ArticleInternational journal of molecular sciences2020
MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 19 citations in OpenAlex.
- Review
- MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods.Frontiers in immunology · 2024Review
- Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling.Bioinformatics (Oxford, England) · 2023Article
- Immunoinformatics Approaches for Vaccine Design: A Fast and Secure Strategy for Successful Vaccine Development.Vaccines · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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 with different sequence-based machine learning methods. The Pearson correlation to experimentally determine IC
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