CACHE挑战#2:针对SARS-CoV-2螺旋酶Nsp13的RNA部位
Oleksandra Herasymenko1, Madhushika Silva1, Abd Al-Aziz A Abu-Saleh1
1Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
Journal of chemical information and modeling
|June 20, 2025
概括
计算发现实验 (CACHE) 挑战成功识别了SARS-CoV-2 Nsp13螺旋酶的新型联体支架. 这项众筹的努力利用了计算和机器学习方法,从而发现了抑制病毒复制的化合物.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学的计算化学
- 病毒学 病毒学
背景情况:
- 由于其在病毒复制中的作用,SARS-CoV-2 Nsp13螺旋酶是COVID-19药物发现的关键目标.
- 预测蛋白质标的有效配体仍然是药物开发中的一个重大挑战.
研究的目的:
- 评估计算方法在识别SARS-CoV-2 Nsp13螺旋酶RNA结合部位的抑制性连接体中的有效性.
- 为了评估计算发现实验 (CACHE) #2 挑战在众包联体预测中的成功.
主要方法:
- 23个团队使用计算和机器学习方法,以蛋白质结构和碎片选数据为指导,以预测连接体.
- 预测的化合物被采购并使用表面等离子体共振测试来测试结合亲和力.
- 成功的计算工作流涉及碎片增长,主动学习和虚拟选,通常通过深度学习来增强.
主要成果:
- 在1957年预测的化合物中,0.7%显示与Nsp13.0结合.
- 两种化合物支架证明与Kd低于10μM的结合,并抑制了NSP13螺旋酶活性.
- 最有效的策略包括碎片链接/增长和对接各种图书馆用于机器学习模型培训.
结论:
- 在CACHE #2的挑战中,成功地确定了针对Nsp13螺旋酶的命中化合物支架.
- 群众资源连接体预测与生物物理验证相结合,对于发现新药潜在客户而言是有效的.
- 经常成功的计算策略包括基于片段的方法和机器学习模型优化.
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