Speaker
Description
Molecular dynamics (MD) simulations and binding free energy calculations are critical techniques in computational chemistry and molecular biology. This study presents the implementation of an advanced tool designed to fully automate system preparation, start or extend MD simulations, perform trajectory analysis, and compute binding free energies (using gmx_MMPBSA), along with detailed protein-ligand interaction analyses. The pipeline supports diverse systems, including protein-only, protein-cofactors, protein-ligand, and protein-ligand-cofactors complexes in explicit water environments. By requiring only a PDB file for the protein and, where necessary, SDF or MOL files for ligands or cofactors, the tool streamlines the input process.
The tool's efficiency is significantly enhanced through the use of the Dask Python library, which enables parallelization and distributed computing across a network of servers via SSH connections independent from a particular scheduler. It also supports GPU-accelerated computations, offering a substantial reduction in processing time. Advanced features include the Gaussian-based parameterization of non-standard ligands (such as boron-containing molecules) and MCPB.py parameterization for atoms involved in metal coordination, making the tool highly versatile for complex molecular systems.
For validation we run 1 ns simulations and calculated the GBSA energies for 161 molecules of human β-secretase 1 (UniProt ID: P56817), 63 molecules of human α-thrombin (UniProt ID: P00734) and 51 molecules of bovine trypsin (UniProt ID: P00760). The resulting Pearson correlation coefficients between GBSA energies and experimental activity values were found to be -0.67 for the β-secretase 1, -0.53 for the trypsin and -0.72 for the α-thrombin datasets. The StreaMD and MM-GBSA method were applied in advanced high-throughput screening process during the Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge #1 competition as a final ranking procedure to select 150 molecules from 900 candidates obtained from the previous ranking stages. The achieved hit rate was ~10% (8 compounds were active out of 82 total studied ones).
The tool is available as an open-source package at https://github.com/ci-lab-cz/streamd/tree/master.
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The work was supported by the Ministry of Education, Youth and Sports of the Czech Republic through INTER_EXCELLENCE II grant LUAUS23262, the e-INFRA CZ (ID:90254) and projects ELIXIR-CZ (LM2023055) and CZ-OPENSCREEN (LM2023052).
