Automated matching of high- and low-resolution structural models

TLDR

The method enables comparative analysis of structural models from high‑resolution crystallographic structures and low‑resolution models derived from solution scattering or electron microscopy. The study presents a method for automated best‑matching alignment of three‑dimensional models represented by ensembles of points. It introduces a normalized spatial discrepancy (NSD) as a proximity measure, aligns models using inertia‑axes alignment, minimizes NSD to quantify similarity, and is evaluated through simulations on structures ranging from a few to several thousand points. The algorithm produces a quantitative similarity estimate via minimized NSD and is implemented in a computer program.

Abstract

A method is presented for automated best-matching alignment of three-dimensional models represented by ensembles of points. A normalized spatial discrepancy (NSD) is introduced as a proximity measure between three-dimensional objects. Starting from an inertia-axes alignment, the algorithm minimizes the NSD; the final value of the NSD provides a quantitative estimate of similarity between the objects. The method is implemented in a computer program. Simulations have been performed to test its performance on model structures with specified numbers of points ranging from a few to a few thousand. The method can be used for comparative analysis of structural models obtained by different methods, e.g. of high-resolution crystallographic atomic structures and low-resolution models from solution scattering or electron microscopy.