Knowledge acquisition and development of accurate rules for predicting protein stability changes

Computational Biology and Chemistry

Volumn 30, Issue 6, 2006, Pages 408-415

  Huang, Liang Tsung ^a   Gromiha, M Michael ^b   Hwang, Shiow Fen ^a   Ho, Shinn Ying ^c  

^a FENG CHIA UNIVERSITY   (Taiwan)

^b NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^c NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Bioinformatics; Data mining; Decision trees; Prediction; Protein stability

Indexed keywords

DATA MINING; DATABASE SYSTEMS; DECISION THEORY; MOLECULAR BIOLOGY; NEURAL NETWORKS; PROTEINS; TREES (MATHEMATICS);

BIOINFORMATICS; DECISION TREES; PREDICTION; PROTEIN STABILITY;

KNOWLEDGE ACQUISITION;

PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; COMPUTER PROGRAM; COMPUTER SIMULATION; GENETICS; HUMAN; METHODOLOGY; POINT MUTATION; PROTEIN DATABASE; SEQUENCE ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, PROTEIN; HUMANS; POINT MUTATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 33751311798     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2006.06.004     Document Type: Article

References (23)

1. Capriotti E., Fariselli P., and Casadio R. A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics 20 Suppl. 1 (2004) I63-I68
2. Capriotti E., Fariselli P., and Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33 Web Server issue (2005) W306-W310
3. Daggett V., and Fersht A.R. Is there a unifying mechanism for protein folding?. Trends Biochem. Sci. 28 (2003) 18-25
4. Deber C.M., Khan A.R., Li Z., Joensson C., Glibowicka M., and Wang J. Val → Ala mutations selectively alter helix-helix packing in the transmembrane segment of phage M13 coat protein. Proc. Natl. Acad. Sci. U.S.A. 90 24 (1993) 11648-11652
5. el-Bastawissy E., Knaggs M.H., and Gilbert I.H. Molecular dynamics simulations of wild-type and point mutation human prion protein at normal and elevated temperature. J. Mol. Graph. Model. 20 2 (2001) 145-154
6. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 (1997) 119-139
7. Funahashi J., Takano K., and Yutani K. Are the parameters of various stabilization factors estimated from mutant human lysozymes compatible with other proteins?. Protein Eng. 14 (2001) 127-134
8. Geurts P., Fillet M., de Seny D., Meuwis M.A., Malaise M., Merville M.P., and Wehenkel L. Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics 21 14 (2005) 3138-3145
9. Gilis D., and Rooman M. Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence. J. Mol. Biol. 272 (1997) 276-290
10. Gromiha M.M., Oobatake M., Kono H., Uedaira H., and Sarai A. Relationship between amino acid properties and protein stability: buried mutations. J. Protein Chem. 18 (1999) 565-578
11. Gromiha M.M., An J., Kono H., Oobatake M., Uedaira H., Prabakaran P., and Sarai A. ProTherm, version 2.0: thermodynamic database for proteins and mutants. Nucleic Acids Res. 28 (2000) 283-285
12. Gromiha M.M., and Selvaraj S. Important amino acid properties for determining the transition state structures of two-state protein mutants. FEBS Lett. 526 (2002) 129-134
13. Guerois R., Nielsen J.E., and Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol. 320 (2002) 369-387
14. Ho S.-Y., Chang C.-Y., Huang L.-T., Huang W.-L., and Hwang S.-F. Accurate prediction and analysis of DNA-binding proteins using a rule-based decision tree system. Proceedings of 12th International Conference on BioMedical Engineering: ICBME 2005. Singapore (2005) p. 108
15. Kabsch W., and Sander C. Dictionary of protein secondary structure: pattern of hydrogen-bonded and geometrical features. Biopolymers 22 (1983) 2577-2637
16. Prevost M., Wodak S.J., Tidor B., and Karplus M. Contribution of the hydrophobic effect to protein stability: analysis based on simulations of the Ile-96-Ala mutation in barnase. Proc. Natl. Acad. Sci. U.S.A. 88 (1991) 10880-10884
17. Quinlan J.R. Induction of decision trees. Machine Learn. 1 1 (1986) 81-106
18. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Francisco
19. Robertson A.D., and Murphy K.P. Protein structure and the energetics of protein stability. Chem. Rev. 97 (1997) 1251-1267
20. Saraboji K., Gromiha M.M., and Ponnuswamy M.N. Relative importance of secondary structure and solvent accessibility to the stability of protein mutants. A case study with amino acid properties and energetics on T4 and human lysozymes. Comput. Biol. Chem. 29 1 (2005) 25-35
21. Vivian J.P., Hastings A.F., Duggin I.G., Wake R.G., Wilce M.C., and Wilce J.A. The impact of single cysteine residue mutations on the replication terminator protein. Biochem. Biophys. Res. Commun. 310 4 (2003) 1096-1103
22. Zhou H., and Zhou Y. Quantifying the effect of burial of amino acid residues on protein stability. Proteins 54 (2004) 315-322
23. Zscherp C., Aygun H., Engels J.W., and Mantele W. Effect of proline to alanine mutation on the thermal stability of the all-beta-sheet protein tendamistat. Biochim. Biophys. Acta 1651 1-2 (2003) 139-145