Large-scale bioactivity analysis of the small-molecule assayed proteome.
Large-scale bioactivity analysis of the small-molecule assayed proteome.
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DOI：
10.1371/journal.pone.0171413
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Girke T
中科院分区：
文献类型：
--
作者：
Backman TW;Evans DS;Girke T
本研究对 PubChem BioAssay 中代表的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 批准的药物与非 FDA 批准的化合物的生物活性概况进行了比较，并报告了批准药物的几种不同模式特征。我们发现，与非 FDA 批准的生物活性物质相比，之前报道的 FDA 批准的化合物中较高的靶标混杂性很大一部分通常是由于蛋白质家族内部而非蛋白质家族之间的交叉反应造成的。我们为 FDA 批准的药物确定了 804 个潜在的新型候选蛋白质靶标，以及 901 个具有非 FDA 批准的活性化合物的潜在新型候选靶标，但 FDA 没有批准对这些靶标具有活性的药物。我们还鉴定了 486348 种对与 FDA 批准的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 批准的活性药物的靶点具有活性的潜在新型化合物。通过量化重复屏幕之间的一致性，我们估计这些新结果中有一半以上是可重复的。使用双聚类，我们确定了 FDA 批准的许多密集药物簇，这些药物对一组常见的蛋白质靶点具有丰富的活性。我们还使用贝叶斯统计模型报告了化合物混杂的分布，并报告了两种识别混杂化合物的常用方法的敏感性和特异性。聚合器检测在识别高度混杂化合物方面表现出更高的准确性，而 PAINS 子结构能够识别更多的“中等范围”混杂化合物。此外，我们报告了大量未确定为聚合剂或 PAINS 的混杂化合物。总之，本研究结果为选择新药和靶蛋白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。
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.