预测药物蛋白相互作用与深度学习框架的分子图表和序列:潜在的候选人对抗SARS-CoV-2
Weian Du1, Liang Zhao2, Rong Wu1
1Department of Dermatology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
PloS one
|May 10, 2024
概括
由于冠状病毒发生突变,开发广泛的抗病毒药物至关重要. 一个新的深度学习框架,GraphDPI-3CL,有效地识别了针对3CLpro酶的潜在抑制剂,这是病毒复制的关键.
科学领域:
- 病毒学 病毒学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 导致COVID-19,这是全球卫生危机.
- 像疫苗这样的现有治疗方法受到快速突变的冠状病毒 (例如,Delta,Omicron) 的挑战.
- 病毒的3 - 胆素类型的氨酸蛋白酶 (3CLpro) 对于冠状病毒复制和可行的药物标至关重要.
研究的目的:
- 开发一种新的,基于注意力的深度学习框架,用于识别广泛的抗病毒分子实体.
- 针对3CLpro酶,这是各种冠状病毒中保存和关键组成部分.
- 选大型化合物数据库以寻找具有高结合亲和力的潜在3CLpro抑制剂.
主要方法:
- 开发了一个基于注意力的深度学习框架 (GraphDPI-3CL) 用于分子图和序列.
- 在BindingDB 3CLpro数据集 (114,555个化合物) 上训练模型.
- 数据库 (276,003个化合物) 进行了选,并利用基于几何的亲和力预测进行了验证.
主要成果:
- 该GraphDPI-3CL框架实现了一个高的接收器运行特征曲线 (AUROC) 下面的面积为0.958.
- 与现有最先进的模型相比,表现出优越的性能.
- 确定了10个候选分子,它们对3CLpro.pro.具有高结合亲和力和有利的结合模式.
结论:
- 开发的深度学习框架GraphDPI-3CL对于发现新型3CLpro抑制剂是有效的.
- 这种方法为开发针对冠状病毒的广泛抗病毒药物提供了一个有前途的战略.
- 已识别的分子代表了未来针对SARS-CoV-2和相关病毒的治疗开发的潜在线索.
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