Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA.
Molecules. 2021 Apr 28;26(9):2581. doi: 10.3390/molecules26092581.
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
药物再利用，即将现有药物用于新的临床适应症的实践，具有极大地改善人类健康结果和提高治疗开发效率的潜力。多疾病多靶点药物再利用的目标，也称为 shotgun 药物再利用，是开发评估每个现有药物对每个临床适应症的治疗潜力的平台。我们的 shotgun multitarget 再利用的计算分析新型药物机会 (CANDO) 平台实施了几个用于大规模建模和模拟综合药物/化合物库与蛋白质结构之间相互作用的管道。在这些管道中，每种药物都由相互作用特征描述，然后与所有其他特征进行比较，并根据相似性进行排序和排名。平台内的管道根据它们从我们的库中恢复所有适应症的已知药物的能力进行基准测试，并基于以下假设生成预测：（新型）具有相似特征的药物可能被重新用于相同的适应症。用于创建药物-蛋白质特征的药物-蛋白质相互作用可以通过任何筛选或对接方法确定，但迄今为止主要方法是 BANDOCK，我们的内部生物分析或相似性对接协议。在这项研究中，我们使用公开可用的分子对接方法 Autodock Vina 计算了药物-蛋白质相互作用特征，并创建了混合决策树管道，将我们原始的生物和化学信息学方法与评估和基准测试它们的药物再利用能力和性能的目标相结合。与组成它的两个管道相比，混合决策树管道的性能优于这两个管道，在最严格的 top10 截止值处，其平均适应症准确性为 13.3%，而其组成管道的准确性分别为 10.9%和 7.1%，随机对照准确性为 2.2%。我们证明，基于对接的虚拟筛选管道具有独特的性能特征，并且 CANDO shotgun 再利用范例不依赖于特定的对接方法。我们的结果还进一步证明，可以合成多个 CANDO 管道来提高相对于组成管道的药物再利用预测能力。总的来说，这项研究表明，由不同对接基础特征生成方法组成的管道可以捕获独特且有用的信号，用于准确比较药物-蛋白质相互作用特征，从而提高 CANDO shotgun 药物再利用平台的基准测试和预测性能。
