The Community for Technology Leaders
RSS Icon
Issue No.04 - October-December (2010 vol.7)
pp: 681-687
Liang-Tsung Huang , Mingdao University, Changhua
Lien-Fu Lai , National Changhua University of Education, Changhua
M. Michael Gromiha , National Institute of Advanced Industrial Science and Technology (AIST), Tokyo
Most of the bioinformatics tools developed for predicting mutant protein stability appear as a black box and the relationship between amino acid sequence/structure and stability is hidden to the users. We have addressed this problem and developed a human-readable rule generator for integrating the knowledge of amino acid sequence and experimental stability change upon single mutation. Using information about the original residue, substituted residue, and three neighboring residues, classification rules have been generated to discriminate the stabilizing and destabilizing mutants and explore the basis for experimental data. These rules are human readable, and hence, the method enhances the synergy between expert knowledge and computational system. Furthermore, the performance of the rules has been assessed on a nonredundant data set of 1,859 mutants and we obtained an accuracy of 80 percent using cross validation. The results showed that the method could be effectively used as a tool for both knowledge discovery and predicting mutant protein stability. We have developed a Web for classification rule generator and it is freely available at
Protein stability, prediction, classification rule, data mining.
Liang-Tsung Huang, Lien-Fu Lai, M. Michael Gromiha, "Human-Readable Rule Generator for Integrating Amino Acid Sequence Information and Stability of Mutant Proteins", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.7, no. 4, pp. 681-687, October-December 2010, doi:10.1109/TCBB.2008.128
[1] B.A. Shirley, Protein Stability and Folding: Theory and Practice. Humana Press, 1995.
[2] S.A. Petty, T. Adalsteinsson, and S.M. Decatur, "Correlations among Morphology, Beta-Sheet Stability, and Molecular Structure in Prion Peptide Aggregates," Biochemistry, vol. 44, pp. 4720-4726, 2005.
[3] A.T. Lorincz and S.I. Reed, "Sequence Analysis of Temperature-Sensitive Mutations in the Saccharomyces Cerevisiae Gene CDC28," Molecular and Cellular Biology, vol. 6, pp. 4099-4103, 1986.
[4] N. Dissmeyer, "Control of the Cyclin-Dependent Kinase CDKA;1 in the Cell Cycle of Arabidopsis Thaliana," diploma thesis, Univ. of Cologne, Cologne, 2005.
[5] K. Saraboji, M.M. Gromiha, and M.N. Ponnuswamy, "Average Assignment Method for Predicting the Stability of Protein Mutants," Biopolymers, vol. 82, pp. 80-92, 2006.
[6] E. Capriotti, P. Fariselli, and R. Casadio, "I-Mutant2.0: Predicting Stability Changes upon Mutation from the Protein Sequence or Structure," Nucleic Acids Research, vol. 33, pp. W306-W310, 2005.
[7] L.T. Huang, M.M. Gromiha, and S.Y. Ho, "iPTREE-STAB: Interpretable Decision Tree Based Method for Predicting Protein Stability Changes upon Mutations," Bioinformatics, vol. 23, pp. 1292-1293, 2007.
[8] J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
[9] Y. Freund and R.E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Computer and System Sciences, vol. 55, pp. 119-139, 1997.
[10] M.M. Gromiha, J. An, H. Kono, M. Oobatake, H. Uedaira, and A. Sarai, "ProTherm: Thermodynamic Database for Proteins and Mutants," Nucleic Acids Research, vol. 27, pp. 286-288, 1999.
[11] K.A. Bava, M.M. Gromiha, H. Uedaira, K. Kitajima, and A. Sarai, "ProTherm, Version 4.0: Thermodynamic Database for Proteins and Mutants," Nucleic Acids Research, vol. 32, pp. D120-D121, 2004.
[12] A. Bairoch, R. Apweiler, C.H. Wu, W.C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M.J. Martin, D.A. Natale, C. O'Donovan, N. Redaschi, and L.S. Yeh, "The Universal Protein Resource (UniProt)," Nucleic Acids Research, vol. 33, pp. D154-D159, 2005.
[13] J.R. Quinlan, "Induction of Decision Trees," Machine Learning, vol. 1, pp. 81-106, 1986.
[14] T.M. Mitchell, Machine Learning. McGraw-Hill, 1997.
[15] J. Khatun, S.D. Khare, and N.V. Dokholyan, "Can Contact Potentials Reliably Predict Stability of Proteins?" J. Molecular Biology, vol. 336, pp. 1223-1238, 2004.
[16] E. Capriotti, P. Fariselli, and R. Casadio, "A Neural-Network-Based Method for Predicting Protein Stability Changes upon Single Point Mutations," Bioinformatics, vol. 20, no. 1, pp. I63-I68, 2004.
[17] J. Cheng, A. Randall, and P. Baldi, "Prediction of Protein Stability Changes for Single-Site Mutations Using Support Vector Machines," Proteins, vol. 62, pp. 1125-1132, 2006.
[18] V. Parthiban, M.M. Gromiha, and D. Schomburg, "CUPSAT: Prediction of Protein Stability upon Point Mutations," Nucleic Acids Research, vol. 34, pp. W239-W242, 2006.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool