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

2021 ◽  
Author(s):  
Anda Trifan ◽  
Defne Gorgun ◽  
Zongyi Li ◽  
Alexander Brace ◽  
Maxim Zvyagin ◽  
...  

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.

2020 ◽  
Vol 39 (15-16) ◽  
pp. 587-598 ◽  
Author(s):  
Vahid Daghigh ◽  
Thomas E Lacy ◽  
Hamid Daghigh ◽  
Grace Gu ◽  
Kourosh T Baghaei ◽  
...  

Tailorability is an important advantage of composites. Incorporating new bio-reinforcements into composites can contribute to using agricultural wastes and creating tougher and more reliable materials. Nevertheless, the huge number of possible natural material combinations works against finding optimal composite designs. Here, machine learning was employed to effectively predict fracture toughness properties of multiscale bio-nano-composites. Charpy impact tests were conducted on composites with various combinations of two new bio fillers, pistachio shell powders, and fractal date seed particles, as well as nano-clays and short latania fibers, all which reinforce a poly(propylene)/ethylene–propylene–diene-monomer matrix. The measured energy absorptions obtained were used to calculate strain energy release rates as a fracture toughness parameter using linear elastic fracture mechanics and finite element analysis approaches. Despite the limited number of training data obtained from these impact tests and finite element analysis, the machine learning results were accurate for prediction and optimal design. This study applied the decision tree regressor and adaptive boosting regressor machine learning methods in contrast to the K-nearest neighbor regressor machine learning approach used in our previous study for heat deflection temperature predictions. Scanning electron microscopy, optical microscopy, and transmission electron microscopy were used to study the nano-clay dispersion and impact fracture morphology.


2015 ◽  
Vol 27 (02) ◽  
pp. 1550013 ◽  
Author(s):  
M. M. Youssef ◽  
D. E. T. Shepherd ◽  
O. G. Titley

A failed compass hinge external fixator for fingers has been analyzed. The device consists of polymer parts manufactured from polyetherimide. Finite element analysis (FEA) was used to investigate the principal stresses in the device under different loading conditions. Scanning electron microscopy (SEM) was used to investigate the fracture surfaces. The FEA showed that the maximum principal stress was greater than the fatigue strength of polyetherimide. The SEM fractographs confirm that failure was by brittle fatigue.


2011 ◽  
Vol 54 (6) ◽  
pp. 920-928 ◽  
Author(s):  
H. Eid ◽  
G. G. Adams ◽  
N. E. McGruer ◽  
A. Fortini ◽  
S. Buldyrev ◽  
...  

Author(s):  
Robin A. Richardson ◽  
Benjamin S. Hanson ◽  
Daniel J. Read ◽  
Oliver G. Harlen ◽  
Sarah A. Harris

Abstract Flagellar dyneins are the molecular motors responsible for producing the propagating bending motions of cilia and flagella. They are located within a densely packed and highly organised super-macromolecular cytoskeletal structure known as the axoneme. Using the mesoscale simulation technique Fluctuating Finite Element Analysis (FFEA), which represents proteins as viscoelastic continuum objects subject to explicit thermal noise, we have quantified the constraints on the range of molecular conformations that can be explored by dynein-c within the crowded architecture of the axoneme. We subsequently assess the influence of crowding on the 3D exploration of microtubule-binding sites, and specifically on the axial step length. Our calculations combine experimental information on the shape, flexibility and environment of dynein-c from three distinct sources; negative stain electron microscopy, cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET). Our FFEA simulations show that the super-macromolecular organisation of multiple protein complexes into higher-order structures can have a significant influence on the effective flexibility of the individual molecular components, and may, therefore, play an important role in the physical mechanisms underlying their biological function.


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