MOTIVATION: The flow of information within cellular pathways largely relies on specific protein-protein interactions. Discovering such interactions that are mostly mediated by peptide recognition modules (PRM) is therefore a fundamental step towards unravelling the complexity of varying pathways. Since peptides can be recognized by more than one PRM and high-throughput experiments are both time consuming and expensive, it would be preferable to narrow down all potential peptide ligands for one specific PRM by a computational method. We at first present Domain Interaction Footprint (DIF) a new approach to predict binding peptides to PRMs merely based on the sequence of the peptides. Second, we show that our method is able to create a multi-classification model that assesses the binding specificity of a given peptide to all examined PRMs at once. RESULTS: We first applied our approach to a previously investigated dataset of different SH3 domains and predicted their appropriate peptide ligands with an exceptionally high accuracy. This result outperforms all recent methods trained on the same dataset. Furthermore, we used our technique to build two multi-classification models (SH3 and PDZ domains) to predict the interaction preference between a peptide and every single domain in the corresponding domain family at once. Predicting the domain specificity most reliably, our proposed approach can be seen as a first step towards a complete multi-domain classification model comprised of all domains of one family. Such a comprehensive domain specificity model would benefit the quest for highly specific peptide ligands interacting solely with the domain of choice. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The flow of information within cellular pathways largely relies on specific protein-protein interactions. Discovering such interactions that are mostly mediated by peptide recognition modules (PRM) is therefore a fundamental step towards unravelling the complexity of varying pathways. Since peptides can be recognized by more than one PRM and high-throughput experiments are both time consuming and expensive, it would be preferable to narrow down all potential peptide ligands for one specific PRM by a computational method. We at first present Domain Interaction Footprint (DIF) a new approach to predict binding peptides to PRMs merely based on the sequence of the peptides. Second, we show that our method is able to create a multi-classification model that assesses the binding specificity of a given peptide to all examined PRMs at once. RESULTS: We first applied our approach to a previously investigated dataset of different SH3 domains and predicted their appropriate peptide ligands with an exceptionally high accuracy. This result outperforms all recent methods trained on the same dataset. Furthermore, we used our technique to build two multi-classification models (SH3 and PDZ domains) to predict the interaction preference between a peptide and every single domain in the corresponding domain family at once. Predicting the domain specificity most reliably, our proposed approach can be seen as a first step towards a complete multi-domain classification model comprised of all domains of one family. Such a comprehensive domain specificity model would benefit the quest for highly specific peptide ligands interacting solely with the domain of choice. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.