Motivation: Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides.#R##N##R##N#Results: The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule.#R##N##R##N#Availability: The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.#R##N##R##N#Contact: massimo@cbs.dtu.dk#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online.
['Massimo Andreatta', 'Ole Lund', 'Morten Nielsen']
Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach