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Bioinformatics Advance Access published online on December 13, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti826
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 3, 2005
Revised December 7, 2005
Accepted December 7, 2005

Article

A simple method to predict protein binding from aligned sequences -- application to MHC superfamily and beta2-microglobulin

Elodie Duprat 1, Marie-Paule Lefranc 2, and Olivier Gascuel 3 *

1 Laboratoire d'ImmunoGénétique Moléculaire, IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France
2 Laboratoire d'ImmunoGénétique Moléculaire, IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France; Institut Universitaire de France, 103 Boulevard Saint-Michel, 75005 Paris, France
3 Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM (UMR CNRS 5506), 161 rue Ada, 34392 Montpellier, France

* To whom correspondence should be addressed.
Olivier Gascuel, E-mail: gascuel{at}lirmm.fr


  Abstract

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. Beta2-microglobulin (B2M) noncovalent binding to MHC-I proteins is required for their surface expression and function, while 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; (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. 18 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 alignment are available on the IMGT website (http://imgt.cines.fr), and supplementary material is downloadable at http://imgt.igh.cnrs.fr/MhcSF-B2M.html.


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