MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides.

ABSTRACT: Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we describe a complementary approach called MHCII3D, which is based on structural scaffolds of MHC II-peptide complexes and statistical scoring functions (SSFs). The MHC II alleles reported in the Immuno Polymorphism Database are processed in a dedicated 3D-modeling pipeline providing a set of scaffold complexes for each distinct allotype sequence. Antigen protein sequences are threaded through the scaffolds and evaluated by optimized SSFs. We compared the predictive power of MHCII3D w

SUBMITTER: Laimer J 

PROVIDER: S-EPMC7792572 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature