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Updated: Apr 16, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
CholMine: Determinants and Prediction of Cholesterol and Cholate Binding Across Nonhomologous Protein Structures
Nan Liu1, Jeffrey R Van Voorst1, John B Johnston1
1†Department of Chemistry, ‡Department of Computer Science and Engineering, and §Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824-1319, United States.
Abstract:
Identifying physiological ligands is necessary for annotating new protein structures, yet this presents a significant challenge to biologists and pharmaceutical chemists. Here we develop a predictor of cholesterol and cholate binding that works across diverse protein families, extending beyond sequence motif-based prediction. This approach combines SimSite3D site comparison with the detection of conserved interactions in cholesterol/cholate bound crystal structures to define three-dimensional interaction motifs. The resulting predictor identifies cholesterol sites with an ∼82% unbiased true positive rate in both membrane and soluble proteins, with a very low false positive rate relative to other predictors. The CholMine Web server can analyze users' structures, detect those likely to bind cholesterol/cholate, and predict the binding mode and key interactions. By deciphering the determinants of binding for these important steroids, CholMine may also aid in the design of selective inhibitors and detergents for targets such as G protein coupled receptors and bile acid receptors.
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