GradPose: a very fast and memory-efficient gradient descent-based tool for superimposing millions of protein structures from computational simulations

Abstract

Abstract Summary Computational simulations like molecular dynamics and docking are providing crucial insights into the dynamics and interaction conformations of proteins, complementing experimental methods for determining protein structures. These methods often generate millions of protein conformations, necessitating highly efficient structure comparison and clustering methods to analyze the results. In this article, we introduce GradPose, a fast and memory-efficient structural superimposition tool for models generated by these large-scale simulations. GradPose uses gradient descent to optimally superimpose structures by optimizing rotation quaternions and can handle insertions and deletions compared to the reference structure. It is capable of superimposing thousands to millions of protein structures on standard hardware and utilizes multiple CPU cores and, if available, CUDA acceleration to further decrease superimposition time. Our results indicate that GradPose generally outperforms traditional methods, with a speed improvement of 2–65 times and memory requirement reduction of 1.7–48 times, with larger protein structures benefiting the most. We observed that traditional methods outperformed GradPose only with very small proteins consisting of ∼20 residues. The prerequisite of GradPose is that residue–residue correspondence is predetermined. With GradPose, we aim to provide a computationally efficient solution to the challenge of efficiently handling the demand for structural alignment in the computational simulation field. Availability and implementation Source code is freely available at https://github.com/X-lab-3D/GradPose; doi:10.5281/zenodo.7671922.