Split the Charge Difference in Two! a Rule of Thumb for Adding Proper Amounts of Ions in MD Simulations

2020 ◽  
Author(s):  
Matías R. Machado ◽  
Sergio Pantano
Keyword(s):  
Molecular Dynamics ◽  
Ionic Strength ◽  
Molecular Simulations ◽  
Md Simulations ◽  
Good Practices ◽  
Rule Of Thumb ◽  
Ionic Concentrations

<p> Despite the relevance of properly setting ionic concentrations in Molecular Dynamics (MD) simulations, methods or practical rules to set ionic strength are scarce and rarely documented. Based on a recently proposed thermodynamics method we provide an accurate rule of thumb to define the electrolytic content in simulation boxes. Extending the use of good practices in setting up MD systems is promptly needed to ensure reproducibility and consistency in molecular simulations.</p>

Split the Charge Difference in Two! a Rule of Thumb for Adding Proper Amounts of Ions in MD Simulations

2020 ◽  
Author(s):  
Matías R. Machado ◽  
Sergio Pantano
Keyword(s):  
Molecular Dynamics ◽  
Ionic Strength ◽  
Molecular Simulations ◽  
Md Simulations ◽  
Good Practices ◽  
Rule Of Thumb ◽  
Ionic Concentrations

<p> Despite the relevance of properly setting ionic concentrations in Molecular Dynamics (MD) simulations, methods or practical rules to set ionic strength are scarce and rarely documented. Based on a recently proposed thermodynamics method we provide an accurate rule of thumb to define the electrolytic content in simulation boxes. Extending the use of good practices in setting up MD systems is promptly needed to ensure reproducibility and consistency in molecular simulations.</p>

Split the Charge Difference in Two! a Rule of Thumb for Adding Proper Amounts of Ions in MD Simulations

2019 ◽  
Author(s):  
Matías R. Machado ◽  
Sergio Pantano
Keyword(s):  
Molecular Dynamics ◽  
Ionic Strength ◽  
Molecular Simulations ◽  
Md Simulations ◽  
Good Practices ◽  
Rule Of Thumb ◽  
Ionic Concentrations

<p> Despite the relevance of properly setting ionic concentrations in Molecular Dynamics (MD) simulations, methods or practical rules to set ionic strength are scarce and rarely documented. Based on a recently proposed thermodynamics method we provide an accurate rule of thumb to define the electrolytic content in simulation boxes. Extending the use of good practices in setting up MD systems is promptly needed to ensure reproducibility and consistency in molecular simulations.</p>

scholarly journals Sharing Data from Molecular Simulations

2019 ◽  
Author(s):  
Mark J. Abraham ◽  
Rossen Apostolov ◽  
Jonathan Barnoud ◽  
Paul Bauer ◽  
Christian Blau ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Best Practices ◽  
Molecular Dynamics Simulations ◽  
Software Package ◽  
Molecular Simulations ◽  
Md Simulations ◽  
Scientific Output ◽  
Level Of Detail ◽  
Simulation Data ◽  
Dynamics Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( <a href="https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/">https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/</a>). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way towards more open, interoperable and reproducible outputs coming from research studies using MD simulations.

scholarly journals Sharing Data from Molecular Simulations

2019 ◽  
Author(s):  
Mark J. Abraham ◽  
Rossen Apostolov ◽  
Jonathan Barnoud ◽  
Paul Bauer ◽  
Christian Blau ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Best Practices ◽  
Molecular Dynamics Simulations ◽  
Software Package ◽  
Molecular Simulations ◽  
Md Simulations ◽  
Scientific Output ◽  
Level Of Detail ◽  
Simulation Data ◽  
Dynamics Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( <a href="https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/">https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/</a>). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way towards more open, interoperable and reproducible outputs coming from research studies using MD simulations.

scholarly journals Testing for Physical Validity in Molecular Simulations

2018 ◽  
Author(s):  
Pascal T. Merz ◽  
Michael R. Shirts
Keyword(s):  
Molecular Dynamics ◽  
Open Source ◽  
Lipid Bilayers ◽  
Software Package ◽  
Degrees Of Freedom ◽  
Molecular Simulations ◽  
Polymeric Materials ◽  
Md Simulations ◽  
Time Step ◽  
Software Packages

<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>

scholarly journals Testing for Physical Validity in Molecular Simulations

2018 ◽  
Author(s):  
Pascal T. Merz ◽  
Michael R. Shirts
Keyword(s):  
Molecular Dynamics ◽  
Open Source ◽  
Lipid Bilayers ◽  
Software Package ◽  
Degrees Of Freedom ◽  
Molecular Simulations ◽  
Polymeric Materials ◽  
Md Simulations ◽  
Time Step ◽  
Software Packages

<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>

scholarly journals Testing for Physical Validity in Molecular Simulations

2018 ◽  
Author(s):  
Pascal T. Merz ◽  
Michael R. Shirts
Keyword(s):  
Molecular Dynamics ◽  
Open Source ◽  
Lipid Bilayers ◽  
Software Package ◽  
Degrees Of Freedom ◽  
Molecular Simulations ◽  
Polymeric Materials ◽  
Md Simulations ◽  
Time Step ◽  
Software Packages

<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>

Efficient Treatment of Fixed and Induced Dipolar Interactions

2000 ◽  
Vol 653 ◽  
Author(s):  
Celeste Sagui ◽  
Thoma Darden
Keyword(s):  
Molecular Dynamics ◽  
Md Simulations ◽  
Lagrangian Formalism ◽  
Dipolar Interactions ◽  
Time Step ◽  
Efficient Treatment ◽  
Particle Mesh Ewald ◽  
Fixed Charges ◽  
Self Consistency ◽  
Induced Dipoles

AbstractFixed and induced point dipoles have been implemented in the Ewald and Particle-Mesh Ewald (PME) formalisms. During molecular dynamics (MD) the induced dipoles can be propagated along with the atomic positions either by interation to self-consistency at each time step, or by a Car-Parrinello (CP) technique using an extended Lagrangian formalism. The use of PME for electrostatics of fixed charges and induced dipoles together with a CP treatment of dipole propagation in MD simulations leads to a cost overhead of only 33% above that of MD simulations using standard PME with fixed charges, allowing the study of polarizability in largemacromolecular systems.

Assessing the Antimalarial Potentials of Phytochemicals: Virtual Screening, Molecular Dynamics and In-Vitro Investigations

2019 ◽  
Vol 16 (3) ◽  
pp. 291-300
Author(s):  
Saumya K. Patel ◽  
Mohd Athar ◽  
Prakash C. Jha ◽  
Vijay M. Khedkar ◽  
Yogesh Jasrai ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Structural Integrity ◽  
Md Simulations ◽  
Tylophora Indica ◽  
Multidrug Resistance Protein 1 ◽  
Receptor Complexes ◽  
Resistance Protein ◽  
Dynamics Simulations ◽  
Stable Trajectory

Background: Combined in-silico and in-vitro approaches were adopted to investigate the antiplasmodial activity of Catharanthus roseus and Tylophora indica plant extracts as well as their isolated components (vinblastine, vincristine and tylophorine). </P><P> Methods: We employed molecular docking to prioritize phytochemicals from a library of 26 compounds against Plasmodium falciparum multidrug-resistance protein 1 (PfMDR1). Furthermore, Molecular Dynamics (MD) simulations were performed for a duration of 10 ns to estimate the dynamical structural integrity of ligand-receptor complexes. </P><P> Results: The retrieved bioactive compounds viz. tylophorine, vinblastin and vincristine were found to exhibit significant interacting behaviour; as validated by in-vitro studies on chloroquine sensitive (3D7) as well as chloroquine resistant (RKL9) strain. Moreover, they also displayed stable trajectory (RMSD, RMSF) and molecular properties with consistent interaction profile in molecular dynamics simulations. </P><P> Conclusion: We anticipate that the retrieved phytochemicals can serve as the potential hits and presented findings would be helpful for the designing of malarial therapeutics.

Effect of Ionic Strength on the Aggregation Propensity of Aβ1-42 Peptide: An In-silico Study

2020 ◽  
Vol 14 (3) ◽  
pp. 216-226
Author(s):  
Priyanka Borah ◽  
Venkata S.K. Mattaparthi
Keyword(s):  
Diffusion Coefficient ◽  
Ionic Strength ◽  
Marked Effect ◽  
Computational Study ◽  
Conformational Dynamics ◽  
Rapid Change ◽  
Md Simulations ◽  
Radius Of Gyration ◽  
Aggregation Propensity ◽  
In Silico Study

Background: Aggregation of misfolded proteins under stress conditions in the cell might lead to several neurodegenerative disorders. Amyloid-beta (Aβ1-42) peptide, the causative agent of Alzheimer’s disease, has the propensity to fold into β-sheets under stress, forming aggregated amyloid plaques. This is influenced by factors such as pH, temperature, metal ions, mutation of residues, and ionic strength of the solution. There are several studies that have highlighted the importance of ionic strength in affecting the folding and aggregation propensity of Aβ1-42 peptide. Objective: To understand the effect of ionic strength of the solution on the aggregation propensity of Aβ1-42 peptide, using computational approaches. Materials and Methods: In this study, Molecular Dynamics (MD) simulations were performed on Aβ1-42 peptide monomer placed in (i) 0 M, (ii) 0.15 M, and (iii) 0.30 M concentration of NaCl solution. To prepare the input files for the MD simulations, we have used the Amberff99SB force field. The conformational dynamics of Aβ1-42 peptide monomer in different ionic strengths of the solutions were illustrated from the analysis of the corresponding MD trajectory using the CPPtraj tool. Results: From the MD trajectory analysis, we observe that with an increase in the ionic strength of the solution, Aβ1-42 peptide monomer shows a lesser tendency to undergo aggregation. From RMSD and SASA analysis, we noticed that Aβ1-42 peptide monomer undergoes a rapid change in conformation with an increase in the ionic strength of the solution. In addition, from the radius of gyration (Rg) analysis, we observed Aβ1-42 peptide monomer to be more compact at moderate ionic strength of the solution. Aβ1-42 peptide was also found to hold its helical secondary structure at moderate and higher ionic strengths of the solution. The diffusion coefficient of Aβ1-42 peptide monomer was also found to vary with the ionic strength of the solution. We observed a relatively higher diffusion coefficient value for Aβ1-42 peptide at moderate ionic strength of the solution. Conclusion: Our findings from this computational study highlight the marked effect of ionic strength of the solution on the conformational dynamics and aggregation propensity of Aβ1-42 peptide monomer.

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