通过深度生成基础模型加速药物向抑制剂的发现
Vijil Chenthamarakshan1, Samuel C Hoffman1, C David Owen2,3
1IBM Research, Thomas J. Watson Research Center, Yorktown Heights, New York, NY, USA.
Science advances
|June 21, 2023
概括
一个新的深度生成框架通过仅使用蛋白质序列来设计小分子抑制剂来加速药物发现. 这种方法成功地确定了SARS-CoV-2点的强效抑制剂,在没有先前的结构数据的情况下证明了效率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 结构生物学是结构生物学.
背景情况:
- 发现新药标的抑制剂是很困难的,特别是没有结构信息或已知的活性分子.
- 现有的方法通常需要大量的特定目标数据,限制对新出现的威胁的快速反应.
研究的目的:
- 为了验证一个深层次的生成框架,以无偏见的,基于序列的小分子抑制剂设计.
- 评估该框架对抗SARS-CoV-2点的有效性,特别是尖端蛋白受体结合域 (RBD) 和主要蛋白酶.
主要方法:
- 一个大规模的深度生成基础模型被训练在蛋白质序列,小分子和它们的相互作用.
- 使用序列条件采样来设计候选抑制剂,仅基于标蛋白序列.
- 合成的化合物在试验室中进行了针对目标蛋白的抑制活性的实验测试.
主要成果:
- 在4种合成的小分子候选药物中,有2种对每个位都表现出微分子水平的抑制.
- 最有效的尖端蛋白RBD抑制剂在活病毒中和试验中对多种SARS-CoV-2变体表现出活性.
- 生成框架成功地识别出抑制剂,而无需先前了解目标结构或现有的结合剂.
结论:
- 一个单一的,广泛适用的深度生成基础模型可以有效地加速抑制剂的发现.
- 这种基于序列的方法即使在没有目标结构或活性分子信息的情况下也有效.
- 该框架为快速开发治疗新兴传染病的治疗方法提供了一个有希望的战略.
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