Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions

Many important cellular response mechanisms are activated when a peptide binds to an appropriate receptor. In the immune system, the recognition of pathogen peptides begins when they bind to cell membrane Major Histocompatibility Complexes (MHCs). MHC proteins then carry these peptides to the cell surface in order to allow the activation of cytotoxic T-cells. The MHC binding cleft is highly polymorphic and therefore protein-peptide binding is highly specific. Developing computational methods for predicting protein-peptide binding is important for vaccine design and treatment of diseases like cancer. Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose a novel approach for predicting binding affinity. Our approach is based on learning a peptide-peptide distance function. Moreover, we learn a single peptide-peptide distance function over an entire family of proteins (e.g MHC class I). This distance...

• Type: Conference
• Year: 2005
• Where: RECOMB
• Authors: Chen Yanover, Tomer Hertz
• Keywords: Cellular Response Mechanisms, Computational Biology, MHC Class, Peptide-peptide Distance Function, RECOMB 2005