Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States

), which highlights low-energy regions corresponding to conformational states. The accurate definition of these states, however, is often impeded by projection artifacts, resulting in artificially shortened state lifetimes or even the complete disappearance of states from the analysis. As demonstrated for a two-dimensional toy model, Gaussian low-pass filtering of the high-dimensional feature trajectory can restore the underlying free energy landscape, allowing recovery of previously hidden states. When applied to an all-atom folding trajectory of HP35, the number of microstates increases by an order of magnitude, which leads to metastable states that are long-lived and much better defined structurally, even compared to dynamically cored state trajectories.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2026-04-02
DOI
https://doi.org/10.1021/acs.jpclett.6c00341
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States

The Journal of Physical Chemistry Letters
Machine Learning in Materials Science
article

Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States

article en

Abstract

), which highlights low-energy regions corresponding to conformational states. The accurate definition of these states, however, is often impeded by projection artifacts, resulting in artificially shortened state lifetimes or even the complete disappearance of states from the analysis. As demonstrated for a two-dimensional toy model, Gaussian low-pass filtering of the high-dimensional feature trajectory can restore the underlying free energy landscape, allowing recovery of previously hidden states. When applied to an all-atom folding trajectory of HP35, the number of microstates increases by an order of magnitude, which leads to metastable states that are long-lived and much better defined structurally, even compared to dynamically cored state trajectories.

The Journal of Physical Chemistry LettersVol. 17(15)
University of Freiburg (DE)
Deutsche Forschungsgemeinschaft, Albert-Ludwigs-Universität Freiburg
Affordable and clean energy
Openalex Percentile: Top 29%
Machine Learning in Materials Science
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