EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

• URL: http://arxiv.org/abs/2205.10390v1
• Date: Fri, 20 May 2022 18:11:41 GMT
• Title: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
• Authors: Alex Morehead, Xiao Chen, Tianqi Wu, Jian Liu, Jianlin Cheng
• Abstract summary: We introduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graph neural network (GNN) for multi-task structure refinement and assessment of protein complexes. Our experiments on new, diverse protein complex datasets, all of which we make publicly available in this work, demonstrate the state-of-the-art effectiveness of EGR.
• Score: 8.494211223965703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein complexes are macromolecules essential to the functioning and well-being of all living organisms. As the structure of a protein complex, in particular its region of interaction between multiple protein subunits (i.e., chains), has a notable influence on the biological function of the complex, computational methods that can quickly and effectively be used to refine and assess the quality of a protein complex's 3D structure can directly be used within a drug discovery pipeline to accelerate the development of new therapeutics and improve the efficacy of future vaccines. In this work, we introduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graph neural network (GNN) for multi-task structure refinement and assessment of protein complexes. Our experiments on new, diverse protein complex datasets, all of which we make publicly available in this work, demonstrate the state-of-the-art effectiveness of EGR for atomistic refinement and assessment of protein complexes and outline directions for future work in the field. In doing so, we establish a baseline for future studies in macromolecular refinement and structure analysis.

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