Work

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow

Year: 2023

Type: article

Abstract: For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbad365

Authors: Gang Xu, Zhenwei Luo, Ruhong Zhou, Qinghua Wang, Jianpeng Ma

Institutions: Fudan University, Shanghai Artificial Intelligence Laboratory, Zhejiang University

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Zhejiang University, National Key Research and Development Program of China