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智能分辨率:将冷冻电镜与人工智能驱动的多分辨率模拟相结合,以观察严重急性呼吸综合征冠状病毒2的复制转录机制在实际运作中的情况。

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.

作者信息

Trifan Anda, Gorgun Defne, Salim Michael, Li Zongyi, Brace Alexander, Zvyagin Maxim, Ma Heng, Clyde Austin, Clark David, Hardy David J, Burnley Tom, Huang Lei, McCalpin John, Emani Murali, Yoo Hyenseung, Yin Junqi, Tsaris Aristeidis, Subbiah Vishal, Raza Tanveer, Liu Jessica, Trebesch Noah, Wells Geoffrey, Mysore Venkatesh, Gibbs Thomas, Phillips James, Chennubhotla S Chakra, Foster Ian, Stevens Rick, Anandkumar Anima, Vishwanath Venkatram, Stone John E, Tajkhorshid Emad, A Harris Sarah, Ramanathan Arvind

机构信息

Argonne National Laboratory.

University of Illinois Urbana-Champaign.

出版信息

Int J High Perform Comput Appl. 2022 Nov;36(5-6):603-623. doi: 10.1177/10943420221113513. Epub 2022 Aug 5.

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)复制转录复合体(RTC)是一种多结构域蛋白,负责在人体细胞内复制和转录病毒mRNA。用药物化合物攻击RTC功能是治疗新冠肺炎的一条途径。传统工具,如冷冻电子显微镜和全原子分子动力学(AAMD),无法提供足够高的分辨率或时间尺度来捕捉这种分子机器的重要动态。因此,我们开发了一种创新的工作流程,利用中尺度波动有限元分析(FFEA)连续介质模拟和一系列人工智能方法来弥合这些分辨率之间的差距,这些人工智能方法不断学习和推断特征,以保持AAMD和FFEA模拟之间的一致性。我们利用一个多站点分布式工作流管理器来编排人工智能、FFEA和AAMD作业,在高性能计算中心实现最佳资源利用。我们的研究为研究SARS-CoV-2 RTC机制提供了前所未有的途径,同时为大规模的人工智能多分辨率模拟提供了通用能力。

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