EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models

Journal of Molecular Biology

Volumn 428, Issue 6, 2016, Pages 1394-1405

  Folkman, Lukas ^a,b   Stantic, Bela ^a   Sattar, Abdul ^a,b   Zhou, Yaoqi ^a  

^a GRIFFITH UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

amino acid substitution; free energy change; machine learning; missense mutation; non synonymous SNV

Indexed keywords

MONOMER; PROTEIN;

AMINO ACID SEQUENCE; AMINO ACID SUBSTITUTION; ARTICLE; CORRELATION COEFFICIENT; GENETIC VARIABILITY; GERMLINE MUTATION; MUTATIONAL ANALYSIS; PREDICTIVE VALUE; PRIORITY JOURNAL; PROTEIN DOMAIN; PROTEIN ENGINEERING; PROTEIN SECONDARY STRUCTURE; PROTEIN STABILITY; SUPPORT VECTOR MACHINE; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; GENETICS; HUMAN; MISSENSE MUTATION; PROCEDURES;

AMINO ACID SUBSTITUTION; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, MOLECULAR; MUTATION, MISSENSE; PROTEIN ENGINEERING; PROTEIN STABILITY; PROTEINS;

EID: 84956619015     PISSN: 00222836     EISSN: 10898638     Source Type: Journal    
DOI: 10.1016/j.jmb.2016.01.012     Document Type: Article

References (49)

1. P. Yue, Z. Li, and J. Moult Loss of protein structure stability as a major causative factor in monogenic disease J. Mol. Biol. 353 2 2005 459 473
2. A. Benedix, C. Becker, B. de Groot, A. Caflisch, and R. Bockmann Predicting free energy changes using structural ensembles Nat. Methods 6 1 2009 3 4
3. Y. Dehouck, A. Grosfils, B. Folch, D. Gilis, P. Bogaerts, and M. Rooman Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 Bioinformatics 25 19 2009 2537
4. R. Guerois, J. Nielsen, and L. Serrano Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations J. Mol. Biol. 320 2 2002 369 387
5. E. Kellogg, A. Leaver-Fay, and D. Baker Role of conformational sampling in computing mutation-induced changes in protein structure and stability Proteins Struct. Funct. Bioinf. 79 2011 830 838
6. S. Yin, F. Ding, and N. Dokholyan Eris: An automated estimator of protein stability Nat. Methods 4 6 2007 466 467
7. H. Zhou, and Y. Zhou Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction Protein Sci. 11 11 2002 2714 2726
8. E. Capriotti, P. Fariselli, and R. Casadio I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure Nucleic Acids Res. 33 Suppl. 2 2005 W306 W310
9. J. Cheng, A. Randall, and P. Baldi Prediction of protein stability changes for single-site mutations using support vector machines Proteins Struct. Funct. Bioinf. 62 4 2006 1125 1132
10. L. Folkman, B. Stantic, and A. Sattar Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants BMC Bioinformatics 14 Suppl. 2 2013 S6
11. L. Huang, M. Gromiha, and S. Ho iPTREE-STAB: Interpretable decision tree based method for predicting protein stability changes upon mutations Bioinformatics 23 10 2007 1292
12. S. Khan, and M. Vihinen Performance of protein stability predictors Hum. Mutat. 1 1 2010 675
13. L. Folkman, B. Stantic, and A. Sattar Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins BMC Genomics 15 Suppl. 1 2014 S4
14. L. Folkman, B. Stantic, and A. Sattar Feature-based multiple models improve classification of mutation-induced stability changes BMC Genomics 15 Suppl. 4 2014 S6
15. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 3 1995 273 297
16. R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma, J. Wang, A. Sattar, Y. Yang, and Y. Zhou Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning Sci. Rep. 5 2015 11476
17. P. Pudil, J. Novovicova, and J. Kittler Floating search methods in feature selection Pattern Recogn. Lett. 15 11 1994 1119 1125
18. E.J. Williams The comparison of regression variables J. R. Stat. Soc. Ser. B Methodol. 1959 396 399
19. W. Kabsch, and C. Sander Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features Biopolymers 22 12 1983 2577 2637
20. H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne The protein data bank Nucleic Acids Res. 28 1 2000 235 242
21. M.J. Landrum, J.M. Lee, G.R. Riley, W. Jang, W.S. Rubinstein, D.M. Church, and D.R. Maglott Clinvar: public archive of relationships among sequence variation and human phenotype Nucleic Acids Res. 42 D1 2014 D980 D985
22. The 1000 Genomes Project Consortium A map of human genome variation from population-scale sequencing Nature 467 7319 2010 1061 1073
23. Z. Wang, and J. Moult SNPs, protein structure, and disease Hum. Mutat. 17 4 2001 263 270
24. B.M. Beadle, and B.K. Shoichet Structural bases of stability - function tradeoffs in enzymes J. Mol. Biol. 321 2 2002 285 296
25. P. Zavodszky, J. Kardos, A. Svingor, and G.A. Petsko Adjustment of conformational flexibility is a key event in the thermal adaptation of proteins Proc. Nat. Acad. Sci. 95 13 1998 7406 7411
26. R. Casadio, M. Vassura, S. Tiwari, P. Fariselli, and P. Luigi Martelli Correlating disease-related mutations to their effect on protein stability: A large-scale analysis of the human proteome Hum. Mutat. 32 10 2011 1161 1170
27. G.T. Marth, F. Yu, A.R. Indap, K. Garimella, S. Gravel, W.F. Leong, C. Tyler-Smith, M. Bainbridge, T. Blackwell., X. Zheng-Bradley, and et al. The functional spectrum of low-frequency coding variation Genome Biol. 12 9 2011 R84
28. I.A. Adzhubei, S. Schmidt, L. Peshkin, V.E. Ramensky, A. Gerasimova, P. Bork, A.S. Kondrashov, and S.R. Sunyaev A method and server for predicting damaging missense mutations Nat. Methods 7 4 2010 248 249
29. L. Folkman, Y. Yang, Z. Li, B. Stantic, A. Sattar, M. Mort, D.N. Cooper, Y. Liu, and Y. Zhou DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels Bioinformatics 31 10 2015 1599 1606
30. B. Li, V.G. Krishnan, M.E. Mort, F. Xin, K.K. Kamati, D.N. Cooper, S.D. Mooney, and P. Radivojac Automated inference of molecular mechanisms of disease from amino acid substitutions Bioinformatics 25 21 2009 2744 2750
31. D.G. Grimm, C.-A. Azencott, F. Aicheler, U. Gieraths, D.G. MacArthur, K.E. Samocha, D.N. Cooper, P.D. Stenson, M.J. Daly, J.W. Smoller, L.E. Duncan, and K.M. Borgwardt The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity Hum. Mutat. 36 5 2015 513 523
32. Y. Dehouck, J.M. Kwasigroch, M. Rooman, and D. Gilis BeAtMuSiC: prediction of changes in protein-protein binding affinity on mutations Nucleic Acids Res. 41 W1 2013 W333 W339
33. T.G. Kucukkal, M. Petukh, L. Li, and E. Alexov Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins Curr. Opin. Struct. Biol. 32 2015 18 24
34. M. Petukh, T.G. Kucukkal, and E. Alexov On human disease-causing amino acid variants: statistical study of sequence and structural patterns Hum. Mutat. 36 5 2015 524 534
35. M. Kumar, K. Bava, M. Gromiha, P. Prabakaran, K. Kitajima, H. Uedaira, and A. Sarai ProTherm and ProNIT: Thermodynamic databases for proteins and protein-nucleic acid interactions Nucleic Acids Res. 34 Suppl. 1 2006 D204
36. S. Altschul, T. Madden, A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D. Lipman Gapped BLAST and PSI-BLAST: A new generation of protein database search programs Nucleic Acids Res. 25 17 1997 3389
37. L.A. Mirny, and E.I. Shakhnovich Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function J. Mol. Biol. 291 1 1999 177 196
38. J.A. Capra, and M. Singh Predicting functionally important residues from sequence conservation Bioinformatics 23 15 2007 1875 1882
39. L. Huang, K. Saraboji, S. Ho, S. Hwang, M. Ponnuswamy, and M. Gromiha Prediction of protein mutant stability using classification and regression tool Biophys. Chem. 125 2-3 2007 462 470
40. S. Kang, G. Chen, and G. Xiao Robust prediction of mutation-induced protein stability change by property encoding of amino acids Protein Eng. Des. Sel. 22 2 2009 75
41. B. Shen, J. Bai, and M. Vihinen Physicochemical feature-based classification of amino acid mutations Protein Eng. Des. Sel. 21 1 2008 37 44
42. S. Teng, A. Srivastava, and L. Wang Sequence feature-based prediction of protein stability changes upon amino acid substitutions BMC Genomics 11 Suppl. 2 2010 S5
43. J. Meiler, M. Muller, A. Zeidler, and F. Schmaschke Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks Mol. Model. Annu. 7 9 2001 360 369
44. M.M. Gromiha, M. Oobatake, H. Kono, H. Uedaira, and A. Sarai Relationship between amino acid properties and protein stability: buried mutations J. Protein Chem. 18 5 1999 565 578
45. M. Vihinen, E. Torkkila, and P. Riikonen Accuracy of protein flexibility predictions Proteins Struct. Funct. Bioinf. 19 2 1994 141 149
46. T. Zhang, E. Faraggi, B. Xue, A.K. Dunker, V.N. Uversky, and Y. Zhou SPINE-D: Accurate prediction of short and long disordered regions by a single neural-network based method J. Biomol. Struct. Dyn. 29 4 2012 799 813
47. S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman Basic local alignment search tool Journal of Molecular Biology 215 3 1990 403 410
48. C.-C. Chang, and C.-J. Lin LIBSVM: A library for support vector machines ACM Trans. Intell. Syst. Technol. 2 3 2011 27:1 27:27
49. O. Tange GNU Parallel - the command-line power tool,;login The USENIX Magazine 36 2011 42 47 (1)