DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Wei Lu, Jixian Zhang, Huang Weifeng, Ziqiao Zhang, Chengtao Li, Shuangjia Zheng

Keywords: Molecular Docking, Protein Dynamics, Diffusion Models, E(3)-equivariant Neural Networks, AI-aided Drug Discovery

TL;DR: We present DynamicBind, an E(3)-equivariant diffusion-based deep generative model designed for protein-ligand "dynamic docking", which can efficiently adjust protein conformations via up to milisecond-level conformational motions during prediction.

Abstract:

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery.
Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid.
While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states.
In this study, we present DynamicBind, a novel method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states.
DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling.
Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify novel cryptic pockets in unseen protein targets.
As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

Submission Number: 30