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Szczuka, M.

Publications and source records attributed to Szczuka, M..

2 recordsLinked to original sources

Fast parameterization of Martini3 models for fragments and small molecules

Coarse-grained molecular dynamics simulations, such as those performed with the recently parametrized Martini 3 force field, simplify molecular models and enable the study of larger systems over longer timescales. With this new implementation, Martini 3 allows more bead types and sizes, becoming more amenable to study dynamical phenomena involving small molecules such as protein-ligand interactions and membrane permeation. However, while there were some solutions to automatically model small molecules using the previous iteration of Martini force field, there is no simple way to generate such molecules for Martini 3 yet. Here, we introduce Auto-MartiniM3, an advanced and updated version of the Auto-Martini program, designed to automate the coarse-graining of small molecules to be used with the Martini 3 force field. We validated our approach by modeling 81 small molecules from the Martini Database and comparing their structural and thermodynamic properties with ones obtained from models designed by Martini experts. Additionally, we assessed the behavior of Auto-MartiniM3-generated models by calculating solute translocation and free energy across lipid bilayers. We also evaluated more complex molecules such as caffeine by testing its binding to the adenosine A2A receptor. Finally, our results from deploying Auto-MartiniM3 on a large dataset of molecular fragments demonstrate that this program can become a tool of choice for fast high-throughput creation of coarse-grained models of small molecules, offering a good balance between automation and accuracy. Auto-MartiniM3 source code is freely available at https://github.com/Martini-Force-Field-Initiative/Automartini_M3

bioinformatics↗

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD [fi]les in generalist data repositories, constituting the dark matter of MD -- data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 [fi]les and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on [fi]les produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identi[fi]ed systems with speci[fi]c molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

bioinformatics↗