Antunes, Dinler A.Abella, Jayvee R.Devaurs, DidierRigo, Maurício M.Kavraki, Lydia E.2019-12-122019-12-122018Antunes, Dinler A., Abella, Jayvee R., Devaurs, Didier, et al.. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes." <i>Current Topics in Medicinal Chemistry,</i> 18, no. 26 (2018) Bentham Science: 2239-2255. https://doi.org/10.2174/1568026619666181224101744.https://hdl.handle.net/1911/107872Understanding 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.engThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Bentham ScienceJournal articleT-cell activationBinding affinity predictionBinding mode predictionImmunogenicityMolecular dockingPeptide- MHC complexesnihms-1008660https://doi.org/10.2174/1568026619666181224101744