Flexible protein-protein docking with a multitrack iterative transformer.

ABSTRACT: Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predic

SUBMITTER: Chu LS 

PROVIDER: S-EPMC10804679 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature