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探索基于AlphaFold2的结构组合和分子动力学模拟的SARS-CoV-2尖端Omicron变异复合体与ACE2受体的融合进化的构造景观和结合机制

Nishank Raisinghani¹, Mohammed Alshahrani¹, Grace Gupta¹

¹Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA. verkhivk@chapman.edu.

Physical chemistry chemical physics : PCCP

|June 13, 2024

概括

研究人员使用AlphaFold和分子模拟来研究SARS-CoV-2 Omicron变种. 这揭示了突变如何影响尖端蛋白与ACE2结合,有助于理解病毒进化和免疫逃避.

科学领域:

计算生物学和结构生物信息学.
病毒学和分子动力学模拟.

背景情况:

SARS-CoV-2 Omicron 变种迅速进化,获得了影响其尖端蛋白与人类 ACE2 受体相互作用的突变.
了解这些变异的构造动态和结合机制对于预测病毒进化和免疫逃避策略至关重要.

研究的目的:

使用AlphaFold和分子动力学 (MD) 模拟,准确地描述SARS-CoV-2 Omicron尖端变体 (BA.1,BA.2,BA.2.75,BA.3,BA.4/BA.5,BQ.1.1) 的构造组合和结合机制.
探索序列变化如何影响Omicron尖峰-ACE2复合体的结构动态.
预测和验证与ACE2受体的Omicron变体复合物的结合能.

主要方法:

采用并验证了多个AlphaFold适应用于建模蛋白质构造组合,包括随机的全序列扫描.
进行了微秒的原子分子动力学 (MD) 模拟,以表征构造景观和热力学稳定性.
集成AlphaFold预测与统计信心指标,并将绑定能量预测与实验数据进行比较.

主要成果:

基于AlphaFold的方法和MD模拟提供了对Omicron尖峰-ACE2复合体的形状组合的详细表征.
综合方法准确预测了结合能量,与实验数据有很好的一致性.
确定了功能性构造和动态合,优化了ACE2结合亲和力,并有助于免疫逃避.

结论:

当与MD模拟相结合时,对构造组合的AlphaFold预测提供了更全面的蛋白质动态的特征.
这项研究强调了在Omicron变体进化中的形状适应性,动态合和结合亲和力之间的相互作用.
这些发现提供了关于融合突变如何增强ACE2结合和促进免疫逃避的见解,为未来的治疗策略提供了信息.

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