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Identifying physiological ligands is necessary for annotating new protein structures, yet this presents a significant challenge to biologists and pharmaceutical chemists. 识别生理学配体是注解全新蛋白质结构的必须过程,然而这一点却呈现一种重大的挑战,等待生物学家和药物化学家面对. Here we develop a predictor of cholesterol and cholate binding that works across diverse protein families, extending beyond sequence motif-based prediction. 在此,我们研制了一种越策机制,能预测跨越不同蛋白质族的胆固醇和胆酸盐绑定,而不再仅限于排好序列的,基于主题的预测.

This approach combines SimSite3D site comparison with the detection of conserved interactions in cholesterol/cholate bound crystal structures to define 3-dimensional interaction motifs. 这种方式既能够进行SimSite3D 站点比力,也能够探测,胆固醇/胆盐晶体结构中的守恒互动,从而定义3D互动主题.

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. 相比其它预测机制,运用该结果预测机制,对膜和可溶性蛋白质中的胆固醇点进行识别,公正真实的阳性率达到82%,假阳性率十分低.

The CholMine webserver can analyze users' structures, detect those likely to bind cholesterol/cholate, and predict the binding mode and key interactions. CholMine网络服务器是否分析用户的结构,探测那些可能绑定胆固醇或者胆酸盐的物质,同时能预测结合模式和重要的相互作用.

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. 通过破译,能通过绑定生成这些重要的类固醇,决定因素,CholMine也可以帮助设计 选择性抑制剂和洗涤剂,从而获得目标物,例如G蛋白偶联受体和胆汁酸受体.

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