PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

Background: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHCpeptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design. Methods: Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a se...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tomer Hertz, Chen Yanover
• Keywords: Binding Prediction, BMCBI 2006, MHC Class, Peptide-peptide Distance Functions