Access through your institution Buy or subscribe Researchers have long strived to develop methods for accurately predicting the binding of a peptide to the major histocompatibility complex

(MHC) for recognition by a T cell receptor. So far, this has posed a formidable challenge given the vast diversity of antigens as well as the highly polymorphic MHC class II molecules.

Although recent method developments have led to several methods for predicting peptide binding to MHC class I, fewer predictors exist for peptide–MHC II binding and these remain limited by

their low accuracy or by the number of alleles recognized. In a recent study in _Immunity_, researchers at the University of Lausanne led by David Gfeller report a new machine learning

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AUTHORS AND AFFILIATIONS * Nature Methods https://www.nature.com/nmeth/ Madhura Mukhopadhyay Authors * Madhura Mukhopadhyay View author publications You can also search for this author

inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Madhura Mukhopadhyay. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Mukhopadhyay, M.

Machine learning for MHC II specificities. _Nat Methods_ 20, 632 (2023). https://doi.org/10.1038/s41592-023-01891-2 Download citation * Published: 11 May 2023 * Issue Date: May 2023 * DOI:

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