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Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...
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  3. Tutorial On Quantifying And Sampling Biomolecular Ensembles With Shapegmm.
  1. Home
  3. Tutorial On Quantifying And Sampling Biomolecular Ensembles With Shapegmm.

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Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes
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Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM.

Subarna Sasmal1, Martin McCullagh2, Glen M Hocky1,3

  • 1Department of Chemistry, New York University, New York, New York 10003, USA.

The Journal of Chemical Physics
|December 23, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We present a workflow for biomolecular conformation analysis using ShapeGMM. This method models free energy from atomic fluctuations, enabling enhanced sampling and refined conformational models.

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Statistical mechanics

Background:

  • Understanding biomolecular conformations is crucial for drug discovery and protein function.
  • Existing methods for conformational sampling can be computationally intensive and may miss important states.

Purpose of the Study:

  • To present a detailed workflow for clustering and enhanced sampling of biomolecular conformations.
  • To introduce the ShapeGMM methodology for modeling free energy landscapes.
  • To demonstrate a complete pipeline from simulation data to refined conformational models.

Main Methods:

  • ShapeGMM (Shape Gaussian Mixture Model) methodology for probabilistic modeling of conformations.
  • Analysis of equilibrium molecular dynamics simulation data.
  • Generation of reaction coordinates.
  • Enhanced sampling using metadynamics with a size-and-shape PLUMED module.
  • Clustering of biased conformations.

    • Main Results:

    • A robust workflow for analyzing biomolecular conformations was established.
    • The ShapeGMM model effectively captures free energy landscapes based on atomic fluctuations.
    • The integrated approach successfully samples along reaction coordinates and refines conformational models.

    Conclusions:

    • The ShapeGMM methodology provides an efficient approach for enhanced sampling and clustering of biomolecular conformations.
    • This workflow facilitates a deeper understanding of protein dynamics and function.
    • The developed tools can aid in the identification of novel drug targets and the design of therapeutic molecules.