Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.
Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.
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DOI：
10.1016/j.jmgm.2015.11.008
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发表时间：
2016-01
影响因子：
2.9
通讯作者：
Amin EA
中科院分区：
文献类型：
--
作者：
Zhang X;Amin EA
炭疽病是一种高致死性的急性传染病，由杆状革兰氏阳性细菌炭疽杆菌引起。炭疽毒素致死因子（LF）是由炭疽杆菌分泌的一种锌金属蛋白酶，在炭疽病的发病机制中起着关键的作用，其主要机制是通过使丝裂原活化蛋白激酶激酶（MAPKK）失活，进而破坏关键的细胞信号传导途径，导致炭疽相关的毒血症和宿主死亡。抗生素如氟喹诺酮类能够清除杆菌，但对LF介导的毒血症没有影响;因此LF本身仍然是毒素灭活的首选靶点。然而，目前市场上还没有LF抑制剂作为治疗药物，部分原因是现有的LF抑制剂支架在功效、选择性和毒性方面存在不足。在目前的工作中，我们提出了新的支持向量机（SVM）模型，预测精度高，旨在快速识别潜在的新的，结构多样的LF抑制剂的化合物库的化学物质。这些SVM模型使用508种具有公开的LF生物活性数据的化合物和847种保存在Pub Chem BioAssay数据库中的非活性化合物进行训练和验证。一个模型，M1，表现出对高活性化合物的特别有利的选择性，通过正确预测39（95.12%）的41纳摩尔水平的LF抑制剂，46（93.88%）的49非活性，和844（99.65%）的847 Pub Chem非活性在外部，无偏测试集。预计这些模型将有助于预测现有分子的LF抑制活性，以及从大型数据集中识别新的潜在LF抑制剂。
Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets.