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Proc Natl Acad Sci U S A ; 120(9): e2216697120, 2023 02 28.
Artículo en Inglés | MEDLINE | ID: mdl-36802421

RESUMEN

Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.


Asunto(s)
Antígenos de Histocompatibilidad Clase II , Péptidos , Unión Proteica , Péptidos/química , Antígenos de Histocompatibilidad Clase II/metabolismo , Genes MHC Clase II , Dominios PDZ
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