Bioinformatics Advance Access originally published online on December 13, 2005
Bioinformatics 2006 22(4):453-459; doi:10.1093/bioinformatics/bti826
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A simple method to predict protein-binding from aligned sequences—application to MHC superfamily and ß2-microglobulin
1Laboratoire d'ImmunoGénétique Moléculaire IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France
2Institut Universitaire de France 103 Boulevard Saint-Michel, 75005 Paris, France
3Projet Méthodes et Algorithmes pour la Bioinformatique LIRMM (UMR CNRS-UM2 5506), 161 rue Ada, 34392 Montpellier Cedex 5, France
*To whom correspondence should be addressed.
Motivation: The MHC superfamily (MhcSF) consists of immune system MHC class I (MHC-I) proteins, along with proteins with a MHC-I-like structure that are involved in a large variety of biological processes. ß2-Microglobulin (B2M) non-covalent binding to MHC-I proteins is required for their surface expression and function, whereas MHC-I-like proteins interact, or not, with B2M. This study was designed to predict B2M binding (or non-binding) of newly identified MhcSF proteins, in order to decipher their function, understand the molecular recognition mechanisms and identify deleterious mutations. IMGT standardization of MhcSF protein domains provides a unique numbering of the multiple alignment positions, and conditions to develop such predictive tool.
Method: We combine a simple-Bayes classifier with IMGT unique numbering. Our method involves two steps: (1) selection of discriminant binary features, which associate an alignment position with an amino acid group; and (2) learning of the classifier by estimating the frequencies of selected features, conditionally to the B2M binding property.
Results: Our dataset contains aligned sequences of 806 allelic forms of 47 MhcSF proteins, corresponding to 9 receptor types and 4 mammalian species. Eighteen discriminant features are selected, belonging to B2M contact sites, or stabilizing the molecular structure required for this contact. Three leave-one-out procedures are used to assess classifier performance, which corresponds to B2M binding prediction for: (1) new proteins, (2) species not represented in the dataset and (3) new receptor types. The prediction accuracy is high, i.e. 98, 94 and 70%, respectively. Application of our classifier to lower vertebrate MHC-I proteins indicates that these proteins bind to B2M and should then be expressed on the cellular surface by a process similar to that of mammalian MHC-I proteins. These results demonstrate the usefulness and accuracy of our (simple) approach, which should apply to other function or interaction prediction problems.
Availability: Data and MhcSF multiple alignments are available on the IMGT website (http://imgt.cines.fr).
Contact: gascuel{at}lirmm.fr, duprat{at}ligm.igh.cnrs.fr, lefranc{at}ligm.igh.cnrs.fr
Supplementary information: Supplementary material is downloadable at http://imgt.igh.cnrs.fr/MhcSF-B2M.html.
Received on August 3, 2005; revised on December 7, 2005; accepted on December 7, 2005
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