Pan-Prediction of MHC-II Restricted Epitopes Across Species via an Alphafold-based Quantification Scheme
Wang, S.; Kong, L.; Hu, D.; Zheng, L.; Fei, C.; Du, L.; Tang, Z.; Wang, S.; Xu, S.; Yang, H.; Zhang, N.
Show abstract
Predicting MHC-II restricted epitopes across species used to be challenging, but Alphafold (AF) may provide a structure-based pan-prediction solution. In this study, we established the new tool AF-pred with a clear standard for quantitative prediction results. Compared to the sequence-based tools heavily trained with human ligandome, AF-pred does not show advantage in predicting the binding patterns of human HLA-II but has far better performance in predicting binding patterns of other animals MHC-II. Using recently resolved bat MHC-II structures, we analyzed AF-preds prediction capability, logic and limitation. In addition, we also explored the impact of AF algorithm iterations on the prediction of MHC-II restricted epitopes. The results demonstrated that AF-pred is capable of cross-species prediction of MHC-II restricted epitopes and is conducive to the development of novel veterinary vaccines.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Structural Basis and Designing of Peptide Vaccine using PE-PGRS Family Protein of Mycobacterium ulcerans - An Integrated Vaccinomics Approach 93%
- Dichotomy in TCR V-domain dynamics modeled for binding the opposed inclined planes of pMHC-II and pMHC-I α-helices 92%
- CDR3 binding chemistry controls TCR V-domain rotational probability and germline CDR2 'scanning' of polymorphic MHC 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.