Interpreting Molecular Dynamics Forces as Deep Learning Gradients Improves Quality Of Predicted Protein Structures

Biophys J. 2023 Dec 15:S0006-3495(23)04149-8. doi: 10.1016/j.bpj.2023.12.011. Online ahead of print.ABSTRACTProtein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method's efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.PMID:38104241 | DOI:10.1016/j.bpj.2023.12.011

Source: Biophysical Journal - December 17, 2023 Category: Physics Authors: Jonathan Edward King David Ryan Koes Source Type: research