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Trebesch, N.

Publications and source records attributed to Trebesch, N..

2 recordsLinked to original sources

Structure Reveals Homology in Elevator Transporters

The elevator transport mechanism is one of the handful of canonical mechanisms by which transporters shuttle their substrates across the semi-permeable membranes that surround cells and organelles. Studies of molecular function are naturally guided by evolutionary context, but until now this context has been limited for elevator transporters because established evolutionary classification methods have organized them into several apparently unrelated families. Through comprehensive examination of the pertinent structures available in the Protein Data Bank, we show that 62 elevator transporters from 18 families share a conserved architecture in their transport domains consisting of 10 helices connected in 8 topologies. Through quantitative analysis of the structural similarity, structural complexity, and topologically-corrected sequence similarity among the transport domains, we provide compelling evidence that these elevator transporters are all homologous. Using our analysis, we have constructed a phylogenetic tree to enable quantification and visualization of the evolutionary relationships among elevator transporters and their families. We also report several examples of functional features that are shared by elevator transporters from different families. Our findings shed new light on the elevator transport mechanism and allow us to understand it in a far deeper and more nuanced manner.

biophysics↗

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

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.

biophysics↗