Peptide-binding specificity prediction using fine-tuned protein structure prediction networks

• Published in: Proceedings of the National Academy of Sciences - PNAS 2023-02, Vol.120 (9), p.e2216697120
• Main Authors: Motmaen, Amir, Dauparas, Justas, Baek, Minkyung, Abedi, Mohamad H, Baker, David, Bradley, Philip
• Format: Article
• Language: English
• Subjects: Amino acid sequence; Binding; Biological Sciences; Genes, MHC Class II; Histocompatibility Antigens Class II - metabolism; Major histocompatibility complex; Networks; PDZ Domains; Peptides; Peptides - chemistry; Predictions; Protein Binding; Protein structure; Proteins
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2216697120

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.