Abstract
A challenging problem in bioinformatics is the detection of residues that account for protein function specificity, not only in order to gain deeper insight in the nature of functional specificity but also to guide protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. The majority of the state-of-the art algorithms for this task use multiple sequence alignments (MSA’s) to identify residue positions conserved within- and divergent between- protein subfamilies. In this study, we focus on a recent method based on this approach called multi-RELIEF. We analyze and modify the two core parts of the method in order to improve its predictive performance. A parametric generalization of the popular RELIEF machine learning algorithm for weighting residues is introduced and incorporated in multi-RELIEF. The ensemble criterion of multi-RELIEF for merging the weights of multiple runs is simplified. Finally, the method used by multi-RELIEF for exploiting tertiary structure information is modified by incorporating prior information describing the confidence of the original scores assigned to residues. Extensive computational experiments on six real-life datasets show improvement of both robustness and detection capability of the new multi-RELIEF over the original method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bickel, P.J., Kechris, K.J., Spector, P.C., Wedemayer, G.J., Glazer, A.N.: Finding important sites in protein sequences. Proc. Natl. Acad. Sci. USA 99, 14764–14771 (2002)
Carro, A., Tress, M., de Juan, D., Pazos, F., Lopez-Romero, P., Del Sol, A., Valencia, A., Rojas, A.M.: Treedet: a web server to explore sequence space. Nucleic Acids Res. 35(web server issue), 99 (2006)
Chakrabarti, S., Panchenko, A.R.: Ensemble approach to predict specificity determinants: benchmarking and validation. BMC Bioinformatics 10, 207 (2009)
Del Sol Mesa, A., Pazos, F., Valencia, A.: Automatic methods for predicting functionally important residues. J. Mol. Biol. 326(4), 1289–1302 (2003)
Feenstra, K.A., Pirovano, W., Krab, K., Heringa, J.: Sequence harmony: detecting functional specificity from alignments. Nucleic Acids Res. 35(web server issue), W495–W498 (2007)
Gu, X.: A simple statistical method for estimating type-ii (cluster-specific) functional divergence of protein sequence. Mol. Biol. Evol. 23, 1937–1945 (2006)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Hannenhalli, S.S., Russell, R.B.: Analysis and prediction of functional sub-types from protein sequence alignments. J. Mol. Biol. 303(1), 61–76 (2000)
Kalinina, O.V., Gelfand, M.S., Russell, R.B.: Combining specificity determining and conserved residues improves functional site prediction. BMC Bioinformatics (2009)
Kalinina, O.V., Novichkov, P.S., Mironov, A.A., Gelfand, M.S., Rakhmaninova, A.B.: SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins. Nucleic Acids Res. 32(web server issue), W424–W428 (2004)
Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
Kuipers, R.K., Joosten, H.-J.J., Verwiel, E., Paans, S., Akerboom, J., van der Oost, J., Leferink, N.G., van Berkel, W.J., Vriend, G., Schaap, P.J.: Correlated mutation analyses on super-family alignments reveal functionally important residues. Proteins 76(3), 608–616 (2009)
Mihalek, I., Res, I., Lichtarge, O.: A family of evolution-entropy hybrid methods for ranking protein residues by importance. J. Mol. Biol. 336(5), 1265–1282 (2004)
Mirny, L.A., Gelfand, M.S.: Using orthologous and paralogous proteins to identify specificity-determining residues in bacterial transcription factors. J. Mol. Biol. 321(1), 7–20 (2002)
Moore, J.H., White, B.C.: Tuning relieff for genome-wide genetic analysis. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol. 4447, pp. 166–175. Springer, Heidelberg (2007)
Pirovano, W., Feenstra, K.A., Heringa, J.: Sequence comparison by sequence harmony identifies subtype specific functional sites. Nucleic Acids Res. 34, 6540–6548 (2006)
Provost, F., Kohavi, R.: Guest editors’ introduction: On applied research in machine learning. Machine Learning 30, 127–132 (1998)
Shenkin, P.S., Erman, B., Mastrandrea, L.D.: Information-theoretical entropy as a measure of sequence variability. Proteins 11(4), 297–313 (1991)
Sobolev, V., Sorokine, A., Prilusky, J., Abola, E.E., Edelman, M.: Automated analysis of interatomic contacts in proteins. Bioinformatics 15, 327–332 (1999)
Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988)
Whisstock, J.C., Lesk, A.M.: Prediction of protein function from protein sequence and structure. Quart. Rev. Biophys. 36(3), 307–340 (2003)
Ye, K., Feenstra, K.A., Heringa, J., IJzerman, A.P., Marchiori, E.: Multi-relief: a method to recognize specificity determining residues from multiple sequence alignments using a machine-learning approach for feature weighting. Bioinformatics 24(1), 18–25 (2008)
Ye, K., Lameijer, E.W., Beukers, M.W., IJzerman, A.P.: A two-entropies analysis to identify functional positions in the transmembrane region of class a g protein-coupled receptors. Proteins 63, 1018–1030 (2006)
Zhang, Y., Ding, C., Li, T.: Gene selection algorithm by combining relieff and mrmr. BMC Genomics 9(suppl. 2) (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marchiori, E. (2010). Improving Multi-Relief for Detecting Specificity Residues from Multiple Sequence Alignments. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-12211-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12210-1
Online ISBN: 978-3-642-12211-8
eBook Packages: Computer ScienceComputer Science (R0)