2026-10-11T11:29:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34039030 2026-10-01T05:08:11Z category_1 category_4 category_915 category_873 category_734 category_931 category_135 portal_63 item_type_6 month_year_10_2026

Bayesian Learning of Distance Metrics Beyond RMSD for Biomolecule Alignment, Clustering, and Domain Identification Saumyak Mukherjee (4576951) Gerhard Hummer (90147) Biophysics Biochemistry Physical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Computational Biology σ → ∞) phosphotransferase adenylate kinase endoplasmic reticulum translocon atom weights jointly associated protein snd3 focused alignment onto two md systems optimal rmsd (< domain identification root domain identification structural alignment specified domain biomolecule alignment weight uniformity trajectory smoothing structural comparison structural averages standard metric square deviation progressive focus overall metric optimizes per molecular dynamics low rmsd k </ global rmsd flexible loops disordered regions conventional form classical rmsd brmsd </ bayesian posterior bayesian learning atoms regardless Root-mean-square deviation (RMSD) is the standard metric of structural comparison in molecular dynamics (MD) simulations. In its conventional form, RMSD assigns equal weight to all atoms regardless of mobility. Hence, flexible loops and disordered regions can dominate a global RMSD, while the rigid functional core contributes negligibly to the overall metric. To address this issue, we introduce the Bayes-optimal RMSD (<i>BRMSD</i>), which optimizes per-atom weights jointly with structural averages by maximizing a Bayesian posterior. In a trade-off between low RMSD and weight uniformity, a position-fluctuation parameter σ controls the transition from classical RMSD (σ → ∞) to a progressive focus on a rigid core (σ → 0). The BRMSD framework supports analysis modules for structural alignment, focused alignment onto a user-specified domain, trajectory smoothing, soft <i>K</i>-means conformational clustering, and rigid-domain identification. These modules are implemented in the open-source Python package BRMSD and benchmarked on two MD systems, the endoplasmic reticulum translocon-associated protein SND3 and the phosphotransferase adenylate kinase. 2026-10-01T00:00:00Z Text Journal contribution 10.1021/acs.jctc.6c01395.s003 https://figshare.com/articles/journal_contribution/Bayesian_Learning_of_Distance_Metrics_Beyond_RMSD_for_Biomolecule_Alignment_Clustering_and_Domain_Identification/34039030 CC BY-NC 4.0