  The binding complexes formed by proteins and small molecule ligands are
ubiquitous and critical to life. Despite recent advancements in protein
structure prediction, existing algorithms are so far unable to systematically
predict the binding ligand structures along with their regulatory effects on
protein folding. To address this discrepancy, we present NeuralPLexer, a
computational approach that can directly predict protein-ligand complex
structures solely using protein sequence and ligand molecular graph inputs.
NeuralPLexer adopts a deep generative model to sample the 3D structures of the
binding complex and their conformational changes at an atomistic resolution.
The model is based on a diffusion process that incorporates essential
biophysical constraints and a multi-scale geometric deep learning system to
iteratively sample residue-level contact maps and all heavy-atom coordinates in
a hierarchical manner. NeuralPLexer achieves state-of-the-art performance
compared to all existing methods on benchmarks for both protein-ligand blind
docking and flexible binding site structure recovery. Moreover, owing to its
specificity in sampling both ligand-free-state and ligand-bound-state
ensembles, NeuralPLexer consistently outperforms AlphaFold2 in terms of global
protein structure accuracy on both representative structure pairs with large
conformational changes (average TM-score=0.93) and recently determined
ligand-binding proteins (average TM-score=0.89). Case studies reveal that the
predicted conformational variations are consistent with structure determination
experiments for important targets, including human KRAS$^\textrm{G12C}$,
ketol-acid reductoisomerase, and purine GPCRs. Our study suggests that a
data-driven approach can capture the structural cooperativity between proteins
and small molecules, showing promise in accelerating the design of enzymes,
drug molecules, and beyond.
