SyNDock: N Rigid Protein Docking via Learnable Group Synchronization

TL;DR

SyNDock is a novel neural framework for multimeric protein docking that learns global transformations, enabling rapid and accurate assembly of protein complexes with significant improvements over existing methods.

Contribution

It introduces a learnable group synchronization approach with a two-step SE(3) algorithm for efficient multimeric protein docking.

Findings

Achieves 4.5% better accuracy than existing methods

Runs a million times faster than previous approaches

Outperforms current software in key performance metrics

Abstract

The regulation of various cellular processes heavily relies on the protein complexes within a living cell, necessitating a comprehensive understanding of their three-dimensional structures to elucidate the underlying mechanisms. While neural docking techniques have exhibited promising outcomes in binary protein docking, the application of advanced neural architectures to multimeric protein docking remains uncertain. This study introduces SyNDock, an automated framework that swiftly assembles precise multimeric complexes within seconds, showcasing performance that can potentially surpass or be on par with recent advanced approaches. SyNDock possesses several appealing advantages not present in previous approaches. Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution. Secondly, SyNDock proposes a trainable two-step SE(3) algorithm, involving initial pairwise transformation and confidence estimation, followed by global transformation synchronization. This enables effective learning for assembling the complex in a globally consistent manner. Lastly, extensive experiments conducted on our proposed benchmark dataset demonstrate that SyNDock outperforms existing docking software in crucial performance metrics, including accuracy and runtime. For instance, it achieves a 4.5% improvement in performance and a remarkable millionfold acceleration in speed.