Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

2011 IEEE International Conference on Systems Biology, ISB 2011

Volumn , Issue , 2011, Pages 18-26

  Wang, Mingjun ^a   Shen, Hong Bin ^b   Akutsu, Tatsuya ^c   Song, Jiangning ^a,d  

^a Chinese Academy of Sciences   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c KYOTO UNIVERSITY   (Japan)

^d MONASH UNIVERSITY   (Australia)

Author keywords

feature selection; non synonymous SNPs; random forest; single amino acid polymorphisms (SAPs); support vector machine

Indexed keywords

CHARACTERISTIC SEQUENCE; CORRELATION COEFFICIENT; DATA SETS; GENETIC VARIATION; HUMAN DISEASE; NON-SYNONYMOUS SNPS; PERFORMANCE COMPARISON; PREDICTION ACCURACY; PREDICTION PERFORMANCE; RANDOM FOREST; RANDOM FOREST ALGORITHM; RANDOM FORESTS; SINGLE AMINO ACID POLYMORPHISMS (SAPS); SOMATIC MUTATION; STRUCTURAL FEATURE; SVM-BASED CLASSIFIERS;

AMINO ACIDS; DECISION TREES; DISEASES; FORECASTING; OPTIMIZATION; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 80054867468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISB.2011.6033115     Document Type: Conference Paper

References (57)

1. Collins, F. S., L. D. Brooks, and A. Chakravarti, A DNA Polymorphism Discovery Resource for Research on Human Genetic Variation. Genome Research, 1998. 8 (12): p. 1229-1231.
2. Care, M. A., et al., Deleterious SNP prediction: be mindful of your training data! Bioinformatics, 2007. 23 (6): p. 664-672.
3. Ye, Z.-Q., et al., Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP). Bioinformatics, 2007. 23 (12): p. 1444-1450.
4. Halushka, M. K., et al., Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet, 1999. 22 (3): p. 239-247.
5. Bromberg, Y., G. Yachdav, and B. Rost, SNAP predicts effect of mutations on protein function. Bioinformatics, 2008. 24 (20): p. 2397-2398.
6. Cargill, M. E. A., Characterization of single-nucleotide polymorphisms in coding regions of human genes Nat. Genet, 1999. 22: p. 231-238.
7. Sunyaev, S., V. Ramensky, and P. Bork, Towards a structural basis of human non-synonymous single nucleotide polymorphisms. Trends in genetics, 2000. 16 (5): p. 198-200.
8. Wang, Z. and J. Moult, SNPs, protein structure, and disease. Human Mutation, 2001. 17 (4): p. 263-270.
9. Gong, S. and T. L. Blundell, Structural and Functional Restraints on the Occurrence of Single Amino Acid Variations in Human Proteins. Plos One, 2010.5(2):e9186.
10. Botstein, D. and N. Risch, Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet, 2003.
11. Liu, Q.-R., et al., Addiction molecular genetics: 639, 401 SNP whole genome association identifies many "cell adhesion" genes. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2006. 141 B (8): p. 918-925.
12. Li, Y. Z., et al., Predicting disease-associated substitution of a single amino acid by analyzing residue interactions. BMC Bioinformatics, 2011. 12.
13. Huang, T., et al., Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties. Plos One, 2010.5(7):e11900.
14. Dobson, R., et al., Predicting deleterious nsSNPs: an analysis of sequence and structural attributes. BMC Bioinformatics, 2006. 7 (1): p. 217.
15. Bao, L. and Y. Cui, Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics, 2005. 21 (10): p. 2185-2190.
16. Cai, Z., et al., Bayesian approach to discovering pathogenic SNPs in conserved protein domains. Human Mutation, 2004. 24 (2): p. 178-184.
17. Ferrer-Costa, C., M. Orozco, and X. de la Cruz, Sequence-based prediction of pathological mutations. Proteins: Structure, Function, and Bioinformatics, 2004. 57 (4): p. 811-819.
18. Karchin, R., et al., LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources. Bioinformatics, 2005. 21 (12): p. 2814-2820.
19. Krishnan, V. G. and D. R. Westhead, A comparative study of machine-learning methods to predict the effects of single nucleotide polymorphisms on protein function. Bioinformatics, 2003. 19 (17): p. 2199-2209.
20. Ramensky, V., P. Bork, and S. Sunyaev, Human non-synonymous SNPs: server and survey. Nucleic Acids Research, 2002. 30 (17): p. 3894-3900.
21. Yue, P. and J. Moult, Identification and Analysis of Deleterious Human SNPs. Journal of Molecular Biology, 2006. 356 (5): p. 1263-1274.
22. Bairoch, A., et al., The Universal Protein Resource (UniProt). Nucleic Acids Res, 2005. 33: p. D154-159.
23. Forbes, S. A., et al., The Catalogue of Somatic Mutations in Cancer (COSMIC). Curr Protoc Hum Genet, 2008. Chapter 10: p. Unit 10.11.
24. Chen, Y., et al., Ensembl variation resources. BMC Genomics, 2010. 11 (1): p. 293.
25. Altschul, S. F., et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 1997. 25 (17): p. 3389-3402.
26. Jones, D. T., Protein secondary structure prediction based on position-specific scoring matrices. Journal of Molecular Biology, 1999. 292 (2): p. 195-202.
27. Cheng, J., et al., SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Research. 33 (suppl 2): p. W72-W76.
28. Ward, J. J., et al., Prediction and Functional Analysis of Native Disorder in Proteins from the Three Kingdoms of Life. Journal of Molecular Biology, 2004. 337 (3): p. 635-645.
29. Song, J., et al., HSEpred: predict half-sphere exposure from protein sequences. Bioinformatics, 2008. 24 (13): p. 1489-1497.
30. Song, J., et al., Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics, 2010. 26 (6): p. 752-760.
31. Schlessinger, A., et al., Improved Disorder Prediction by Combination of Orthogonal Approaches. Plos One, 2009. 4 (2): p. e4433.
32. Song, J., et al., Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure. Bioinformatics, 2007. 23 (23): p. 3147-3154.
33. Hubbard, S. J. A. T., J. M., 'NACCESS', computer program. Department Biochemistry and Molecular Biology, University College, London., 1993.
34. Fernandez-Escamilla, A.-M., et al., Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotech, 2004. 22 (10): p. 1302-1306.
35. Kabsch, W. and C. Sander, Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983. 22 (12): p. 2577-2637.
36. Sunyaev, S. R., et al., PSIC: profile extraction from sequence alignments with position-specific counts of independent observations. Protein Engineering, 1999. 12 (5): p. 387-394.
37. Ng, P. C. and S. Henikoff, Predicting deleterious amino acid substitutions. Genome Res, 2001. 11 (5): p. 863-74.
38. Bromberg, Y. and B. Rost, SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 2007. 35 (11): p. 3823-3835.
39. Adzhubei, I. A., et al., A method and server for predicting damaging missense mutations. Nat Meth, 2010. 7 (4): p. 248-249.
40. Li, W. and A. Godzik, Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006. 22 (13): p. 1658-1659.
41. Liaw, A. and M. Wiener, Classification and Regression by randomForest. R news, 2002. 2: p. 18-22.
42. Liu, Z.-P., et al., Prediction of protein-RNA binding sites by a random forest method with combined features. Bioinformatics, 2010. 26 (13): p. 1616-1622.
43. Chen, X.-W. and M. Liu, Prediction of protein-protein interactions using random decision forest framework. Bioinformatics, 2005. 21 (24): p. 4394-4400.
44. Wang, L., C. Huang, and J. Yang, Predicting siRNA potency with random forests and support vector machines. BMC Genomics, 2010. 11 (Suppl 3): p. S2.
45. Ebina, T., H. Toh, and Y. Kuroda, DROP: an SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics, 2011. 27 (4): p. 487-494.
46. Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. Software availabe at http://www.csie.ntu.edu.tw/~cjlin/libsvm. 2001.
47. Matthews, B. W., Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure, 1975. 405(2): p. 442-451.
48. The UniProt Consortium, The Universal Protein Resource (UniProt) 2009. Nucl. Acids Res., 2009. 37(suppl-1): p. D169-174.
49. Yip, Y. L., et al., Annotating single amino acid polymorphisms in the UniProt/Swiss-Prot knowledgebase. Human Mutation, 2008. 29 (3): p. 361-366.
50. Berman, H. M., et al., The Protein Data Bank. Nucl. Acids Res., 2000. 28 (1): p. 235-242.
51. Gong, S. and T. L. Blundell, Discarding functional residues from the substitution table improves predictions of active sites within three-dimensional structures. PLoS Comput Biol, 2008. 4 (10): p. e1000179.
52. Bailey, R. A., Design of Comparative Experiments. 2008, Cambridge: Cambridge University Press.
53. http://en. wikipedia.org/wiki/Analysis-of-variance.
54. Sing, T., et al., ROCR: visualizing classifier performance in R. Bioinformatics, 2005. 21 (20): p. 3940-3941.
55. Ng, P. C. and S. Henikoff, Accounting for human polymorphisms predicted to affect protein function. 2002. 12 (3): p. 436-46.
56. Ng, P. C. and S. Henikoff, SIFT: predicting amino acid changes that affect protein function. Nucl. Acids Res., 2003. 31 (13): p. 3812-3814.
57. Yue, P., E. Melamud, and J. Moult, SNPs3D: Candidate gene and SNP selection for association studies. BMC Bioinformatics, 2006. 7 (1): p. 166.