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小分子检测蛋白质组的大规模生物活性分析。

Large-scale bioactivity analysis of the small-molecule assayed proteome.

作者信息

Backman Tyler William H, Evans Daniel S, Girke Thomas

机构信息

Department of Bioengineering, University of California Riverside, Riverside, California, United States of America.

Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America.

出版信息

PLoS One. 2017 Feb 8;12(2):e0171413. doi: 10.1371/journal.pone.0171413. eCollection 2017.

Abstract

This study presents an analysis of the small molecule bioactivity profiles across large quantities of diverse protein families represented in PubChem BioAssay. We compared the bioactivity profiles of FDA approved drugs to non-FDA approved compounds, and report several distinct patterns characteristic of the approved drugs. We found that a large fraction of the previously reported higher target promiscuity among FDA approved compounds, compared to non-FDA approved bioactives, was frequently due to cross-reactivity within rather than across protein families. We identified 804 potentially novel protein target candidates for FDA approved drugs, as well as 901 potentially novel target candidates with active non-FDA approved compounds, but no FDA approved drugs with activity against these targets. We also identified 486348 potentially novel compounds active against the same targets as FDA approved drugs, as well as 153402 potentially novel compounds active against targets without active FDA approved drugs. By quantifying the agreement among replicated screens, we estimated that more than half of these novel outcomes are reproducible. Using biclustering, we identified many dense clusters of FDA approved drugs with enriched activity against a common set of protein targets. We also report the distribution of compound promiscuity using a Bayesian statistical model, and report the sensitivity and specificity of two common methods for identifying promiscuous compounds. Aggregator assays exhibited greater accuracy in identifying highly promiscuous compounds, while PAINS substructures were able to identify a much larger set of "middle range" promiscuous compounds. Additionally, we report a large number of promiscuous compounds not identified as aggregators or PAINS. In summary, the results of this study represent a rich reference for selecting novel drug and target protein candidates, as well as for eliminating candidate compounds with unselective activities.

摘要

本研究对PubChem生物测定中大量不同蛋白质家族的小分子生物活性谱进行了分析。我们将FDA批准药物的生物活性谱与非FDA批准的化合物进行了比较,并报告了几种批准药物特有的不同模式。我们发现,与非FDA批准的生物活性物质相比,先前报道的FDA批准化合物中较高的靶点多配性很大一部分通常是由于蛋白质家族内部而非跨家族的交叉反应所致。我们为FDA批准的药物确定了804个潜在的新型蛋白质靶点候选物,以及901个具有活性的非FDA批准化合物的潜在新型靶点候选物,但没有针对这些靶点具有活性的FDA批准药物。我们还确定了486348种对与FDA批准药物相同靶点具有活性的潜在新型化合物,以及153402种对没有FDA批准活性药物的靶点具有活性的潜在新型化合物。通过量化重复筛选之间的一致性,我们估计这些新结果中超过一半是可重复的。使用双聚类分析,我们确定了许多FDA批准药物的密集簇,这些药物对一组共同的蛋白质靶点具有富集活性。我们还使用贝叶斯统计模型报告了化合物多配性的分布,并报告了两种识别多配性化合物的常用方法的敏感性和特异性。聚集分析在识别高度多配性化合物方面表现出更高的准确性,而PAINS子结构能够识别出更多的“中等范围”多配性化合物。此外,我们报告了大量未被识别为聚集物或PAINS的多配性化合物。总之,本研究结果为选择新型药物和靶点蛋白质候选物以及消除具有非选择性活性的候选化合物提供了丰富的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d090/5298297/e1b166d74ec4/pone.0171413.g001.jpg

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