Seq2Bind webserver for binding site prediction from sequences using fine-tuned protein language models.

ABSTRACT: Decoding protein-protein interactions at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind webserver, a computational framework that leverages fine-tuned protein language models (PLMs) to determine binding affinity between proteins and identify critical binding residues directly from sequences, eliminating the structural requirements that limit affinity prediction tools. We fine-tuned four architectures, including ProtBERT, ProtT5, Evolutionary Scale Modeling 2 (ESM2), and Bidirectional Long Short-Term Memory on the SKEMPI 2.0 dataset. Through systematic alanine mutagenesis on each residue for 6063 dimer proteins from Protein Data Bank, we evaluated each model's ability to identify interface residues. Performance wa

SUBMITTER: Ma X 

PROVIDER: S-EPMC12639246 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature