用于计算机强化学习的新型刺突/ACE2抑制性大环的发现

L. Shapira, Shaul Lerner, Guila Assayag, A. Vardi, D. Haham, Gideon Bar, Vicky Fidelsky Kozokaro, Maayan Elias Robicsek, Immanuel Lerner, Amit Michaeli
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引用次数: 1

摘要

导言:2019冠状病毒病大流行给人类生命和全球经济造成了重大损失。COVID-19是由SARS-CoV-2病毒引起的,该病毒通过其结合人类ACE2的刺突蛋白感染细胞。方法:为了发现潜在的抑制尖刺/ACE2复合物的拟肽大循环,我们采用人工智能引导的虚拟筛选方法,采用三种不同的策略:1)变构尖刺抑制剂;2)竞争性ACE2抑制剂;3)竞争性尖刺抑制剂。通过将大环与相关位点对接、聚类和合成聚类代表进行筛选。利用AlphaLISA和RSV颗粒对合成的分子进行抑制筛选。结果:所有三种策略都产生了抑制肽,但只有竞争性刺突抑制剂显示出“击中”水平的活性。讨论:这些结果表明,直接抑制刺突RBD结构域是针对当前大流行的拟肽“头到尾”大周期药物开发的最有吸引力的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery of novel spike/ACE2 inhibitory macrocycles using in silico reinforcement learning
Introduction: The COVID-19 pandemic has cast a heavy toll in human lives and global economics. COVID-19 is caused by the SARS-CoV-2 virus, which infects cells via its spike protein binding human ACE2. Methods: To discover potential inhibitory peptidomimetic macrocycles for the spike/ACE2 complex we deployed Artificial Intelligence guided virtual screening with three distinct strategies: 1) Allosteric spike inhibitors 2) Competitive ACE2 inhibitors and 3) Competitive spike inhibitors. Screening was performed by docking macrocycles to the relevant sites, clustering and synthesizing cluster representatives. Synthesized molecules were screened for inhibition using AlphaLISA and RSV particles. Results: All three strategies yielded inhibitory peptides, but only the competitive spike inhibitors showed “hit” level activity. Discussion: These results suggest that direct inhibition of the spike RBD domain is the most attractive strategy for peptidomimetic, “head-to-tail” macrocycle drug development against the ongoing pandemic.
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