xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction

xBind is an interactive, freely accessible, and fully configurable webserver for large language model (LLM)-enabled cross-molecular protein binding-site prediction. xBind leverages LLM embeddings from the ESM-2 model together with sequence- and structure-derived features to predict protein-protein, protein-DNA, and protein-RNA binding sites using symmetry-aware deep graph neural networks. The input to xBind is either a single-chain protein sequence in FASTA format or a monomer protein structure in PDB or mmCIF format and it outputs predicted residue-level binding sites of the input protein with its pre-selected interaction partner. The customizable xBind web interface provides: (i) choice of interaction partners including protein-protein, protein-DNA, and protein-RNA; (ii) on-the-fly AlphaFold-based protein structure prediction for sequence-only inputs; (iii) on-demand selection of the likelihood threshold for calibrating structure-aware binding site annotations; (iv) interactive and interpretable web-based results, including sequence and structural visualizations and plots of residue-level binding likelihoods with user-adjustable threshold calibration; and (v) extensive help information for usage and results interpretation through a web-based tutorial and guide. xBind is freely available at https://fusion.cs.vt.edu/xBind.