AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps

TL;DR

AlignOT introduces a novel optimal transport-based algorithm for fast and accurate 3D alignment of cryo-EM density maps, outperforming standard methods and effectively handling conformational heterogeneity.

Contribution

This paper presents AlignOT, a new optimal transport-based method for 3D map alignment that improves accuracy and robustness over existing local alignment techniques.

Findings

AlignOT outperforms standard local alignment methods.

AlignOT handles larger rotation angles effectively.

Benchmark results show good performance on experimental maps.

Abstract

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. We quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.