Enhancing MD simulations: ASGARD’s automated analysis for GROMACS

Abstract

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.

Communicated by Ramaswamy H. Sarma

Keywords:

• Structural bioinformatics
• molecular modeling
• molecular dynamics
• GROMACS
• analysis

Software and data availability

ASGARD is available in the GitHub repository: https://github.com/bio-hpc/ASGARD. The repository contains all the files necessary for running the tool, as well as a README.md file that indicates the download link and installation instructions.

Additionally, the data generated by ASGARD for the case studies and the input data used for these analyses are deposited in the following Zenodo link: https://zenodo.org/records/10213139.

Acknowledgments

This research was funded by JUNTA ANDALUCIA GRANT, the Andalusian Regional Government through the Grant: Proyectos de Excelencia (P18-RT-1193); the Programa Regional de Fomento de la Investigación (Plan de Actuación 2018, Región de Murcia, Spain) through the Grant: ‘Ayudas a la realización de proyectos para el desarrollo de investigación científica y técnica por grupos competitivos (20988/PI/18)’. This work was partially supported by the supercomputing infrastructure at Poznan Supercomputing Center, the e-infrastructure program of the Research Council of Norway, the supercomputer center at UiT—the Arctic University of Norway, and the computing facilities at the Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT) and funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain. The authors also acknowledge the computing resources and technical support provided by the Plataforma Andaluza de Bioinformática at the University of Málaga. Powered@NLHPC research was partially supported by the super-computing infrastructure at the NLHPC (ECM-02).

Authors contributions

A.R.: original draft preparation. A.R. and C.M.: methodology, software development. A.R., J.N., M.C., C.M., I.L., and H.P.: Review & editing. I.L. and H.P.: Supervision. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Plan Propio de Investigación de la UCAM. It formed part of the AGROALNEXT program and was supported by MCIN with funding from European Union NextGenerationEU [PRTR-C17.I1]. Also, it was supported by projects PY20_00678 (Ayudas para la realización de proyectos de I + D + I) and B-BIO-18-UGR20 (Proyectos I + D + i en el marco del programa operativo FEDER) from the Andalussian Government and FEDER funds. Supercomputing resources in this work were supported by the super-computing infrastructure of the NLHPC (ECM-02) and Extremadura Research Center for Advanced Technologies’s (CETA-CIEMAT) High Performance Computing Cluster. J.N. is a predoctoral researcher employed to the training of research staff and financed by Eurofins VillaPharma. M.C.-B is a predoctoral student founded by the Plan Propio de Investigación, UCAM.