用人工智能引导的蛋白质蛋白相互作用药物发现管道确定了一种SARS-CoV-2抑制剂
Philipp Trepte1,2, Christopher Secker3,4, Julien Olivet5,6,7,8,9
1Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany. philipp.trepte@imba.oeaw.ac.at.
Molecular systems biology
|March 12, 2024
概括
这项研究引入了一条用于识别和验证蛋白质与蛋白质相互作用 (PPI) 药物点的管道. 它通过准NSP10-NSP16复合体,成功地确定了一种抑制SARS-CoV-2复制的化合物.
科学领域:
- 生物化学 生物化学
- 药物发现 药物发现 药物发现
- 计算生物学 计算生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 在疾病中至关重要,但具有挑战性的药物标.
- 制定有效的策略来确定PPI目标和药物发现是必不可少的.
研究的目的:
- 提出综合实验和计算方法用于PPI目标识别和早期药物发现的综合管道.
- 优先考虑PPI目标并加速开发复杂疾病的候选药物.
主要方法:
- 开发了一种机器学习方法,使用定量测试数据 (LuTHy) 和AlphaFold-Multimer预测来优先考虑PPI.
- 使用VirtualFlow进行超大虚拟查,以识别SARS-CoV-2 NSP10-NSP16甲基转移酶复合物的抑制剂.
主要成果:
- 确定了高可信度的SARS-CoV-2蛋白相互作用,并预测了它们的结构.
- 发现一种与NSP10结合的化合物,抑制NSP10-NSP16相互作用,破坏甲基转移酶活性,并减少SARS-CoV-2复制.
结论:
- 开发的管道有效地优先考虑PPI目标,并促进早期药物发现.
- 这种方法加速了针对治疗干预的蛋白质复合体的新药候选药物的识别.
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