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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.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

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.

Calmodulin (CaM) is a ubiquitous Ca2+ sensing protein that binds to and modulates numerous target proteins and enzymes during cellular signaling processes. A large number of CaM-target complexes have been identified and structurally characterized, revealing a wide diversity of CaM-binding modes. A newly identified target is creatine kinase (CK), a central enzyme in cellular energy homeostasis. This study reports two high-resolution X-ray structures, determined to 1.24 ​Å and 1.43 ​Å resolution, of calmodulin in complex with peptides from human brain and muscle CK, respectively. Both complexes adopt a rare extended binding mode with an observed stoichiometry of 1:2 CaM:peptide, confirmed by isothermal titration calorimetry, suggesting that each CaM domain independently binds one CK peptide in a Ca2+-depended manner. While the overall binding mode is similar between the structures with muscle or brain-type CK peptides, the most significant difference is the opposite binding orientation of the peptides in the N-terminal domain. This may extrapolate into distinct binding modes and regulation of the full-length CK isoforms. The structural insights gained in this study strengthen the link between cellular energy homeostasis and Ca2+-mediated cell signaling and may shed light on ways by which cells can ‘fine tune’ their energy levels to match the spatial and temporal demands.