Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Author:

Antunes Dinler A.1,Abella Jayvee R.1,Devaurs Didier1,Rigo Maurício M.2,Kavraki Lydia E.1

Affiliation:

1. Computer Science Department, Rice University, Houston, TX, United States

2. School of Medicine, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

Abstract

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Publisher

Bentham Science Publishers Ltd.

Subject

Drug Discovery,General Medicine

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