PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions.

ABSTRACT:

Background

Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide 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). T

SUBMITTER: Hertz T 

PROVIDER: S-EPMC1810314 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature