Probabilistic in silico prediction of protein-peptide interactions

Peptide recognition modules (PRMs) are specialised com- pact protein domains that mediate many important protein-protein interactions. They are responsible for the assembly of critical macromolec- ular complexes and biochemical pathways [Pawson and Scott, 1997], and they have been implicated in carcinogenesis and various other human diseases [Sudol and Hunter, 2000]. PRMs recognise and bind to peptide ligands that contain a specific structural motif. This paper introduces a novel discriminative model which models these PRMs and allows prediction of their behaviour, which we compare with a recently proposed generative model. We find that on a yeast two-hybrid dataset, the generative model performs better when background sequences are included, while our discriminative model performs better when the evaluation is focused on discriminating between the SH3 domains. Our model is also evaluated on a phage display dataset, where it consistently out-performed the generative model