Fishing the Target of Antitubercular Compounds: In Silico Target Deconvolution Model Development and Validation
Fishing the Target of Antitubercular Compounds: In Silico Target Deconvolution Model Development and Validation
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DOI：
10.1021/pr8010843
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发表时间：
2009-06-01
影响因子：
4.4
通讯作者：
Bender, Andreas
中科院分区：
文献类型：
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作者：
Prathipati, Philip;Ma, Ngai Ling;Bender, Andreas
在计算机模拟，抗结核（抗TB）化合物的目标预测协议已在这项工作中提出。该协议是最近发表的“域钓鱼模型”（DFM）的扩展，验证其预测的目标上的一组42种常见的抗结核药物。对于该组中与靶标直接相关的23种抗TB化合物（定义参见文本），DFM表现出95%的非常好的靶标预测准确度。对于19种与靶标间接相关的化合物，也实现了84%的合理途径/嵌入途径预测准确度。由于大多数真核配体结合数据用于DFM生成，因此原核生物的高靶标预测准确性（其是来自训练数据的外推）是出乎意料的，并且提供了DFM概念的额外证明。为了估计模型的普遍适用性，进行配体-靶覆盖分析。在此，发现尽管DFM仅适度地覆盖整个TB蛋白质组（所有蛋白质的32%），但它捕获了由42种常见抗TB化合物靶向的蛋白质组子集的70%，这与DFM对于此处选择的化合物的靶标的良好预测能力一致。在前瞻性验证中，该模型成功预测了新的抗结核化合物CBR-2092和Amiclenomycin的靶点。总之，这些发现表明，在硅片上，目标预测工具可能是一个有用的补充，现有的实验目标去卷积策略。
An in silico, target prediction protocol for antitubercular (antiTB) compounds has been proposed in this work. This protocol is the extension of a recently published 'domain fishing model' (DFM), validating its predicted targets on a set of 42 common antitubercular drugs. For the 23 antiTB compounds of the set which are directly linked to targets (see text for definition), the DFM exhibited a very good target prediction accuracy of 95%. For 19 compounds indirectly linked to targets also, a reasonable pathway/embedded pathway prediction accuracy of 84% was achieved. Since mostly eukaryotic ligand binding data was used for the DFM generation, the high target prediction accuracy for prokaryotes (which is an extrapolation from the training data) was unexpected and provides an additional proof of concept of the DFM. To estimate the general applicability of the model, ligand-target coverage analysis was performed. Here, it was found that, although the DFM only modestly covers the entire TB proteome (32% of all proteins), it captures 70% of the proteome subset targeted by 42 common antiTB compounds, which is in agreement with the good predictive ability of the DFM for the targets of the compounds chosen here. In a prospective validation, the model successfully predicted the targets of new antiTB compounds, CBR-2092 and Amiclenomycin. Together, these findings suggest that in silico, target prediction tools may be a useful supplement to existing, experimental target deconvolution strategies.