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通过片段重用的有效蛋白质-配体对接策略及概念验证实现

Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation.

作者信息

Yanagisawa Keisuke, Kubota Rikuto, Yoshikawa Yasushi, Ohue Masahito, Akiyama Yutaka

机构信息

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.

AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8560, Japan.

出版信息

ACS Omega. 2022 Aug 19;7(34):30265-30274. doi: 10.1021/acsomega.2c03470. eCollection 2022 Aug 30.

Abstract

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing -best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.

摘要

虚拟筛选是在药物设计早期阶段从大量化合物中寻找可行药物候选物的常用方法。随着化合物数据库持续扩展至数十亿条记录甚至更多,迫切需要加快对接计算过程。重复使用计算结果是加快该过程的一种可行方法。在本研究中,我们首先通过结合化合物分解、简化片段网格存储最佳分数以及对预先生成构象的灵活性考虑这三个因素,提出了另一种面向虚拟筛选的对接策略。候选化合物包含许多常见片段(化学子结构)。因此,这些常见片段的计算结果可以在它们之间重复使用。作为上述策略的概念验证,我们还开发了REstretto,这是一个实现这三个因素以实现计算结果重复使用的工具。我们证明了REstretto的速度和准确性与著名的免费对接工具AutoDock Vina相当。REstretto的实现还有很大的进一步性能提升空间,因此,结果表明了该策略的可行性。代码可在https://github.com/akiyamalab/restretto上根据MIT许可获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c79/9435046/70eccd023d87/ao2c03470_0001.jpg

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