Year: 2022
Type: preprint
Abstract: Proteins interact to form complexes to carry out essential biological functions. Computational methods have been developed to predict the structures of protein complexes. However, an important challenge in protein complex structure prediction is to estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. We challenge this significant task with DProQ, which introduces a gated neighborhood-modulating Graph Transformer (GGT) designed to predict the quality of 3D protein complex structures. Notably, we incorporate node and edge gates within a novel Graph Transformer framework to control information flow during graph message passing. We train and evaluate DProQ on four newly-developed datasets that we make publicly available in this work. Our rigorous experiments demonstrate that DProQ achieves state-of-the-art performance in ranking protein complex structures.
Source: arXiv (Cornell University)
Institution: University of Missouri
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G1645119126, G2411683797, III: Medium: Collaborative Research: Guiding Exploration of Protein Structure Spaces with Deep Learning, G2584197162, G2988778103, G3945850812, ABI Innovation: Deep learning methods for protein bioinformatics, Distance-based ab initio protein structure prediction, G8906985441
OpenAlex ID: W4308289796