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

• Published in: Current topics in medicinal chemistry Vol. 18; no. 26; p. 2239
• Main Authors: Antunes, Dinler A, Abella, Jayvee R, Devaurs, Didier, Rigo, Maurício M, Kavraki, Lydia E
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01-01-2018
• Subjects: Binding Sites; HLA Antigens - chemistry; Humans; Peptides - chemistry; Structure-Activity Relationship; Binding affinity prediction; Binding mode prediction; Immunogenicity; Peptide- MHC complexes; Molecular docking; T-cell activation
• ISSN: 1873-4294
• DOI: 10.2174/1568026619666181224101744

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.