INPS: Predicting the impact of non-synonymous variations on protein stability from sequence

Bioinformatics

Volumn 31, Issue 17, 2015, Pages 2816-2821

  Fariselli, Piero ^a   Martelli, Pier Luigi ^a   Savojardo, Castrense ^a   Casadio, Rita ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN; PROTEIN P53; TP53 PROTEIN, HUMAN;

ALGORITHM; BIOLOGY; CHEMISTRY; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; MACHINE LEARNING; MUTATION; PROCEDURES; PROTEIN ENGINEERING; PROTEIN STABILITY; SOFTWARE; THERMODYNAMICS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MACHINE LEARNING; MUTATION; PROTEIN ENGINEERING; PROTEIN STABILITY; PROTEINS; SOFTWARE; THERMODYNAMICS; TUMOR SUPPRESSOR PROTEIN P53;

EID: 84940765456     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv291     Document Type: Article

References (32)

1. Capriotti E., et al. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res., 33, 306-310
2. Capriotti E., et al. (2008). A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics, 26, S6
3. Capriotti E., et al. (2012). Bioinformatics for personal genome interpretation. Brief Bioinform., 13, 495-512
4. Casadio R., et al. (2011). Correlating disease-related mutations to their effect on protein stability: A large-scale analysis of the human proteome. Hum. Mutat., 2, 1161-1170
5. Chang C.C., et al. (2011). LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 1-27
6. Chen C.W., et al. (2013). iStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformatics, 14, S5
7. Cheng J., et al. (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 62, 1125-1132
8. Dehouck Y., et al. (2009). Fast, and accurate predictions of protein stability changes upon mutations using statistical potentials, and neural networks: PoPMuSiC-2.0. Bioinformatics, 25, 2537-2543
9. Dayhoff M.O., et al. (1978). A model of evolutionary change in proteins. Atlas Protein Sequence Struct., 5, 3
10. Eddy S.R. (1998). Profile hiddenMarkov models. Bioinformatics, 14, 755-763
11. Eddy S.R. (2011). Accelerated profile HMM searches. PLoS Comp. Biol., 7, e1002195
12. Giollo M., et al. (2014). NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation. BMC Genomics, 15, S7
13. Guerois R., et al. (2002). Predicting changes in the stability of proteins, and protein complexes: a study of more than 1000 mutations. J. Mol. Biol., 320, 369-387
14. Henikoff S., and Henikoff J.G. (1992). Amino acid substitution matrices from protein blocks. PNAS, 89, 10915-10919
15. Huang L.T., et al. (2007). iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics, 23, 1292-1293
16. Hudson T.J., et al. (2012). International network of cancer genome projects. Nature, 464, 993-998
17. Khan S., and Vihinen M. (2010). Performance of protein stability predictors. Hum. Mutat., 31, 675-684
18. Kumar M.S., et al. (2006). Protherm, and pronit: thermodynamic databases for proteins, and protein-nucleic acid interactions. Nucleic Acids Res., 34, D204-D206
19. Kyte J., and Doolittle R.F. (1982). A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 157, 105-132
20. Lahti J.L.T, et al. (2012) Bioinformatics, and variability in drug response: a protein structural perspective. J. R. Soc. Interface, 9, 1409-1437
21. Masso M., and Vaisman I. (2008). Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis. Bioinformatics, 24, 2002-2009
22. Parthiban V., et al. (2006). CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res., 34, 239-242
23. Pires D.E.V., et al. (2014a). mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 30, 335-342
24. Pires D.E.V., et al. (2014b). DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res., 42, W314-W319
25. Rahman N.A. (1968). A Course in Theoretical Statistics. Charles Griffin, and Company, UK
26. Stratton M.R., et al. (2012). The cancer genome. Nature, 458, 719-724
27. Topham C.M., et al. (1997). Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution, and propensity tables. Protein Eng., 10, 7-21
28. Teng S., et al. (2010). Sequence feature-based prediction of protein stability changes upon amino acid substitutions. BMC Genomics, 11, 5
29. Wainreb G. (2011). Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site. Bioinformatics, 27, 3286-3292
30. Worth C.L., et al. (2011). SDM-a server for predicting effects of mutations on protein stability, and malfunction. Nucleic Acids Res., 39, W215-W222
31. Yin S., et al. (2007). Eris: an automated estimator of protein stability. Nat. Methods, 4, 466-467
32. Zhou H., and Zhou Y. (2002). Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection, and stability prediction. Protein Sci., 11, 2714-2726