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

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

Volume: 18 Issue: 26

Author(s): Dinler A. Antunes*, Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo and Lydia E. Kavraki*

Affiliation:

• Computer Science Department, Rice University, Houston, TX,United States

• Computer Science Department, Rice University, Houston, TX,United States

Keywords: T-cell activation, Binding affinity prediction, Binding mode prediction, Immunogenicity, Molecular docking, Peptide- MHC complexes.

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.

Cite this article as:

Antunes A. Dinler *, Abella R. Jayvee , Devaurs Didier, Rigo M. Maurício and Kavraki E. Lydia *, Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes, Current Topics in Medicinal Chemistry 2018; 18 (26) . https://dx.doi.org/10.2174/1568026619666181224101744