PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes

PLoS ONE

Volumn 7, Issue 10, 2012, Pages

  Li, Yunqi ^a   Fang, Jianwen ^a  

^a UNIVERSITY OF KANSAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SOLVENT;

ACCURACY; AREA UNDER THE CURVE; ARTICLE; COMPUTER MODEL; COMPUTER PROGRAM; POINT MUTATION; PREDICTION; PROTEIN ENGINEERING; PROTEIN SECONDARY STRUCTURE; PROTEIN STABILITY; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; REVERTANT; THERMOSTABILITY;

ALGORITHMS; MODELS, MOLECULAR; MUTATION; PROTEIN ENGINEERING; PROTEIN STABILITY; PROTEIN STRUCTURE, SECONDARY; PROTEINS; ROC CURVE;

EID: 84867539303     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0047247     Document Type: Article

References (52)

1. Dahiyat BI, (1999) In silico design for protein stabilization. Current Opinion in Biotechnology 10: 387-390.
2. Korkegian A, Black ME, Baker D, Stoddard BL, (2005) Computational thermostabilization of an enzyme. Science 308: 857-860.
3. Lazar GA, Marshall SA, Plecs JJ, Mayo SL, Desjarlais JR, (2003) Designing proteins for therapeutic applications. Curr Opin Struct Biol 13: 513-518.
4. Schweiker KL, Makhatadze GI (2009) Protein Stabilization by the Rational Design of Surface Charge-Charge Interactions. In: Shriver JW, editor. Protein Structure, Stability, and Interactions: Humana Press. pp. 261-283.
5. Sterner R, Liebl W, (2001) Thermophilic adaptation of proteins. Critical Reviews in Biochemistry and Molecular Biology 36: 39-106.
6. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL, (2009) Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci U S A 106: 11937-11942.
7. Unsworth LD, van der Oost J, Koutsopoulos S, (2007) Hyperthermophilic enzymes-stability, activity and implementation strategies for high temperature applications. FEBS J 274: 4044-4056.
8. Schoemaker HE, Mink D, Wubbolts MG, (2003) Dispelling the myths- Biocatalysis in industrial synthesis. Science 299: 1694-1697.
9. Frokjaer S, Otzen DE, (2005) Protein drug stability: a formulation challenge. Nat Rev Drug Discov 4: 298-306.
10. Lippow SM, Tidor B, (2007) Progress in computational protein design. Current Opinion in Biotechnology 18: 305-311.
11. Guerois R, Nielsen JE, Serrano L, (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320: 369-387.
12. Chan CH, Liang HK, Hsiao NW, Ko MT, Lyu PC, et al. (2004) Relationship between local structural entropy and protein thermostability. Proteins 57: 684-691.
13. Pokala N, Handel TM, (2005) Energy functions for protein design: adjustment with protein-protein complex affinities, models for the unfolded state, and negative design of solubility and specificity. J Mol Biol 347: 203-227.
14. Zhou H, 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.
15. Yin S, Ding F, Dokholyan NV, (2007) Modeling backbone flexibility improves protein stability estimation. Structure 15: 1567-1576.
16. Kellogg EH, Leaver-Fay A, Baker D, (2011) Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics 79: 830-838.
17. Capriotti E, Fariselli P, Casadio R, (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33: W306-310.
18. Cheng J, Randall A, Baldi P, (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62: 1125-1132.
19. Masso M, Vaisman II, (2008) Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis. Bioinformatics 24: 2002-2009.
20. Montanucci L, Fariselli P, Martelli PL, Casadio R, (2008) Predicting protein thermostability changes from sequence upon multiple mutations. Bioinformatics 24: I190-I195.
21. Wu LC, Lee JX, Huang HD, Liu BJ, Horng JT, (2009) An expert system to predict protein thermostability using decision tree. Expert Systems with Applications 36: 9007-9014.
22. Gromiha MM, Oobatake M, Sarai A, (1999) Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins. Biophysical Chemistry 82: 51-67.
23. Huang LT, Gromiha MM, (2009) Reliable prediction of protein thermostability change upon double mutation from amino acid sequence. Bioinformatics 25: 2181-2187.
24. Glyakina AV, Garbuzynskiy SO, Lobanov MY, Galzitskaya OV, (2007) Different packing of external residues can explain differences in the thermostability of proteins from thermophilic and mesophilic organisms. Bioinformatics 23: 2231-2238.
25. Capriotti E, Fariselli P, Rossi I, Casadio R, (2008) A three-state prediction of single point mutations on protein stability changes. BMC Bioinformatics 9 (Suppl 2): S6.
26. Li Y, Zhang J, Tai D, Russell Middaugh C, Zhang Y, et al. (2012) Prots: A fragment based protein thermo-stability potential. Proteins: Structure, Function, and Bioinformatics 80: 81-92.
27. Becktel WJ, Schellman JA, (1987) Protein stability curves. Biopolymers 26: 1859-1877.
28. Khan S, Vihinen M, (2010) Performance of protein stability predictors. Human Mutation 31: 675-684.
29. Sanchez IE, Tejero J, Gomez-Moreno C, Medina M, Serrano L, (2006) Point mutations in protein globular domains: Contributions from function, stability and misfolding. J Mol Biol 363: 422-432.
30. McGuffin LJ, Bryson K, Jones DT, (2000) The PSIPRED protein structure prediction server. Bioinformatics 16: 404-405.
31. Li Y, Middaugh CR, Fang J, (2010) A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants. BMC Bioinformatics 11: 62.
32. Potapov V, Cohen M, Schreiber G, (2009) Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel 22: 553-560.
33. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389-3402.
34. Kumar MD, Bava KA, Gromiha MM, Prabakaran P, Kitajima K, et al. (2006) ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res 34: D204-206.
35. Kabsch W, Sander C, (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577-2637.
36. Breiman L, (2001) Random Forests. Machine Learning 45: 5-32.
37. Wang L, Yang MQ, Yang JY, (2009) Prediction of DNA-binding residues from protein sequence information using random forests. BMC Genomics 10 (Suppl 1): S1.
38. Sikic M, Tomic S, Vlahovicek K, (2009) Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. PLoS Comput Biol 5: e1000278.
39. Li Y, Fang Y, Fang J, (2011) Predicting Residue-Residue Contacts Using Random Forest Models. Bioinformatics 27: 3379-3384.
40. Fang J, Koen YM, Hanzlik RP, (2009) Bioinformatic analysis of xenobiotic reactive metabolite target proteins and their interacting partners. BMC Chem Biol 9: 5.
41. Fang JW, Dong YH, Williams TD, Lushington GH, (2008) Feature selection in validating mass spectrometry database search results. J Bioinform Comput Biol 6: 223-240.
42. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, et al. (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43: 1947-1958.
43. Lobo JM, Jimenez-Valverde A, Real R, (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17: 145-151.
44. Hand DJ, (2009) Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning 77: 103-123.
45. Schweiker KL, Makhatadze GI, (2009) A Computational Approach for the Rational Design of Stable Proteins and Enzymes: Optimization of Surface Charge-Charge Interactions. Methods in Enzymology: Computer Methods, Vol 454, Pt A 454: 175-211.
46. Frenz CM, (2005) Neural network-based prediction of mutation-induced protein stability changes in staphylococcal nuclease at 20 residue positions. Proteins-Structure Function and Bioinformatics 59: 147-151.
47. Hirano S, Kamikubo H, Yamazaki Y, Kataoka M, (2005) Elucidation of information encoded in tryptophan 140 of staphylococcal nuclease. Proteins 58: 271-277.
48. Galzitskaya OV, Reifsnyder DC, Bogatyreva NS, Ivankov DN, Garbuzynskiy SO, (2008) More compact protein globules exhibit slower folding rates. Proteins: Structure, Function, and Bioinformatics 70: 329-332.
49. Shakhnovich BE, Deeds E, Delisi C, Shakhnovich E, (2005) Protein structure and evolutionary history determine sequence space topology. Genome Research 15: 385-392.
50. England JL, Shakhnovich BE, Shakhnovich EI, (2003) Natural selection of more designable folds: A mechanism for thermophilic adaptation. Proceedings of the National Academy of Sciences 100: 8727-8731.
51. Glyakina AV, Bogatyreva NS, Galzitskaya OV, (2011) Accessible Surfaces of Beta Proteins Increase with Increasing Protein Molecular Mass More Rapidly than Those of Other Proteins. PLoS One 6: e28464.
52. Niwa T, Ying BW, Saito K, Jin W, Takada S, et al. (2009) Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins. Proc Natl Acad Sci U S A 106: 4201-4206.