Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling
Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling
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DOI：
10.1186/s13321-019-0337-8
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发表时间：
2019-02-14
影响因子：
8.6
通讯作者：
van Westen, Gerard J. P.
中科院分区：
文献类型：
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作者：
Burggraaff, Lindsey;Oranje, Paul;van Westen, Gerard J. P.
钠依赖性葡萄糖协同转运蛋白1（SGLT 1）是负责主动葡萄糖吸收的溶质载体。SGLT 1存在于肾小管和小肠中。相比之下，密切相关的钠依赖性葡萄糖协同转运蛋白2（SGLT 2），一种治疗II型糖尿病的靶蛋白，仅在肾小管中表达。虽然已经开发出SGLT 1和SGLT 2的双重抑制剂，但市场上没有药物以降低胃肠道中SGLT 1的饮食葡萄糖摄取为目标。在这里，我们的目标是通过应用不需要结构信息的机器学习方法在计算机上识别SGLT 1抑制剂，而SGLT 1不需要结构信息。我们应用蛋白质化学计量学的化合物和蛋白质为基础的信息到随机森林模型的实施。我们得到的预测模型的灵敏度为0.64 +/- 0.06，特异性为0.93 +/- 0.01，阳性预测值为0.47 +/- 0.07，阴性预测值为0.96 +/- 0.01，马修斯相关系数为0.49 +/- 0.05。在模型训练之后，我们将我们的模型应用于虚拟筛选，以识别新型SGLT 1抑制剂。在77种测试化合物中，30种在体外实验中证实了SGLT 1抑制活性，导致命中率为39%，活性在低微摩尔范围内。此外，命中化合物包括新分子，这反映在这些化合物与训练集的低相似性（< 0.3）上。总之，SGLT 1的蛋白化学计量学建模是鉴定活性小分子的可行策略。因此，该方法也可应用于其他转运蛋白的新型小分子的检测。
Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 +/- 0.06, specificity of 0.93 +/- 0.01, positive predictive value of 0.47 +/- 0.07, negative predictive value of 0.96 +/- 0.01, and Matthews correlation coefficient of 0.49 +/- 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins.