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用于类固醇靶点预测的类固醇特异性靶标库。

Steroids-specific target library for steroids target prediction.

作者信息

Dang Xiaoxue, Liu Zheng, Zhou Yanzhuo, Chen Peizi, Liu Jiyuan, Yao Xiaojun, Lei Beilei

机构信息

Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.

Key Laboratory of Plant Protection Resources & Pest Management of the Ministry of Education, Northwest A&F University, Yangling, Shaanxi, China.

出版信息

Steroids. 2018 Dec;140:83-91. doi: 10.1016/j.steroids.2018.10.002. Epub 2018 Oct 6.

Abstract

Steroids exist universally and play critical roles in various biological processes. Identifying potential targets of steroids is of great significance in studying their physiological and biochemical activities, the side effects and for drug repurposing. Herein, aiming at more precise steroids targets prediction, a steroids-specific target library integrating 3325 PDB or homology modeling structures categorized into 196 proteins was built by considering chemical similarity from DrugBank and biological processes from KEGG. The main properties of this library include: (1) It was manually prepared and checked to eliminate mistakes. (2) The library enriched the possible steroids targets and could decrease the false positives of structure-based target screening for steroids. (3) The ranking by protein name instead of PDB ID could make the screening more efficiency and precise. (4) Protein flexibility was taken into account partially by the different active conformations through the structural redundancy of each category of protein, which leads to more accurate prediction. The case studies of glycocholic acid and 24-epibrassinolide proved its powerful predictive accuracy. In summary, our strategy to build the steroids-specific protein library for steroids target prediction is a promising approach and it provides a novel idea for the target prediction of small molecules.

摘要

类固醇普遍存在,并在各种生物过程中发挥关键作用。确定类固醇的潜在靶点对于研究其生理和生化活性、副作用以及药物再利用具有重要意义。在此,为了更精确地预测类固醇靶点,通过考虑来自DrugBank的化学相似性和来自KEGG的生物过程,构建了一个类固醇特异性靶点库,该库整合了3325个PDB或同源建模结构,分为196种蛋白质。该库的主要特性包括:(1)它是人工制备并经过检查以消除错误的。(2)该库丰富了可能的类固醇靶点,并可减少基于结构的类固醇靶点筛选的假阳性。(3)按蛋白质名称而非PDB ID进行排序可使筛选更高效、更精确。(4)通过每类蛋白质的结构冗余,不同的活性构象部分考虑了蛋白质的灵活性,这导致更准确的预测。甘氨胆酸和24-表油菜素内酯的案例研究证明了其强大的预测准确性。总之,我们构建用于类固醇靶点预测的类固醇特异性蛋白质库的策略是一种有前途的方法,它为小分子的靶点预测提供了新的思路。

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