The accurate prediction of ligand-binding sites in proteins remains an outstanding challenge, despite its potential to accelerate drug discovery and inform on natural protein function. Protein-peptide and protein-ligand interactions play an important role in cell signaling by carrying out essential biological processes. Here, we train a neural network, AF2BIND, using embedding features from a protein structure prediction model, AlphaFold2, to accurately predict the binding epitopes of proteins from single structures. AF2BIND effectively captures binding signatures of small-molecule- and peptide-binding sites, without knowledge of the true ligand.
Experiments were conducted using the latest ColabDesign github commit as of 03/11/2023 (https://github.com/sokrypton/ColabDesign on commit 961c7afda81a2bc8d8990d539cce25dc5a15dd1f), with the Alphafold's weights as of 2022-03-02 (https://github.com/deepmind/alphafold on https://storage.googleapis.com/alphafold/alphafold_params_2022-03-02.tar)