Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform.
Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform.
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DOI：
10.2174/1389557515666150219145148
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Samudrala R
中科院分区：
文献类型：
--
作者：
Sethi G;Chopra G;Samudrala R
我们已经检查了八种不同蛋白质类别（通道、GPCR、激酶、连接酶、核受体、蛋白酶、磷酸酶、转运蛋白）对CANDO药物发现和再利用平台（http：//protinfo.org/cando）的基准性能的影响。CANDO平台的第一个版本利用了48278种蛋白质和3733种人类可摄入化合物（包括FDA批准的药物和补充剂）之间的预测相互作用矩阵，这些相互作用使用基于层次化学和生物信息学片段的对接与动力学协议映射到2030种适应症/疾病（考虑了> 10亿个预测相互作用）。该平台使用化合物-蛋白质组相互作用特征的相似性作为相似功能行为的指示，并在1439种适应症/疾病中使用一种以上批准的药物计算基准准确性。CANDO平台在考虑的蛋白质数量和获得的基准准确度之间产生显著相关性（0.99，p值< 0.0001），表明多靶向对于药物发现的重要性。当考虑排名前10位的化合物时，八个类别的平均基准准确度范围为6.2%至7.6%，相比之下，由10、100、1000和10000个单一最佳表现蛋白质组成的比较/对照组获得的范围为5.5%至11.7%。这些结果通常是两个数量级优于平均精度0.2%时，随机生成的（完全打乱）矩阵使用。当使用不同的类别时，不同的适应症表现良好，但当使用包含最广泛的蛋白质折叠分布的类别组合时，可以实现最佳的准确度（排名前10的化合物高达11.7%）。我们的研究结果说明了CANDO方法的实用性，并考虑不同的蛋白质类别，以设计药物再利用以及药物发现的适应症特异性方案。
We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.
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影响因子：
4.8
作者：
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DOI：
10.1073/pnas.0810818105
发表时间：
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影响因子：
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