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Journal articles on the topic 'GROMACS'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Irrgang, M. Eric, Caroline Davis, and Peter M. Kasson. "gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support." PLOS Computational Biology 18, no. 2 (February 14, 2022): e1009835. http://dx.doi.org/10.1371/journal.pcbi.1009835.

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Gmxapi provides an integrated, native Python API for both standard and advanced molecular dynamics simulations in GROMACS. The Python interface permits multiple levels of integration with the core GROMACS libraries, and legacy support is provided via an interface that mimics the command-line syntax, so that all GROMACS commands are fully available. Gmxapi has been officially supported since the GROMACS 2019 release and is enabled by default in current versions of the software. Here we describe gmxapi 0.3 and later. Beyond simply wrapping GROMACS library operations, the API permits several advanced operations that are not feasible using the prior command-line interface. First, the API allows custom user plugin code within the molecular dynamics force calculations, so users can execute custom algorithms without modifying the GROMACS source. Second, the Python interface allows tasks to be dynamically defined, so high-level algorithms for molecular dynamics simulation and analysis can be coordinated with loop and conditional operations. Gmxapi makes GROMACS more accessible to custom Python scripting while also providing support for high-level data-flow simulation algorithms that were previously feasible only in external packages.

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2

van der Spoel, David, and Berk Hess. "GROMACS—the road ahead." WIREs Computational Molecular Science 1, no. 5 (April 25, 2011): 710–15. http://dx.doi.org/10.1002/wcms.50.

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3

Briones, Rodolfo, Christian Blau, Carsten Kutzner, Bert L. de Groot, and Camilo Aponte-Santamaría. "GROmaρs: A GROMACS-Based Toolset to Analyze Density Maps Derived from Molecular Dynamics Simulations." Biophysical Journal 116, no. 1 (January 2019): 4–11. http://dx.doi.org/10.1016/j.bpj.2018.11.3126.

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4

Briones, Rodolfo, Christian Blau, Carsten Kutzner, Bert L. de Groot, and Camilo Aponte-Santamaría. "Gromaps: A Gromacs-Based Toolset to Analyse Density Maps Derived from Molecular Dynamics Simulations." Biophysical Journal 116, no. 3 (February 2019): 142a—143a. http://dx.doi.org/10.1016/j.bpj.2018.11.790.

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5

van der Spoel, D., P. J. van Maaren, and C. Caleman. "GROMACS molecule & liquid database." Bioinformatics 28, no. 5 (January 11, 2012): 752–53. http://dx.doi.org/10.1093/bioinformatics/bts020.

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6

Nava, M. "Implementing dimer metadynamics using gromacs." Journal of Computational Chemistry 39, no. 25 (September 30, 2018): 2126–32. http://dx.doi.org/10.1002/jcc.25386.

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7

Van Der Spoel, David, Erik Lindahl, Berk Hess, Gerrit Groenhof, Alan E. Mark, and Herman J. C. Berendsen. "GROMACS: Fast, flexible, and free." Journal of Computational Chemistry 26, no. 16 (2005): 1701–18. http://dx.doi.org/10.1002/jcc.20291.

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8

Nguyen, Trang Truc, Man Hoang Viet, and Mai Suan Li. "Effects of Water Models on Binding Affinity: Evidence from All-Atom Simulation of Binding of Tamiflu to A/H5N1 Neuraminidase." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/536084.

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The influence of water models SPC, SPC/E, TIP3P, and TIP4P on ligand binding affinity is examined by calculating the binding free energyΔGbindof oseltamivir carboxylate (Tamiflu) to the wild type of glycoprotein neuraminidase from the pandemic A/H5N1 virus.ΔGbindis estimated by the Molecular Mechanic-Poisson Boltzmann Surface Area method and all-atom simulations with different combinations of these aqueous models and four force fields AMBER99SB, CHARMM27, GROMOS96 43a1, and OPLS-AA/L. It is shown that there is no correlation between the binding free energy and the water density in the binding pocket in CHARMM. However, for three remaining force fieldsΔGbinddecays with increase of water density. SPC/E provides the lowest binding free energy for any force field, while the water effect is the most pronounced in CHARMM. In agreement with the popular GROMACS recommendation, the binding score obtained by combinations of AMBER-TIP3P, OPLS-TIP4P, and GROMOS-SPC is the most relevant to the experiments. For wild-type neuraminidase we have found that SPC is more suitable for CHARMM than TIP3P recommended by GROMACS for studying ligand binding. However, our study for three of its mutants reveals that TIP3P is presumably the best choice for CHARMM.

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9

Sellis, Diamantis, Dimitrios Vlachakis, and Metaxia Vlassi. "Gromita: A Fully Integrated Graphical user Interface to Gromacs 4." Bioinformatics and Biology Insights 3 (January 2009): BBI.S3207. http://dx.doi.org/10.4137/bbi.s3207.

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Gromita is a fully integrated and efficient graphical user interface (GUI) to the recently updated molecular dynamics suite Gromacs, version 4. Gromita is a cross-platform, perl/tcl-tk based, interactive front end designed to break the command line barrier and introduce a new user-friendly environment to run molecular dynamics simulations through Gromacs. Our GUI features a novel workflow interface that guides the user through each logical step of the molecular dynamics setup process, making it accessible to both advanced and novice users. This tool provides a seamless interface to the Gromacs package, while providing enhanced functionality by speeding up and simplifying the task of setting up molecular dynamics simulations of biological systems. Gromita can be freely downloaded from http://bio.demokritos.gr/gromita/ .

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10

Hyun, Haelee, Do Heon Kim, and Young-Ouk Lee. "Validation of Thermal Neutron Scattering Cross Sections for Heavy Water based on Molecular Dynamics Simulation." EPJ Web of Conferences 211 (2019): 06001. http://dx.doi.org/10.1051/epjconf/201921106001.

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Recently, the thermal scattering libraries of ENDF/B-VIII.0 and JEFF-3.3 for light and heavy water were released with a new water model (CAB model) proposed by Damian. For the CAB model, the molecular dynamics code GROMACS was used to more accurately describe the realistic motions of water molecules. In this paper, to consider the coherent component we also generated the thermal scattering cross section of the deuterium and oxygen bound in the heavy water molecules using the GROMACS code and EPSR code. In addition, the frequency spectrum was also calculated using the GROMACS code. Thermal scattering cross sections based on the newly calculated Sköld correction factor and the frequency spectrum were generated by NJOY2016 code. Finally, the performance of the generated thermal scattering cross sections were validated by performing an ICSBEP benchmark simulation using MCNPX code.

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11

Smith, Andrea, Xin Dong, and Vijaya Raghavan. "An Overview of Molecular Dynamics Simulation for Food Products and Processes." Processes 10, no. 1 (January 7, 2022): 119. http://dx.doi.org/10.3390/pr10010119.

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Molecular dynamics (MD) simulation is a particularly useful technique in food processing. Normally, food processing techniques can be optimized to favor the creation of higher-quality, safer, more functional, and more nutritionally valuable food products. Modeling food processes through the application of MD simulations, namely, the Groningen Machine for Chemical Simulations (GROMACS) software package, is helpful in achieving a better understanding of the structural changes occurring at the molecular level to the biomolecules present in food products during processing. MD simulations can be applied to define the optimal processing conditions required for a given food product to achieve a desired function or state. This review presents the development history of MD simulations, provides an in-depth explanation of the concept and mechanisms employed through the running of a GROMACS simulation, and outlines certain recent applications of GROMACS MD simulations in the food industry for the modeling of proteins in food products, including peanuts, hazelnuts, cow’s milk, soybeans, egg whites, PSE chicken breast, and kiwifruit.

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12

TAMAKI, Teppei, Tomohiro KABASHIMA, Kazuki MORI, Satoshi MINAMOTO, and Kazuyoshi UEDA. "Development of Gromacs Support Program: GISP (2)." Journal of Computer Chemistry, Japan 13, no. 3 (2014): 159–60. http://dx.doi.org/10.2477/jccj.2014-0018.

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13

Ullmann, R. Thomas, and Helmut Grubmueller. "A Versatile Lambda-Dynamics Module for GROMACS." Biophysical Journal 118, no. 3 (February 2020): 138a. http://dx.doi.org/10.1016/j.bpj.2019.11.881.

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14

Rawat, Ravi, Kamal Kant, Anoop Kumar, Kajal Bhati, and Saurabh M. Verma. "HeroMDAnalysis: an automagical tool for GROMACS-based molecular dynamics simulation analysis." Future Medicinal Chemistry 13, no. 5 (March 2021): 447–56. http://dx.doi.org/10.4155/fmc-2020-0191.

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Background & objective: Molecular dynamics simulations (MDS) using GROMACS are among the commonly used computational experiments in the area of molecular biology and drug discovery. This article presents a project called HeroMDAnalysis, an automagical tool to analyze the GROMACS-based MDS trajectories and generate plots as high-quality images for various parameters. Materials & methods: The tool was built using bash shell programming, and graphical user interface was built using Zenity engine. Results & conclusion: This tool offers a simple, semiautomated, and relatively fast framework for what was previously a complex, manual, time-consuming and error-prone task, presenting a useful method for biochemists and synthetic chemists with no prior experience of the command line interface.

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15

Aragones, J. L., E. G. Noya, C. Valeriani, and C. Vega. "Free energy calculations for molecular solids using GROMACS." Journal of Chemical Physics 139, no. 3 (July 21, 2013): 034104. http://dx.doi.org/10.1063/1.4812362.

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16

Bussi, Giovanni. "Hamiltonian replica exchange in GROMACS: a flexible implementation." Molecular Physics 112, no. 3-4 (August 25, 2013): 379–84. http://dx.doi.org/10.1080/00268976.2013.824126.

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17

Kutzner, Carsten, David Van Der Spoel, Martin Fechner, Erik Lindahl, Udo W. Schmitt, Bert L. De Groot, and Helmut Grubmüller. "Speeding up parallel GROMACS on high-latency networks." Journal of Computational Chemistry 28, no. 12 (2007): 2075–84. http://dx.doi.org/10.1002/jcc.20703.

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18

Abraham, Mark J. "Performance enhancements for GROMACS nonbonded interactions on BlueGene." Journal of Computational Chemistry 32, no. 9 (April 5, 2011): 2041–46. http://dx.doi.org/10.1002/jcc.21766.

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19

Berendsen, H. J. C., D. van der Spoel, and R. van Drunen. "GROMACS: A message-passing parallel molecular dynamics implementation." Computer Physics Communications 91, no. 1-3 (September 1995): 43–56. http://dx.doi.org/10.1016/0010-4655(95)00042-e.

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20

Faccioli, R. A., L. O. Bortot, and A. C. B. Delbem. "Multi-Objective Evolutionary Algorithm NSGA-II for Protein Structure Prediction using Structural and Energetic Properties." International Journal of Natural Computing Research 4, no. 1 (January 2014): 43–53. http://dx.doi.org/10.4018/ijncr.2014010104.

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The Protein Structure Prediction (PSP) problem is concerned about the prediction of the native tertiary structure of a protein in respect to its amino acids sequence. PSP is a challenging and computationally open problem. Therefore, several researches and methodologies have been developed for it. In this way, developers are working to integrate frameworks in order to improve their capabilities and make their use more straightforward. This paper presents the application of NSGA-II algorithm using structural and energetic properties of protein. The implementation of this algorithm is based on ProtPred-GROMACS (2PG), an evolutionary framework for PSP. This framework is the integration between ProtPred and GROMACS. Six proteins were used to measure the capacity of ab initio predictions. The results were interesting since in all cases the native-like topology was obtained.

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21

Sinelnikova, Anna, and David van der Spoel. "NMR refinement and peptide folding using the GROMACS software." Journal of Biomolecular NMR 75, no. 4-5 (March 28, 2021): 143–49. http://dx.doi.org/10.1007/s10858-021-00363-z.

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AbstractNuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins and nucleic acids. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package it is possible to model distance restraints, dihedral restraints and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

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22

Noel, Jeffrey K., Paul C. Whitford, Karissa Y. Sanbonmatsu, and Josï¿½ N. Onuchic. "SMOG@ctbp: simplified deployment of structure-based models in GROMACS." Nucleic Acids Research 38, suppl_2 (June 4, 2010): W657—W661. http://dx.doi.org/10.1093/nar/gkq498.

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23

Makarewicz, Tomasz, and Rajmund Kaźmierkiewicz. "Molecular Dynamics Simulation by GROMACS Using GUI Plugin for PyMOL." Journal of Chemical Information and Modeling 53, no. 5 (May 6, 2013): 1229–34. http://dx.doi.org/10.1021/ci400071x.

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24

Kumari, Rashmi, Rajendra Kumar, and Andrew Lynn. "g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations." Journal of Chemical Information and Modeling 54, no. 7 (June 19, 2014): 1951–62. http://dx.doi.org/10.1021/ci500020m.

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25

Vermaas, Josh V., David J. Hardy, John E. Stone, Emad Tajkhorshid, and Axel Kohlmeyer. "TopoGromacs: Automated Topology Conversion from CHARMM to GROMACS within VMD." Journal of Chemical Information and Modeling 56, no. 6 (June 2016): 1112–16. http://dx.doi.org/10.1021/acs.jcim.6b00103.

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26

Páll, Szilárd, Artem Zhmurov, Paul Bauer, Mark Abraham, Magnus Lundborg, Alan Gray, Berk Hess, and Erik Lindahl. "Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS." Journal of Chemical Physics 153, no. 13 (October 7, 2020): 134110. http://dx.doi.org/10.1063/5.0018516.

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27

Dien, Hung, Charlotte M. Deane, and Bernhard Knapp. "Gro2mat: A package to efficiently read gromacs output in MATLAB." Journal of Computational Chemistry 35, no. 20 (June 12, 2014): 1528–31. http://dx.doi.org/10.1002/jcc.23650.

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28

Lindahl, Erik, Berk Hess, and David van der Spoel. "GROMACS 3.0: a package for molecular simulation and trajectory analysis." Journal of Molecular Modeling 7, no. 8 (August 2001): 306–17. http://dx.doi.org/10.1007/s008940100045.

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29

Kohnke, Bartosz, Carsten Kutzner, and Helmut Grubmuller. "A GPU-Accelerated Fast Multipole Method for Gromacs. Performance and Accuracy." Biophysical Journal 120, no. 3 (February 2021): 177a. http://dx.doi.org/10.1016/j.bpj.2020.11.1242.

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30

Lukat, Gunther, Jens Krüger, and Björn Sommer. "APL@Voro: A Voronoi-Based Membrane Analysis Tool for GROMACS Trajectories." Journal of Chemical Information and Modeling 53, no. 11 (November 11, 2013): 2908–25. http://dx.doi.org/10.1021/ci400172g.

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31

van Dijk, Marc, Tsjerk A. Wassenaar, and Alexandre M. J. J. Bonvin. "A Flexible, Grid-Enabled Web Portal for GROMACS Molecular Dynamics Simulations." Journal of Chemical Theory and Computation 8, no. 10 (April 18, 2012): 3463–72. http://dx.doi.org/10.1021/ct300102d.

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32

Kohnke, Bartosz, Carsten Kutzner, and Helmut Grubmüller. "A GPU-Accelerated Fast Multipole Method for GROMACS: Performance and Accuracy." Journal of Chemical Theory and Computation 16, no. 11 (October 21, 2020): 6938–49. http://dx.doi.org/10.1021/acs.jctc.0c00744.

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33

Kutzner, Carsten, Szilárd Páll, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, and Helmut Grubmüller. "Best bang for your buck: GPU nodes for GROMACS biomolecular simulations." Journal of Computational Chemistry 36, no. 26 (August 4, 2015): 1990–2008. http://dx.doi.org/10.1002/jcc.24030.

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34

Kutzner, Carsten, R. Thomas Ullmann, Bert L. de Groot, Ulrich Zachariae, and Helmut Grubmueller. "Ions in Action - Studying Ion Channels by Computational Electrophysiology in GROMACS." Biophysical Journal 112, no. 3 (February 2017): 139a. http://dx.doi.org/10.1016/j.bpj.2016.11.769.

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35

Zhang, Yan, Yu-Yue Chen, Ling Lin, and Pi-Bo Ma. "Nanostructure characterization of beta-sheet crystals in silk under various temperatures." Thermal Science 18, no. 5 (2014): 1459–61. http://dx.doi.org/10.2298/tsci1405459z.

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This paper studies the nanostructure characterizations of ?-sheet in silk fiber with different reaction temperatures. A molecular dynamic model is developed and simulated by Gromacs software packages. The results reveal the change rules of the number of hydrogen bonds in ?-sheet under different temperatures. The best reaction temperature for the ?-sheet crystals is also found. This work provides theoretical basis for the designing of materials based on silk.

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36

Reinhardt, Martin, and Helmut Grubmüller. "GROMACS implementation of free energy calculations with non-pairwise Variationally derived Intermediates." Computer Physics Communications 264 (July 2021): 107931. http://dx.doi.org/10.1016/j.cpc.2021.107931.

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37

Peng, Shaoliang, Yingbo Cui, Shunyun Yang, Wenhe Su, Xiaoyu Zhang, Tenglilang Zhang, Weiguo Liu, and Xingming Zhao. "A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 2 (March 1, 2019): 425–33. http://dx.doi.org/10.1109/tcbb.2017.2713362.

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38

Kutzner, Carsten, Jacek Czub, and Helmut Grubmüller. "Keep It Flexible: Driving Macromolecular Rotary Motions in Atomistic Simulations with GROMACS." Journal of Chemical Theory and Computation 7, no. 5 (March 31, 2011): 1381–93. http://dx.doi.org/10.1021/ct100666v.

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39

Hess, Berk, Carsten Kutzner, David van der Spoel, and Erik Lindahl. "GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation." Journal of Chemical Theory and Computation 4, no. 3 (February 2, 2008): 435–47. http://dx.doi.org/10.1021/ct700301q.

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40

Krüger, J., and G. Fels. "Ion permeation simulations by Gromacs-an example of high performance molecular dynamics." Concurrency and Computation: Practice and Experience 23, no. 3 (November 4, 2010): 279–91. http://dx.doi.org/10.1002/cpe.1666.

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41

Kubař, Tomáš, Kai Welke, and Gerrit Groenhof. "New QM/MM implementation of the DFTB3 method in the gromacs package." Journal of Computational Chemistry 36, no. 26 (August 4, 2015): 1978–89. http://dx.doi.org/10.1002/jcc.24029.

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42

Klimovich, Pavel V., and David L. Mobley. "A Python tool to set up relative free energy calculations in GROMACS." Journal of Computer-Aided Molecular Design 29, no. 11 (October 20, 2015): 1007–14. http://dx.doi.org/10.1007/s10822-015-9873-0.

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43

Kutzner, Carsten, Jacek Czub, and Helmut Grubmueller. "Keep it Flexible: Driving Macromolecular Rotary Motions in Atomistic Simulations with Gromacs." Biophysical Journal 102, no. 3 (January 2012): 171a. http://dx.doi.org/10.1016/j.bpj.2011.11.927.

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44

Kutzner, Carsten, Christian Kniep, Austin Cherian, Ludvig Nordstrom, Helmut Grubmüller, Bert L. de Groot, and Vytautas Gapsys. "GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design." Journal of Chemical Information and Modeling 62, no. 7 (March 30, 2022): 1691–711. http://dx.doi.org/10.1021/acs.jcim.2c00044.

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45

Macchiagodena, Marina, Maurice Karrenbrock, Marco Pagliai, and Piero Procacci. "Virtual Double-System Single-Box for Absolute Dissociation Free Energy Calculations in GROMACS." Journal of Chemical Information and Modeling 61, no. 11 (November 1, 2021): 5320–26. http://dx.doi.org/10.1021/acs.jcim.1c00909.

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46

Carvalho Martins, Luan, Elio A. Cino, and Rafaela Salgado Ferreira. "PyAutoFEP: An Automated Free Energy Perturbation Workflow for GROMACS Integrating Enhanced Sampling Methods." Journal of Chemical Theory and Computation 17, no. 7 (June 18, 2021): 4262–73. http://dx.doi.org/10.1021/acs.jctc.1c00194.

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47

Valdés-Tresanco, Mario S., Mario E. Valdés-Tresanco, Pedro A. Valiente, and Ernesto Moreno. "gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS." Journal of Chemical Theory and Computation 17, no. 10 (September 29, 2021): 6281–91. http://dx.doi.org/10.1021/acs.jctc.1c00645.

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48

Abraham, Mark James, Teemu Murtola, Roland Schulz, Szilárd Páll, Jeremy C. Smith, Berk Hess, and Erik Lindahl. "GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers." SoftwareX 1-2 (September 2015): 19–25. http://dx.doi.org/10.1016/j.softx.2015.06.001.

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49

Kohnke, Bartosz, R. Thomas Ullmann, Carsten Kutzner, Andreas Beckmann, David Haensel, Ivo Kabadshow, Holger Dachsel, Berk Hess, and Helmut Grubmüller. "A Flexible, GPU - Powered Fast Multipole Method for Realistic Biomolecular Simulations in Gromacs." Biophysical Journal 112, no. 3 (February 2017): 448a. http://dx.doi.org/10.1016/j.bpj.2016.11.2402.

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50

Pronk, Sander, Szilárd Páll, Roland Schulz, Per Larsson, Pär Bjelkmar, Rossen Apostolov, Michael R. Shirts, et al. "GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit." Bioinformatics 29, no. 7 (February 13, 2013): 845–54. http://dx.doi.org/10.1093/bioinformatics/btt055.

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