Conditioned Protein Structure Prediction

Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach t...

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Bibliographic Details
Published in:PRX Life 2024-10, Vol.2 (4), Article 043001
Main Authors: Xie, Tengyu, Song, Zilin, Huang, Jing
Format: Article
Language:English
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Summary:Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions is demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights.
ISSN:2835-8279
2835-8279
DOI:10.1103/PRXLife.2.043001