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J Chem Inf Model ; 55(4): 747-59, 2015 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-25760928

RESUMO

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.


Assuntos
Colatos/metabolismo , Colesterol/metabolismo , Biologia Computacional/métodos , Proteínas/química , Proteínas/metabolismo , Motivos de Aminoácidos , Animais , Proteínas de Bactérias/química , Proteínas de Bactérias/metabolismo , Sítios de Ligação , Humanos , Ligantes , Aprendizado de Máquina , Proteínas de Membrana/química , Proteínas de Membrana/metabolismo , Camundongos , Modelos Moleculares , Ligação Proteica
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