Evidence map›Paper›PMID 41279339›Full record

ArticlebioRxiv : the preprint server for biology2025

Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM.

Subarna Sasmal, Martin McCullagh, Glen M Hocky

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here we present a detailed workflow for clustering and enhanced sampling of biomolecular conformations using the ShapeGMM methodology. This approach fits a probabilistic model of biomolecular conformations rooted in the idea that the free energy can expressed in terms of local fluctuations in atomic positions around metastable states. We demonstrate using a single model system how to generate and fit equilibrium molecular dynamics simulation data. We then show how to use the resulting model to generate a reaction coordinate between two states, how to sample along that coordinate using Metadynamics using our size-and-shape PLUMED module, and how to cluster those biased conformations to give a refined equilibrium ShapeGMM model.

Identifiers

PMID41279339
PMCPMC12632468

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.