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

ABSTRACT: 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 t

SUBMITTER: Motmaen A 

PROVIDER: S-EPMC9992841 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature