scholarly journals MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories

2011 ◽  
Vol 27 (23) ◽  
pp. 3276-3285 ◽  
Author(s):  
Peter Schmidtke ◽  
Axel Bidon-Chanal ◽  
F. Javier Luque ◽  
Xavier Barril
2020 ◽  
Author(s):  
Michael Gecht ◽  
Marc Siggel ◽  
Max Linke ◽  
Gerhard Hummer ◽  
Juergen Koefinger

Molecular dynamics simulations resolve biomolecular processes and material properties with incomparable detail. As a result, they consume a significant fraction of worldwide supercomputing resources. With our open source benchmarking software MDBenchmark, expert and novice users alike can easily determine the optimal settings for their specific simulation system, MD engine, software environment, and hardware configuration. Ultimately, saving computation time, energy, and money at essentially no additional cost will produce better science.<br>


2018 ◽  
Author(s):  
Pascal T. Merz ◽  
Michael R. Shirts

<p>Advances in recent years have made molecular dynamics (MD) and Monte Carlo (MC) simulations powerful tools in molecular-level research, allowing the prediction of experimental observables in the study of systems such as proteins, membranes, and polymeric materials. However, the quality of any prediction based on molecular dynamics results will strongly depend on the validity of underlying physical assumptions.</p> <p>Unphysical behavior of simulations can have significant influence on the results and reproducibility of these simulations, such as folding of proteins and DNA or properties of lipid bilayers determined by cutoff treatment, dynamics of peptides and polymers affected by the choice of thermostat, or liquid properties depending on the simulation time step. Motivated by such examples, we propose a two-fold approach to increase the robustness of molecular simulations. The first part of this approach involves tests which can be performed by the users of MD programs on their respective systems and setups. We present a number of tests of different complexity, ranging from simple post-processing analysis to more involved tests requiring additional simulations. These tests are shown to significantly increase the reliability of MD simulations by catching a number of common simulation errors violating physical assumptions, such as non-conservative integrators, deviations from the Boltzmann ensemble, and lack of ergodicity between degrees of freedom. To make the usage as easy as possible, we have developed an open-source and platform-independent Python library (https://physical-validation.readthedocs.io) implementing these tests.</p> <p>The second part of the approach involves testing for code correctness. While unphysical behavior can be due to poor or incompatible choices of parameters by the user, it can just as well originate in coding errors within the program. We therefore propose to include physical validation tests in the code-checking mechanism of MD software packages. We have implemented such a validation for the GROMACS software package, ensuring that every major releases passes a number of physical sanity checks performed on selected representative systems before shipping. It is, to our knowledge, the first major molecular mechanics software package to run such validation routinely. The tests are, as the rest of the package, open source software, and can be adapted for other software packages.</p>


Author(s):  
Simon Bennie ◽  
Kara Ranaghan ◽  
Helen Deeks ◽  
Heather Goldsmith ◽  
Mike O'Connor ◽  
...  

<div> <div> <p>The reemergence of virtual reality (VR) in the last few years has led to affordable commodity hardware that can offer new ways to teach, communicate and engage with difficult concepts, especially those which involve complicated 3D motion and spatial manipulation. In a higher education context, these immersive technologies make it possible to teach complex molecular topics in a way that may aid or even supersede traditional approaches such as molecular models, textbook images, and traditional screen-based computational environments. In this work we describe a study involving 24 third-year UK undergraduate chemistry students who undertook a traditional computational chemistry class complemented with an additional component utilising real-time interactive molecular dynamics simulations in VR (iMD-VR). Exploiting the flexibility of an open-source iMD-VR framework which we recently described,(1) and building on recent work where we demonstrated the ability to use this framework to run ‘on-the-fly’ density functional theory in VR at interactive speeds,2 we designed three tasks for students to complete in iMD-VR: (1) interactive rearrangement of the chorismate molecule to prephenate using forces obtained from ‘on-the-fly’ density functional theory calculations; (2) unbinding of chorismate from the active site chorismate mutase enzyme using molecular-mechanics forces calculated in real-time; and (3) docking of chorismate with chorismate mutase using real-time molecular mechanics forces. A survey indicated that most students found the iMD-VR component more engaging than the traditional approach, and also that it improved their perceived educational outcomes and their interest in continuing on in the field of computational sciences. </p></div> </div>


Author(s):  
Simon Bennie ◽  
Kara Ranaghan ◽  
Helen Deeks ◽  
Heather Goldsmith ◽  
Mike O'Connor ◽  
...  

<div> <div> <p>The reemergence of virtual reality (VR) in the last few years has led to affordable commodity hardware that can offer new ways to teach, communicate and engage with difficult concepts, especially those which involve complicated 3D motion and spatial manipulation. In a higher education context, these immersive technologies make it possible to teach complex molecular topics in a way that may aid or even supersede traditional approaches such as molecular models, textbook images, and traditional screen-based computational environments. In this work we describe a study involving 24 third-year UK undergraduate chemistry students who undertook a traditional computational chemistry class complemented with an additional component utilising real-time interactive molecular dynamics simulations in VR (iMD-VR). Exploiting the flexibility of an open-source iMD-VR framework which we recently described,(1) and building on recent work where we demonstrated the ability to use this framework to run ‘on-the-fly’ density functional theory in VR at interactive speeds,2 we designed three tasks for students to complete in iMD-VR: (1) interactive rearrangement of the chorismate molecule to prephenate using forces obtained from ‘on-the-fly’ density functional theory calculations; (2) unbinding of chorismate from the active site chorismate mutase enzyme using molecular-mechanics forces calculated in real-time; and (3) docking of chorismate with chorismate mutase using real-time molecular mechanics forces. A survey indicated that most students found the iMD-VR component more engaging than the traditional approach, and also that it improved their perceived educational outcomes and their interest in continuing on in the field of computational sciences. </p></div> </div>


Author(s):  
Sumith Yesudasan

In this paper, we introduce a simple yet powerful and working version of the molecular dynamics code using the Python 3.9 language. The code contents are published in the link given in the appendix 1. The structure and components of the program is given in detail using flowcharts and code snippets. The program consists of major features like velocity verlet integrator, thermostats, COM removal, input and output modules, virial, pressure, and other thermodynamic quantities estimation etc. The author believes that this program will be helpful to graduate students who perform research in molecular dynamics simulations who intend to write their own code instead of the sophisticated open source packages.


2017 ◽  
Author(s):  
Jonathan Barnoud ◽  
Hubert Santuz ◽  
Pierrick Craveur ◽  
Agnel Praveen Joseph ◽  
Vincent Jallu ◽  
...  

ABSTRACTProteins are highly dynamic macromolecules. A classical way to analyze their inner flexibility is to perform molecular dynamics simulations. In this context, we present the advantage to use small structural prototypes, namely the Protein Blocks (PBs). PBs give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, they allow analyzes of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is a suite of tools to analyze the dynamics and deformability of protein structures using PBs. It is able to process large amount of data such as those produced by molecular dynamics simulations. It produces various outputs with text and graphics, such as frequencies, entropy and information logo. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.


2014 ◽  
Vol 35 (9) ◽  
pp. 756-764 ◽  
Author(s):  
Bruce M. Allen ◽  
Paul K. Predecki ◽  
Maciej Kumosa

2019 ◽  
Vol 150 (22) ◽  
pp. 220901 ◽  
Author(s):  
Michael B. O’Connor ◽  
Simon J. Bennie ◽  
Helen M. Deeks ◽  
Alexander Jamieson-Binnie ◽  
Alex J. Jones ◽  
...  

2021 ◽  
Vol 5 (2) ◽  
pp. 30
Author(s):  
Andrea Albano ◽  
Eve le Guillou ◽  
Antoine Danzé ◽  
Irene Moulitsas ◽  
Iwan H. Sahputra ◽  
...  

LAMMPS is a powerful simulator originally developed for molecular dynamics that, today, also accounts for other particle-based algorithms such as DEM, SPH, or Peridynamics. The versatility of this software is further enhanced by the fact that it is open-source and modifiable by users. This property suits particularly well Discrete Multiphysics and hybrid models that combine multiple particle methods in the same simulation. Modifying LAMMPS can be challenging for researchers with little coding experience. The available material explaining how to modify LAMMPS is either too basic or too advanced for the average researcher. In this work, we provide several examples, with increasing level of complexity, suitable for researchers and practitioners in physics and engineering, who are familiar with coding without been experts. For each feature, step by step instructions for implementing them in LAMMPS are shown to allow researchers to easily follow the procedure and compile a new version of the code. The aim is to fill a gap in the literature with particular reference to the scientific community that uses particle methods for (discrete) multiphysics.


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