou, C.-C., T.-W. Lin, C.-Y. Chen, and A. H. J. Wang. Crystal structure of the hyperthermophilic archaeal DNA-binding protein sso10b2 at a resolution of 1.85 angstroms. J. Bacteriol. 185:4066-4073, 2003.
16. Cortes, C., and V. Vapnik. Support-vector networks. Mach. Learn. 20:273-297, 1995.
17. Fawcett, T. Roc graphs: notes and practical considerations for data mining researchers, 2003. Available from: http://www.hpl.hp.com/techreports/ 2003/HPL-2003-4.pdf.
18. Freeman, K., M. Gwadz, and D. Shore. Molecular and genetic analysis of the toxic effect of rap1 overexpression in yeast. Genetics 141:1253-1262, 1995.
19. Freund, Y., and R. E. Schapire. Experiments with a new boosting algorithm. In: Proceedings of 13th Annual International. Conference on Machine Learning, Bari, Italy, 1996.
20. Friedman, J., T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting, 1998. Available from: http://www-stat.stanford. edu/~jhf/ftp/boost.ps.
21. Goutte, C. Note on free lunches and cross-validation. Neural. Comp. 9:1211-1215, 1997.
22. Henriette Mozsolits, M.-I. A. Surface plasmon resonance spectroscopy: an emerging tool for the study of peptide-membrane interactions. Peptide. Sci. 66:3-18, 2002.
23. Hurley, J. H., and T. Meyer. Subcellular targeting by membrane lipids. Curr. Opin. Cell. Biol 13:146-52, 2001.
24. Jones, S., D. T. A. Daley, N. M. Luscombe, H. M. Berman, and J. M. Thornton. Protein-RNA interactions: a structural analysis. Nucleic Acids Res. 29:943-954, 2001.
25. Kabsch, W., and C. Sander. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577-2637, 1983.
26. Kaplan, T., N. Friedman, and H. Margalit. Ab initio prediction of transcription factor targets using structural knowledge. PLoS. Comp. Biol. 1:e1, 2005.
27. Kearns, M., and D. Ron. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural. Comp. 11:1427-1453, 1999.
28. Kuznetsov, I. B., Z. Gou, R. Li, and S. Hwang. Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins. Proteins Struct. Funct. Bioinform. 64:19-27, 2006.
29. Langlois, R. E., A. Diec, O. Perisic, Y. Dai, and H. Lu. Improved protein fold assignment using support vector machines. Int. J. Bioinform. Res. Appl. 1:319-334, 2006.
30. Langlois, R. E., and H. Lu. User manual of malibu: machine learning workbench for bioinformatics applications, 2007. Available from: http://proteomics.bioengr.uic.edu/malibu/.
31. Lei, Z., and Y. Dai. A novel approach for prediction of protein subcellular localization from sequence using fourier analysis and support vector machines. In: Proceedings of 4th ACM SIGKDD Workshop on Data Mining in Bioinformatics, Seattle, 2004.
32. McDonald, R. A., D. J. Hand, and I. A. Eckley. An empirical comparison of three boosting algorithms on real data sets with artificial class noise. In: Lecture Notes in Computer Science, edited by T. Windeatt and F. Roli. Berlin: Springer-Verlag, 2003, pp. 35-44.
33. Niculescu-Mizil, A., and R. Caruana. An empirical comparison of supervised learning algorithms In: Proceedings of 23rd Annual International Conference on Machine Learning, Pittsburgh, Pennsylvania, 2006.
34. Oehler, S., R. Alex, and A. Barker. Is nitrocellulose filter binding really a universal assay for protein-DNA interactions? Anal. Biochem. 268:330-336, 1998.
35. Pellegrini-Calace, M., and J. M. Thornton. Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information. Nucleic. Acids Res. 33:2129-2140, 2005.
36. Quinlan, J. R. Induction of decision trees. Mach. Learn. 1:81-106, 1986.
37. Quinlan, J. R. Improved use of continuous attributes in c4.5. J. Artif. Intell. Res. 4:77-90, 1996.
38. Saetrom, P., R. Sneve, K. I. Kristiansen, O. Snove Jr, T. Grunfeld, T. Rognes, and E. Seeberg. Predicting non-coding rna genes in escherichia coli with boosted genetic programming. Nucleic. Acids. Res. 33:3263-3270, 2005.
39. Schapire, R. E., and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37:297-336, 1999.
40. Shanahan, H. P., M. A. Garcia, S. Jones, and J. M. Thornton. Identifying DNA-binding proteins using structural motifs and the electrostatic potential. Nucleic Acids Res. 32:4732-4741, 2004.
41. Stawiski, E. W., L. M. Gregoret, and Y. Mandel-Gutfreund. Annotating nucleic acid-binding function based on protein structure. J. Mol. Biol. 326:1065-1079, 2003.
42. Stormo, G. DNA binding sites: representation and discovery. Bioinformatics 16:16-23, 2000.
43. Stormo, G. D. Computer methods for analyzing sequence recognition of nucleic acids. Annu. Rev. Biophys. Biophys. Chem. 17:241-263, 1988.
44. Teruel, M. N., and T. Meyer. Translocation and reversible localization of signaling proteins: a dynamic future for signal transduction. Cell 103:181-184, 2000.
45. Wang, G., and R. L. Dunbrack Jr. Pisces: a protein sequence culling server. Bioinformatics 19:1589-1591, 2003.
46. Wu, P. G., and L. Brand. Resonance energy transfer: methods and applications. Anal. Biochem. 218:1-13, 1994.
47. Yan, C., M. Terribilini, F. Wu, R. L. Jernigan, D. Dobbs, and V. Honavar. Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinform. 7:262-272, 2006.