Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites.
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites.
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小分子与结合相关 S-亚硝基化位点之间关联的计算预测和分析
DOI：
10.3390/molecules23040954
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发表时间：
2018-04-19
期刊：
影响因子：
--
通讯作者：
Zhao C
中科院分区：
文献类型：
--
作者：
Huang G;Li J;Zhao C
药物与蛋白质之间的相互作用在药物发现和开发过程中占据中心地位。最近开发了许多方法来识别药物与靶点的相互作用，但很少有方法致力于寻找翻译后修饰的蛋白质和药物之间的相互作用。我们提出了一种基于机器学习的方法，用于识别小分子和结合相关的 S-亚硝基化 (SNO-) 蛋白之间的关联。即通过分子指纹对小分子进行编码，通过基于信息熵的方法对SNO-蛋白质进行编码，并使用随机森林来训练分类器。十倍交叉验证和留一交叉验证分别获得了接受者操作特征曲线下面积的 0.7235 和 0.7490。相似性的计算分析表明，与相同药物相关的 SNO 蛋白具有统计学上显着的相似性，反之亦然。该方法和发现有助于识别药物与 SNO 的关联，并进一步促进 SNO 相关药物的发现和开发。
Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs.
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影响因子：
3.7
作者：
Ben-Lulu S;Ziv T;Weisman-Shomer P;Benhar M
通讯作者：
Benhar M
DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
S-亚硝基化诱导罗丹家族蛋白的结构和动态变化。
影响因子：
5.6
作者：
Eichmann C;Tzitzilonis C;Nakamura T;Kwiatkowski W;Maslennikov I;Choe S;Lipton SA;Riek R
通讯作者：
Riek R
影响因子：
9.7
作者：
Adams, CP;Brantner, VV
通讯作者：
Brantner, VV
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer