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Bacteriophage T7 gp4 helicase has served as a model system for understanding mechanisms of hexameric replicative helicase translocation. The mechanistic basis of how nucleoside 5′-triphosphate hydrolysis and translocation of gp4 helicase are coupled is not fully resolved. Here, we used a thermodynamically benchmarked coarse-grained protein force field, Associative memory, Water mediated, Structure and Energy Model (AWSEM), with the single-stranded DNA (ssDNA) force field 3SPN.2C to investigate gp4 translocation. We found that the adenosine 5′-triphosphate (ATP) at the subunit interface stabilizes the subunit–subunit interaction and inhibits subunit translocation. Hydrolysis of ATP to adenosine 5′-diphosphate enables the translocation of one subunit, and new ATP binding at the new subunit interface finalizes the subunit translocation. The LoopD2 and the N-terminal primase domain provide transient protein–protein and protein–DNA interactions that facilitate the large-scale subunit movement. The simulations of gp4 helicase both validate our coarse-grained protein–ssDNA force field and elucidate the molecular basis of replicative helicase translocation.
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Finding Closure Models to Match the Time Evolution of Coarse Grained 2D Turbulence Flows Using Machine Learning
Machine learning is used to develop closure terms for coarse grained model of two-dimensional turbulent flow directly from the coarse grained data by ensuring that the coarse-grained flow evolves in the correct way, with no need for the exact form of the filters or an explicit expression of the subgrid terms. The closure terms are calculated to match the time evolution of the coarse field and related to the average flow using a Neural Network with a relatively simple structure. The time dependent coarse grained flow field is generated by filtering fully resolved results and the predicted coarse field evolution agrees well with the filtered results in terms of instantaneous vorticity field in the short term and statistical quantities (energy spectrum, structure function and enstropy) in the long term, both for the flow used to learn the closure terms and for flows not used for the learning. This work shows the potential of using data-driven method to predict the time evolution of the large scales, in a complex situation where the closure terms may not have an explicit expression and the original fully resolved field is not available.
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- Award ID(s):
- 1953082
- PAR ID:
- 10328664
- Date Published:
- Journal Name:
- Fluids
- Volume:
- 7
- Issue:
- 5
- ISSN:
- 2311-5521
- Page Range / eLocation ID:
- 154
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fluids7050154
