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一个集成的多尺度计算框架破译了SARS-CoV-2对索特罗维马布的耐药性

Akshit Sharma¹, Shweata Maurya¹, Shivank Kumar¹

¹Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.

Biophysical journal

|May 21, 2025

概括

这项研究开发了一种综合框架,以识别SARS-CoV-2尖端蛋白突变,从而产生对关键单克隆抗体治疗方法索特罗维马布的耐药性. 这些发现与临床数据一致,有助于理解耐药性和设计新的抗体疗法.

科学领域:

病毒学病毒学
结构生物学结构生物学
计算化学计算化学

背景情况:

像索特罗维马布这样的单克隆抗体 (mAb) 治疗向SARS-CoV-2尖端 (S) 蛋白.
在S蛋白中出现的抗性突变挑战了这些疗法的有效性.
了解突变驱动的抵抗机制对于治疗开发至关重要.

研究的目的:

开发和验证一个集成的计算框架,用于识别SARS-CoV-2S蛋白抗性突变对sotrovimab.
阐明抗性突变的结构和动态特征.
为下一代抗体疗法的设计提供信息.

主要方法:

综合框架结合了接口蛋白质设计,机器学习,QM/MM和多尺度分子动力学 (MD) 模拟.
对S蛋白-索特罗维马布相互作用的分析和关键残留物的鉴定.
机器学习用于根据结构,序列,结合和能量特征预测阻力突变.
QM/MM用于评估突变对复杂稳定性的影响和MD模拟用于动态行为分析.
根据临床测序数据和经验证据进行验证.

主要成果:

在S蛋白-sotrovimab接口上确定了关键残留物和可信的抗性突变.
确定了两个关键残留物,E340和Y508,它们的设计与临床观察到的耐药性突变相关.
机器学习模型预测了具有改变sotrovimab亲和力的新型S蛋白序列,由AlphaFold结构验证.
多尺度模拟捕获了突变的结构动态和稳定性影响,包括糖甘相互作用.

结论:

综合框架有效地识别了针对sotrovimab的SARS-CoV-2耐药性突变,并得到了临床数据的支持.
这种多维方法为抵抗机制提供了洞察力,并有助于设计有效的抗体治疗方法.
该方法对开发针对其他病毒系统的抗体具有翻译相关性.

关键词:

粗粒度模拟是粗粒度的模拟.计算生物学是一种计算生物学.药物耐药性药物耐药性药物耐药性综合框架综合框架综合框架机器学习是机器学习.分子建模分子建模单克隆抗体是一种单克隆抗体.蛋白质构成的结构.蛋白质的设计蛋白质的设计蛋白质与蛋白质的相互作用量子化学是一种量子化学.抗性突变的突变这就是SARS-CoV-2病毒.这就是Sotrovimab.尖蛋白质是一种尖蛋白质.

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