%0 Journal Article %1 Liu2015CholMine %A Liu, Nan %A Voorst, Jeffrey R. Van %A Johnston, John B. %A Kuhn, Leslie A. %D 2015 %J Journal of Chemical Information and Modeling %K cholesterol computational-biology ligand-binding %N 4 %P 747-759 %R 10.1021/ci5006542 %T CholMine: Determinants and Prediction of Cholesterol and Cholate Binding Across Nonhomologous Protein Structures %U http://pubs.acs.org/doi/full/10.1021/ci5006542 %V 55 %X 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.

@article{Liu2015CholMine, 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. }, added-at = {2016-01-19T20:24:06.000+0100}, author = {Liu, Nan and Voorst, Jeffrey R. Van and Johnston, John B. and Kuhn, Leslie A.}, biburl = {https://www.bibsonomy.org/bibtex/2cc58bdb7698a9dc0cb5fe1f4078206fd/salotz}, description = {CholMine: Determinants and Prediction of Cholesterol and Cholate Binding Across Nonhomologous Protein Structures - Journal of Chemical Information and Modeling (ACS Publications)}, doi = {10.1021/ci5006542}, eprint = {http://dx.doi.org/10.1021/ci5006542}, interhash = {316dcbcecdb6cdd3d4e164abb7baf8a0}, intrahash = {cc58bdb7698a9dc0cb5fe1f4078206fd}, journal = {Journal of Chemical Information and Modeling}, keywords = {cholesterol computational-biology ligand-binding}, note = {PMID: 25760928}, number = 4, pages = {747-759}, timestamp = {2016-01-19T23:11:12.000+0100}, title = {CholMine: Determinants and Prediction of Cholesterol and Cholate Binding Across Nonhomologous Protein Structures}, url = {http://pubs.acs.org/doi/full/10.1021/ci5006542}, volume = 55, year = 2015 }