Year: 2024
Type: article
Abstract: MOTIVATION: The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. RESULTS: Across four distinct molecular-geometric tasks, we demonstrate that GCPNet's predictions (1) for protein-ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. AVAILABILITY AND IMPLEMENTATION: The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.
Source: Bioinformatics
Authors: Alex Morehead, Jianlin Cheng
Institution: University of Missouri
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,188
Awards: G121915436, UNIVERSITY OF MISSOURI - NEW AWARD
CONTROL NUMBER: 2107-1570
PROJECT TITLE: DEEP LEARNING PREDICTION OF PROTEIN COMPLEX STRUCTURES
PROJECT DESCRIPTION: THIS COOPERATIVE AGREEMENT IS HEREBY AWARDED TO THE CURATORS OF THE UNIVERSITY OF MISSOURI WHO PROPOSES TO DEVELOP NOVEL DEEP LEARNING METHODS TO PREDICT INTER-PROTEIN AMINO ACID INTERACTIONS (CONTACTS) AND BUILD THREE-DIMENSIONAL STRUCTURES OF PROTEIN COMPLEXES., G1645119126, HIGH THROUGHPUT, ACCURATE GENE ANNOTATION THROUGH AI AND HPC-ENABLED STRUCTURAL ANALYSIS, G203625172, G2411683797, III: Medium: Collaborative Research: Guiding Exploration of Protein Structure Spaces with Deep Learning, G2584197162, G2988778103, Deep transformers for integrating protein sequence, structure and interaction data to predict function, ABI Innovation: Deep learning methods for protein bioinformatics, Distance-based ab initio protein structure prediction, G8269158468, DEEP GREEN: STRUCTURAL AND FUNCTIONAL GENOMIC CHARACTERIZATION OF CONSERVED UNANNOTATED GREEN LINEAGE PROTEINS, G8906985441, Deep learning methods for automated and accurate reconstruction of protein structures from cryo-EM image data, G946059479
OpenAlex ID: W4392391018