Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250, 14th Street, N.W., Atlanta, GA 30318, USA.
School of Biology, Atlanta, GA 30332, USA ; School of Chemistry and Biochemistry, Aquatic Chemical Ecology Center, Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.
J Cheminform. 2014 Apr 26;6:16. doi: 10.1186/1758-2946-6-16. eCollection 2014.
Identification of ligand-protein binding interactions is a critical step in drug discovery. Experimental screening of large chemical libraries, in spite of their specific role and importance in drug discovery, suffer from the disadvantages of being random, time-consuming and expensive. To accelerate the process, traditional structure- or ligand-based VLS approaches are combined with experimental high-throughput screening, HTS. Often a single protein or, at most, a protein family is considered. Large scale VLS benchmarking across diverse protein families is rarely done, and the reported success rate is very low. Here, we demonstrate the experimental HTS validation of a novel VLS approach, FINDSITE(comb), across a diverse set of medically-relevant proteins.
For eight different proteins belonging to different fold-classes and from diverse organisms, the top 1% of FINDSITE(comb)'s VLS predictions were tested, and depending on the protein target, 4%-47% of the predicted ligands were shown to bind with μM or better affinities. In total, 47 small molecule binders were identified. Low nanomolar (nM) binders for dihydrofolate reductase and protein tyrosine phosphatases (PTPs) and micromolar binders for the other proteins were identified. Six novel molecules had cytotoxic activity (<10 μg/ml) against the HCT-116 colon carcinoma cell line and one novel molecule had potent antibacterial activity.
We show that FINDSITE(comb) is a promising new VLS approach that can assist drug discovery.
鉴定配体-蛋白结合相互作用是药物发现的关键步骤。尽管大型化学文库的实验筛选在药物发现中具有特定的作用和重要性，但它们存在随机性、耗时和昂贵的缺点。为了加速这一过程，传统的基于结构或配体的虚拟筛选方法与实验高通量筛选（HTS）相结合。通常只考虑单个蛋白质，或者最多考虑一个蛋白质家族。很少对不同蛋白质家族进行大规模的虚拟筛选基准测试，而且报告的成功率非常低。在这里，我们展示了一种新的虚拟筛选方法 FINDSITE(comb) 在一系列具有医学相关性的蛋白质中的实验 HTS 验证。
对于属于不同折叠类别的 8 种不同蛋白质，来自不同生物体，测试了 FINDSITE(comb) 的虚拟筛选预测的前 1%，并且根据蛋白质靶标，预测的配体中有 4%-47%以μM 或更好的亲和力结合。总共鉴定出 47 种小分子结合剂。针对二氢叶酸还原酶和蛋白酪氨酸磷酸酶（PTPs）的低纳摩尔（nM）结合剂和针对其他蛋白质的微摩尔结合剂被鉴定出来。六种新分子对 HCT-116 结肠癌细胞系具有细胞毒性（<10μg/ml），一种新分子具有很强的抗菌活性。
我们表明 FINDSITE(comb) 是一种很有前途的新虚拟筛选方法，可以辅助药物发现。
