Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model

Abstract Conformational changes in proteins are vital to their function yet remain challenging for state-of-the-art artificial intelligence, such as AlphaFold3 (AF3), to predict. It has been observed that AF3 sometimes fails to capture ligand-induced conformational changes, even though it explicitly includes ligand molecules that induce such changes. To address this challenge, we develop an enhanced sampling scheme that leverages the diffusion-based generative model used in AF3 to predict protein structures. Interpreting the diffusion generative model as a stochastic sampling process analogous to molecular dynamics (MD) simulations, we introduce here a repulsive biasing potential between predicted structures to explore wider conformational space. We demonstrate that the developed model, AF3-ReD, successfully predicts multiple conformational states, including ligand-bound conformations of motor and transporter proteins that the original AF3 does not capture. Compared to another strategy based on multiple sequence alignment (MSA), AF3-ReD predicted intermediate conformations that are relatively closer to the stable states. Thus, AF3-ReD provides a promising approach for understanding dynamic conformational changes associated with ligand binding, including those induced by drug molecules.