Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories
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| Title: | Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories |
|---|---|
| Authors: | Abdul Wasim, Lars V. Schäfer, Jagannath Mondal |
| Publication Year: | 2025 |
| Collection: | Bath Spa University: Figshare |
| Subject Terms: | Biophysics, Genetics, Biotechnology, Computational Biology, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, https :// github, disk space compared, artificial neural network, thus facilitating storage, large protein complexes, intrinsically disordered proteins, including folded proteins, bound protein systems, biomolecular simulation studies, compress md trajectories, simulation trajectories, biomolecular systems, share large, optimal storage, simulation programs, larger systems, composite systems, tested across, scientific community, remarkable stride, publicly available, practical solution, phospholipid bilayers |
| Description: | With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation data sets has emerged. By leveraging AutoEncoders, a type of artificial neural network, we developed a method to compress MD trajectories into significantly smaller latent spaces. Our method can save up to 98% in disk space compared to xtc, a highly compressed trajectory format from the widely used MD program package GROMACS, thus facilitating storage and sharing of simulation trajectories. Atom coordinates are very accurately reconstructed from compressed data. The method was tested across a diverse sets of biomolecular systems, including folded proteins, intrinsically disordered proteins, phospholipid bilayers, protein–ligand complexes, large protein complexes and membrane-bound protein systems. The reconstructed trajectories demonstrated consistent accuracy in recovering key biophysically relevant properties for proteins, lipids and composite systems. The compression efficiency was particularly beneficial for larger systems. This approach enables the scientific community to efficiently store and share large-scale biomolecular simulation data, potentially enhancing collaborative research efforts. The workflow, termed “compresstraj”, is implemented in PyTorch and is publicly available at https://github.com/SerpentByte/compresstraj, offering a practical solution for handling the increasing volumes of data generated in biomolecular simulation studies. |
| Document Type: | article in journal/newspaper |
| Language: | unknown |
| Relation: | https://figshare.com/articles/journal_contribution/Employing_Artificial_Neural_Networks_for_Optimal_Storage_and_Facile_Sharing_of_Molecular_Dynamics_Simulation_Trajectories/29930872 |
| DOI: | 10.1021/acs.jcim.5c01294.s001 |
| Availability: | https://doi.org/10.1021/acs.jcim.5c01294.s001 https://figshare.com/articles/journal_contribution/Employing_Artificial_Neural_Networks_for_Optimal_Storage_and_Facile_Sharing_of_Molecular_Dynamics_Simulation_Trajectories/29930872 |
| Rights: | CC BY-NC 4.0 |
| Accession Number: | edsbas.319166D4 |
| Database: | BASE |
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