PymolFold: A PyMOL Plugin for API-Driven Structure Prediction and Quality Assessment

Deep learning has transformed protein structure prediction, yet most state-of-the-art models remain challenging for experimental scientists to access. To address this, we present PymolFold, an open-source PyMOL plugin that seamlessly integrates cutting-edge structure predictors accessible via application programming interfaces into the molecular visualization environment. PymolFold supports both graphical and command-line interfaces for flexible usage and incorporates a structural quality assessment tool for the quantitative evaluation of predictions against reference structures. Together, these features establish a unified "predict-visualize-analyze" workflow, lowering technical barriers and broadening access to advanced structural modeling. PymolFold is available at https://github.com/jinyuansun/PymolFold.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-01-02
DOI
https://doi.org/10.1021/acs.jcim.5c02410
Primary Topic
Protein Structure and Dynamics
Type
article
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article

PymolFold: A PyMOL Plugin for API-Driven Structure Prediction and Quality Assessment

Jinyuan Sun, Yifan Deng
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
article

PymolFold: A PyMOL Plugin for API-Driven Structure Prediction and Quality Assessment

Jinyuan Sun, Yifan Deng
article en

Abstract

Deep learning has transformed protein structure prediction, yet most state-of-the-art models remain challenging for experimental scientists to access. To address this, we present PymolFold, an open-source PyMOL plugin that seamlessly integrates cutting-edge structure predictors accessible via application programming interfaces into the molecular visualization environment. PymolFold supports both graphical and command-line interfaces for flexible usage and incorporates a structural quality assessment tool for the quantitative evaluation of predictions against reference structures. Together, these features establish a unified "predict-visualize-analyze" workflow, lowering technical barriers and broadening access to advanced structural modeling. PymolFold is available at https://github.com/jinyuansun/PymolFold.

Journal of Chemical Information and ModelingVol. 66(2)
Urology Team (US)
Life below water
Openalex Percentile: Top 99%
Protein Structure and Dynamics
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