This work has been supported by the The Funda&#231;&#227;o Oswaldo Cruz (Fiocruz), the Funda&#231;&#227;o para o Desenvolvimento Cient&#237;fico e Tecnol&#243;gico em Sa&#250;de (Fiotec), the Instituto Nacional de Ci&#234;ncia e Tecnologia de Epidemiologia da Amaz&#244;nia Ocidental - INCT-EpiAmO, the Funda&#231;&#227;o Rond&#244;nia de Amparo ao Desenvolvimento das A&#231;&#245;es Cient&#237;ficas e Tecnol&#243;gicas e &#224; Pesquisa do Estado de Rond&#244;nia (FAPERO), the Coordena&#231;&#227;o de Aperfei&#231;oamento de Pessoal de N&#237;vel Superior (CAPES) and the Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq).

1. Accessing the software and new user registration
<ol>
	<li>Visit the Visual Dynamics (VD) web page. Click on the +Register icon at the top right to create an account. Register to use the software. 
	NOTE: Only institutional email addresses are allowed. The user will receive an email notification once their registration is approved.</li>
	<li>Click on Login at the top right to access the system login screen. Fill in the username/email and password fields and click Log in. After logging in, the user will have access to their simulation submission area. They can also view tutorials and usage statistics for VD.</li>
</ol>
2. Apoenzyme simulation submission
<ol>
	<li>Click on New Simulation in the left sidebar. On the screen that appears, click on the button APO, which refers to the apoenzyme.</li>
	<li>Upload the free protein 4mvg.pdb file. Select the AMBER94 force field (or any other appropriate option). 
	NOTE: 4mvg.pdb can be obtained from Supplementary File 1 or downloaded directly from the Protein Data Bank (PDB).</li>
	<li>Select the TIP3P Water Model. Select the Cubic Box. Select 0.5 nm distance between protein and box edge. 
	NOTE: The options selected are just suggestions. All other options work in VD.</li>
	<li>Check the option Run in Our Servers to execute the simulation with the submitted files and parameters. After clicking on Run simulation, the user will see the evolution of the simulation steps on the screen and will receive an email notification about the simulation status upon completion.</li>
</ol>
3. Submission of Simulation of Enzyme Complexed with Ligand Prepared in ACPYPE
<ol>
	<li>Using UCSF Chimera20, open the protein-ligand complex 4mgv.pdb41, under Select, click Residue, and set the code to D5I. Then, under File, click Save PDB, select Save Selected Atoms Only, set the file name to ligand.pdb, and click Save.</li>
	<li>Submit the file ligand.pdb generated in the previous step to the Bio2Byte ACPYPE server42, from the output files, ligand_NEW.itp and ligand_NEW.pdb will be the ones used and provided in this experiment. 
	NOTE: The ligand_NEW.itp and ligand_NEW.pdb files can be obtained from Supplementary File 2, and Supplementary File 3.</li>
	<li>Click on New Simulation on the left sidebar. Click on the button Protein + Ligand (prepared in ACPYPE).</li>
	<li>Upload the free protein 4mvg.pdb file. Select the Ligand Files prepared in ACPYPE: ligand_NEW.itp and ligand_NEW.pdb. Select the AMBER94 force field.</li>
	<li>Select the TIP3P Water Model. Select the Cubic Box. Select 0.5 nm distance between protein and box edge. The options selected are just suggestions. All other options work in VD.</li>
	<li>Check the option Run in Our Servers to execute the simulation with the submitted files and parameters. After clicking on Run simulation, the user will see the evolution of the simulation steps on the screen and will receive an email notification about the simulation status upon completion.</li>
</ol>
4. Accessing the simulation results
<ol>
	<li>Click on My Simulations at the left sidebar.</li>
	<li>Click on Download MDP Files to download the simulation configuration files used by the platform to the user&#39;s computer.</li>
	<li>Download simulation elements as described below.
	<ol>
		<li>Click Commands to download the list of commands executed by the platform to the user&#39;s computer.</li>
		<li>Click GROMACS Log to download the log file containing .gmx command outputs in sequence to the user&#39;s computer.</li>
		<li>Click Results to download files generated by .gmx commands, such as _npt.gro, _pr.edr, _pr.tpr, _pr_PBC.gro, and pr_PBC.xtc to the user&#39;s computer.</li>
		<li>Click Figure Graphics to download graphs for analyzing each simulation step in image and .xvg format to the user&#39;s computer.</li>
	</ol>
	</li>
</ol>

Visual Dynamics: Simplifying Molecular Dynamics Simulations

<ol>
	<li>International Union of Pure and Applied Chemistry (IUPAC). <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The IUPAC Compendium of Chemical Terminology: The Gold Book. , (2019).</li><li>Karplus, M., McCammon, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+dynamics+simulations+of+biomolecules.">Molecular dynamics simulations of biomolecules.</a> Nat Str Biol. 9 (9), 646-652 (2002).</li><li>Adcock, S. A., McCammon, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+dynamics:+Survey+of+methods+for+simulating+the+activity+of+proteins.">Molecular dynamics: Survey of methods for simulating the activity of proteins.</a> Chem Rev. 106 (5), 1589-1615 (2006).</li><li>Stillinger, F. H., Rahman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+simulation+of+liquid+water+by+molecular+dynamics.">Improved simulation of liquid water by molecular dynamics.</a> J Chem Phys. 60 (4), 1545-1557 (1974).</li><li>Zinovjev, K., Van Der Kamp, M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enlighten2:+molecular+dynamics+simulations+of+protein-ligand+systems+made+accessible.">Enlighten2: molecular dynamics simulations of protein-ligand systems made accessible.</a> Bioinformatics. 36 (20), 5104-5106 (2020).</li><li>McCammon, J. A., Gelin, B. R., Karplus, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+folded+proteins.">Dynamics of folded proteins.</a> Nature. 267 (5612), 585-590 (1977).</li><li>Berendsen, H. J. C., Van Der Spoel, D., Van Drunen, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GROMACS:+A+message-passing+parallel+molecular+dynamics+implementation.">GROMACS: A message-passing parallel molecular dynamics implementation.</a> Comp Phys Comm. 91 (1-3), 43-56 (1995).</li><li>Pearlman, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=a+package+of+computer+programs+for+applying+molecular+mechanics,+normal+mode+analysis,+molecular+dynamics+and+free+energy+calculations+to+simulate+the+structural+and+energetic+properties+of+molecules.">a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules.</a> Comp Phys Comm. 91 (1-3), 1-41 (1995).</li><li>Nelson, M. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NAMD:+A+parallel,+object-oriented+molecular+dynamics+program.">NAMD: A parallel, object-oriented molecular dynamics program.</a> Int J Supercomp Appl High Performance Comp. 10 (4), 251-268 (1996).</li><li>Addison-Wesley Pub. Co. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Art of human-computer interface design. , (1990).</li><li>Wigdor, D., Wixon, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Brave NUI World: Designing Natural User Interfaces for Touch and Gesture. , (2014).</li><li>Unwin, A., Heike, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> GUI and Command-line - Conflict or Synergy. , (2000).</li><li>Clark, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developing+Web+Applications.">Developing Web Applications.</a> Beginning C# Object-Oriented Programming. , 243-263 (2011).</li><li>Eswar, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+protein+structure+modeling+using+Modeller.">Comparative protein structure modeling using Modeller.</a> Curr Protoc Bioinformatics. 5, 10 (2006).</li><li>Morris, G. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AutoDock4+and+AutoDockTools4:+Automated+docking+with+selective+receptor+flexibility.">AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.</a> J Comp Chem. 30 (16), 2785-2791 (2009).</li><li>Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., Berendsen, H. J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GROMACS:+Fast,+flexible,+and+free.">GROMACS: Fast, flexible, and free.</a> J Comp Chem. 26 (16), 1701-1718 (2005).</li><li>Guex, N., Peitsch, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SWISS-MODEL+and+the+Swiss-Pdb+Viewer:+An+environment+for+comparative+protein+modeling.">SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling.</a> Electrophoresis. 18 (15), 2714-2723 (1997).</li><li>. The PyMOL molecular graphics system Available from: <a target="_blank" href="https://pymol.org/">https://pymol.org/</a> (2023)</li><li>Humphrey, W., Dalke, A., Schulten, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VMD:+Visual+molecular+dynamics.">VMD: Visual molecular dynamics.</a> J Mol Graph. 14 (1), 33-38 (1996).</li><li>Pettersen, E. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UCSF+Chimera-A+visualization+system+for+exploratory+research+and+analysis.">UCSF Chimera-A visualization system for exploratory research and analysis.</a> J Comp Chem. 25 (13), 1605-1612 (2004).</li><li>. PyRx-python prescription v. 0.8 Available from: <a target="_blank" href="https://pyrx.sourceforge.io/">https://pyrx.sourceforge.io/</a> (2023)</li><li>. BIOVIA Discovery Studio 2017 R2: A comprehensive predictive science application for the Life Sciences Available from: <a target="_blank" href="https://www.3ds.com/products/biovia/discovery-studio">https://www.3ds.com/products/biovia/discovery-studio</a> (2023)</li><li>. Maestro Available from: <a target="_blank" href="https://www.schrodinger.com/platform/products/maestro/">https://www.schrodinger.com/platform/products/maestro/</a> (2024)</li><li>. Molecular operating environment (MOE) Available from: <a target="_blank" href="https://www.chemcomp.com/en/Products.htm">https://www.chemcomp.com/en/Products.htm</a> (2023)</li><li>. The Syntax, Vocabulary and APIs of HTML5 Available from: <a target="_blank" href="https://dev.w3.org/html5/html-author/">https://dev.w3.org/html5/html-author/</a> (2023)</li><li>. Cascading Style Sheets Available from: <a target="_blank" href="https://www.w3.org/Style/CSS/">https://www.w3.org/Style/CSS/</a> (2023)</li><li>. ECMAScript 2020 language specification, 11th edition Available from: <a target="_blank" href="https://www.ecma-international.org/publications-and-standards/standards/ecma-262/">https://www.ecma-international.org/publications-and-standards/standards/ecma-262/</a> (2023)</li><li>Pieper, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ModBase,+a+database+of+annotated+comparative+protein+structure+models,+and+associated+resources.">ModBase, a database of annotated comparative protein structure models, and associated resources.</a> Nucleic Acids Res. 39, D465-D474 (2011).</li><li>Kiefer, F., Arnold, K., Kunzli, M., Bordoli, L., Schwede, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+SWISS-MODEL+Repository+and+associated+resources.">The SWISS-MODEL Repository and associated resources.</a> Nucleic Acids Res. 37, D387-D392 (2009).</li><li>De Vries, S. J., Van Dijk, M., Bonvin, A. M. J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+HADDOCK+web+server+for+data-driven+biomolecular+docking.">The HADDOCK web server for data-driven biomolecular docking.</a> Nat Prot. 5 (5), 883-897 (2010).</li><li>Grosdidier, A., Zoete, V., Michielin, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SwissDock,+a+protein-small+molecule+docking+web+service+based+on+EADock+DSS.">SwissDock, a protein-small molecule docking web service based on EADock DSS.</a> Nucleic Acids Res. 39, W270-W277 (2011).</li><li>Kozakov, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ClusPro+web+server+for+protein-protein+docking.">The ClusPro web server for protein-protein docking.</a> Nat Prot. 12 (2), 255-278 (2017).</li><li>Santos, K. B., Guedes, I. A., Karl, A. L. M., Dardenne, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+flexible+ligand+docking:+Benchmarking+of+the+DockThor+program+on+the+LEADS-PEP+protein-peptide+data+set.">Highly flexible ligand docking: Benchmarking of the DockThor program on the LEADS-PEP protein-peptide data set.</a> J Chem Info Modeling. 60 (2), 667-683 (2020).</li><li>Ribeiro, J. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QwikMD+-+Integrative+molecular+dynamics+toolkit+for+novices+and+experts.">QwikMD - Integrative molecular dynamics toolkit for novices and experts.</a> Sci Rep. 6 (1), 26536 (2016).</li><li>Rodr&#237;guez Mart&#237;nez, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> ASGARD. A simple and automatic GROMACS tool to analyze Molecular Dynamic simulations. , (2023).</li><li>Singh, H., Raja, A., Prakash, A., Medhi, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gmx_qk:+An+automated+protein/protein-ligand+complex+simulation+workflow+bridged+to+MM/PBSA,+based+on+Gromacs+and+Zenity-Dependent+GUI+for+beginners+in+MD+simulation+study.">Gmx_qk: An automated protein/protein-ligand complex simulation workflow bridged to MM/PBSA, based on Gromacs and Zenity-Dependent GUI for beginners in MD simulation study.</a> J Chem Info Modeling. 63 (9), 2603-2608 (2023).</li><li>Yekeen, A. A., Durojaye, O. A., Idris, M. O., Muritala, H. F., Arise, R. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CHAPERONg:+A+tool+for+automated+GROMACS-based+molecular+dynamics+simulations+and+trajectory+analyses.">CHAPERONg: A tool for automated GROMACS-based molecular dynamics simulations and trajectory analyses.</a> Comp Str Biotechnol J. 21, 4849-4858 (2023).</li><li>Bayarri, G., Andrio, P., Hospital, A., Orozco, M., Gelp&#237;, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioExcel+building+blocks+workflows+(BioBB-Wfs),+an+integrated+web-based+platform+for+biomolecular+simulations.">BioExcel building blocks workflows (BioBB-Wfs), an integrated web-based platform for biomolecular simulations.</a> Nucleic Acids Res. 50 (W1), W99-W107 (2022).</li><li>Vieira, I. H. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+dynamics:+a+WEB+application+for+molecular+dynamics+simulation+using+GROMACS.">Visual dynamics: a WEB application for molecular dynamics simulation using GROMACS.</a> BMC Bioinfo. 24 (1), 107 (2023).</li><li>Sch&#252;ttelkopf, A. W., Van Aalten, D. M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PRODRG+a+tool+for+high-throughput+crystallography+of+protein-ligand+complexes.">PRODRG a tool for high-throughput crystallography of protein-ligand complexes.</a> Acta Crystallographica Sect D Biol Crystallography. 60 (8), 1355-1363 (2004).</li><li>Harikishore, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adamantyl+derivative+as+a+potent+inhibitor+of+Plasmodium+FK506+binding+protein+35.">Adamantyl derivative as a potent inhibitor of Plasmodium FK506 binding protein 35.</a> ACS Med Chem Lett. 4 (11), 1097-1101 (2013).</li><li>Sousa Da Silva, A. W., Vranken, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ACPYPE+-+AnteChamber+PYthon+Parser+interfacE.">ACPYPE - AnteChamber PYthon Parser interfacE.</a> BMC Res Notes. 5 (1), 367 (2012).</li><li>. Gromacs developmet and developers Available from: <a target="_blank" href="https://www.gromacs.org/development.html">https://www.gromacs.org/development.html</a> (2023)</li><li>Andrio, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioExcel+building+blocks,+a+software+library+for+interoperable+biomolecular+simulation+workflows.">BioExcel building blocks, a software library for interoperable biomolecular simulation workflows.</a> Sci Data. 6 (1), 169 (2019).</li><li>P&#225;ll, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneous+parallelization+and+acceleration+of+molecular+dynamics+simulations+in+GROMACS.">Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS.</a> J Chem Phys. 153 (13), 134110 (2020).</li></ol>

The authors have nothing to disclose.

Automating processes is not easy, but it is also less difficult than reprogramming a system from scratch. Gromacs is currently the most popular molecular simulation software, and it is constantly updated. The Department of Biophysical Chemistry at Groningen University initially developed it, and it is now maintained by the Life Sciences Laboratory at the University of Stockholm43.
For any new user, learning simulation techniques is a lengthy journey. VD emerges as an al...

According to the IUPAC definition, MD is the simulation procedure that consists of computing the motion of atoms in a molecule or of individual atoms or molecules in solids, liquids, and gases, according to Newton&#39;s laws of motion. The forces acting on atoms, necessary to simulate their motion, are commonly calculated using force fields from molecular mechanics1. It can be applied to any phenomenon that seeks to extract information at a molecular and often atomic level2.
MD is one of the techniques incorporated into bioinformatics, specifically structural bioinformatics. With it, it is possible to obtain kinetic and thermodynamic characteristics of biomolecular structures. For example, macromolecular stability, identification of allosteric sites, elucidation of mechanisms of enzymatic activity, molecular recognition and properties of complexes with small molecules, association between proteins, protein folding, and its hydration3. Furthermore, MD enables a wide range of studies, including molecular design (widely used in drug design), in determining the structure and its refinement (X-ray, NMR, and protein modeling)3. The results obtained at the end of an MD are the richest and most complete in terms of non-quantum simulation4.Classical MD is much more efficient than might be expected from a full consideration of the physics of biomolecular systems due to the number of substantial approximations. Notably, quantum dynamical effects are usually ignored3. However, implementing an MD experiment is not trivial5. It requires knowledge of computing, especially the Linux Terminal, as most structural bioinformatics software is made for it. Even with that knowledge, learning Gromacs commands and parametrization is another steep learning curve.
Since its first application to biology in 19776, much has evolved due to increased computational processing and improved coding. More than two decades ago, the first MD software intended for biological problems was launched, namely Gromacs7, AMBER8, and NAMD9.
Since their first versions, these software still remain the most used and cited. However, they continue with the same common implementation difficulties that plague researchers who are not computer specialists5.&#160;Some have complex installation and configuration steps, sometimes requiring extensive knowledge about the hardware it will run on to get the most out of it and highly computer-centric technical documentation. An easier way to interface with them, aside from the command line and infinite parameters, is needed.
An interface acts as an intermediary between the logical process to be performed and the human10. The paradigm of how software is executed has evolved as computing resources have improved. The first digital paradigm was the command line interfaces (CLI) followed by the evolution to the known graphical user interfaces (GUI)11. Following the evolutionary cycle, the interface produced by the World Wide Web (or simply WEB) is considered an evolution of GUIs11. These three paradigms currently co-exist depending on developers. CLI applications use textual commands on the operating system console. GUI applications, also called graphical desktops, use a graphical interface made up of windows, buttons, and other components. It is specific and pre-programmed for an operating system. The main difference from the CLI is the use of the mouse as an additional element in human-machine interaction12. WEB applications, despite being confused with a GUI, are more complex to develop but are more versatile and by far the most agile in operation. Furthermore, they only depend on an interpreter software called a browser, which makes it possible for the client application to communicate with the server through a network independent of the operating system13.
Structural bioinformatics software most commonly use CLI and GUI paradigms. Some examples of classic software that use CLI are Modeller14 for similarity modeling, Autodock15 for molecular docking, and Gromacs16 for molecular dynamics. Examples of software that adopt the GUI type are SwissPDBviewer17, Pymol18, VMD19, UCSF Chimera20, Autodock tools15, PyRx21, Biovia22, Maestro23, and Moe24, among others.
With the emergence of Hypertext Markup Language version 5 (HTML5)25, Cascading Style Sheets (CSS)26, and Javascript27 technologies, among others, many structural bioinformatics applications could be brought to the WEB, thus becoming more accessible. Examples of similarity modeling WEB servers are MODWEB28, which uses Modeller14 as a back-end and Swissmodel29. Examples of web application servers for molecular docking are Haddock30, Swissdock31, Cluspro32, Dockthor33, and others.
While structural analysis, modeling, and docking methodologies evolved from CLI paradigms to GUI and finally to WEB, MD continues to be mostly supported by command line execution (CLI type). Some good initiatives have emerged to improve this panorama. Examples of these initiatives are the implementation of plugins in existing software, such as QwikMD plugin to VMD34, GROMACS Plugin to PyMOL, and the Molecular Dynamics Simulation option in UCSF Chimera20, some new and easier CLI applications, such as ASGARD35, Gmx_qk36, and CHAPERONg37, and a robust web platform, BioBB-Wfs38.&#160;Although the use of these plugins and applications is an advance, their implementation is still a challenge for most unskilled researchers. Common difficulties include problems installing and configuring the MD software, which often compromise the full execution of the simulation5.
In 2022, the Visual Dynamics software for web-based computational simulation was made available by the Laborat&#243;rio de Bioinform&#225;tica e Qu&#237;mica Medicinal at Fiocruz Rond&#244;nia39. Its initial version was built in Python and Flask, allowing simulations of systems with free proteins (apoenzymes) for only 2 ns. Subsequently, it was enhanced to include an automated simulation version with ligands prepared using PRODRG40.
VD was built to assist all researchers in the field of structural biophysics, biotechnology, and related areas who have limitations in computational knowledge; the tool allows these researchers to test their hypotheses involving MD simulations from any operational system and without access to a high-performance computer (HPC). The purpose of this work is to present the new features of Visual Dynamics version 3.0. Additionally, it aims to introduce an updated usage protocol for the tool and highlight the limitations to be addressed in the future, along with usage statistics up to the present moment (Figure 1).

<table><tbody><tr><td>ACPYPE Server</td><td>Bio2Byte</td><td></td><td>Available at https://www.bio2byte.be/acpype/</td></tr><tr><td>GRACE software</td><td>Plasma Laboratory at the Weizmann Institute of Science</td><td></td><td>Available at https://plasma-gate.weizmann.ac.il/Grace/</td></tr><tr><td>GROMACS software</td><td>GROMACS Team</td><td></td><td>Installation instructions at https://manual.gromacs.org/current/install-guide/index.html</td></tr><tr><td>The structure of the FK506-binding protein&lt;br/&gt; From Plasmodium vivax complexed with the&lt;br/&gt; inhibitor D5</td><td>RCSB Protein Data Bank</td><td></td><td>Available at https://www.rcsb.org/structure/4mgv&lt;br/&gt; Already contains the ligand complexed to the macromolecule.</td></tr></tbody></table>

new features in visual dynamics 3.0

Visual Dynamics (VD) is a web tool that aims to facilitate the use and application of Molecular Dynamics (MD) executed in Gromacs, allowing users without computational familiarity to run short-time simulations for validation, demonstration, and teaching purposes. It is true that quantum methods are the most accurate. However, there is currently no computational feasibility to carry out the experiments that MD performs. The tool described here has continuously received improvements over the course of the last couple of years. This protocol will describe what is needed to run a simulation in VD with a protein-ligand complex previously prepared in ACPYPE and some general directions on the other simulation models available. For the detailed simulation, the FK506-binding protein from Plasmodium vivax complexed with the inhibitor D5 (PDB ID: 4mgv) will be used, and all files used will be provided. Note that this protocol will tell every option to be used to achieve the same results presented, but these options are not necessarily the only ones available.

VD provides a fully autonomous simulation execution that does not require user intervention or user-provided computational resources. After submitting a simulation to execution, the user can leave it, turn off their machines, and the simulation will continue running. It also allows users to access the results from any device, be it a laptop or mobile device.
As an example of using VD in automated mode through the WEB, the test was made for a protein-ligand complex prepared in ACPYPE using the ...

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Visual Dynamics is an open-source tool that accelerates implementations and learning in molecular dynamics simulation using Gromacs. The presented protocol will guide you through the steps to perform a protein-ligand simulation prepared in ACPYPE with ease and general steps to other simulation models.

New Features in Visual Dynamics 3.0

Visual Dynamics (VD) is a web tool that aims to facilitate the use and application of Molecular Dynamics (MD) executed in Gromacs, allowing users without ...

Research

JoVE Journal

Biochemistry

773 Views.  Oswaldo Cruz Rondônia Foundation. Visual Dynamics is an open-source tool that accelerates implementations and learning in molecular dynamics simulation using Gromacs. The presented protocol will guide you through the steps to perform a protein-ligand simulation prepared in ACPYPE with ease and general steps to other simulation models.

Video: Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

Biology

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth.&#160;However, unlike a stereo-based method, DFP uses a digital video projector to replace one of the cameras1. The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera&#8217;s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation.
Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop.&#160;DFP systems can also achieve the same measurement resolution as the camera.&#160;For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in1-5). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30&#160;Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame6,7.&#160;Binary defocusing DFP methods can achieve even greater speeds8.
Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis9, facial animation10, cardiac mechanics studies11, and fluid surface measurements, but many other potential applications exist.&#160;This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.

This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces.&#160;It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses ...

Engineering

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

The increasing development of computing (hardware and software) in the last decades has impacted scientific research in many fields including materials science, biology, chemistry and physics among many others. A new computational system for the accurate and fast simulation and 3D/VR visualization of nanostructures is presented here, using the open-source molecular dynamics (MD) computer program LAMMPS. This alternative computational method uses modern graphics processors, NVIDIA CUDA technology and specialized scientific codes to overcome processing speed barriers common to traditional computing methods. In conjunction with a virtual reality system used to model materials, this enhancement allows the addition of accelerated MD simulation capability. The motivation is to provide a novel research environment which simultaneously allows visualization, simulation, modeling and analysis. The research goal is to investigate the structure and properties of inorganic nanostructures (e.g., silica glass nanosprings) under different conditions using this innovative computational system. The work presented outlines a description of the 3D/VR Visualization System and basic components, an overview of important considerations such as the physical environment, details on the setup and use of the novel system, a general procedure for the accelerated MD enhancement, technical information, and relevant remarks. The impact of this work is the creation of a unique computational system combining nanoscale materials simulation, visualization and interactivity in a virtual environment, which is both a research and teaching instrument at UC Merced.

A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.

The increasing development of computing (hardware and software) in the last decades has impacted scientific research in many fields including materials ...

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2.
Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.
First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.
Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.
Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms.
Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper.
We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.
Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.

We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object ...

Neuroscience

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway.

We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality ...

Medicine

Real-Time Recording and Mapping of Human Interaction with 3D Virtual Objects

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes.
The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant&#39;s gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker.
The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a &#34;ball of rotations&#34;; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material.
With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating ...

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don't have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.

The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle ...

Streamlines, Streaklines, and Pathlines

A streamline represents the trajectory that is always tangent to the fluid&#39;s velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline&#39;s path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow&#39;s velocity field. In steady flow, where conditions do not change over time, streamlines offer a consistent picture of the flow pattern.
Streaklines, in contrast, trace the position of particles that have passed through a specific point over time. When a dye is introduced at a fixed location in the flow, it reveals the path fluid particles take as they move through that point, effectively showing a time-integrated history of their positions. Streaklines can differ from streamlines in unsteady flow because they track particles over time, whereas streamlines are instantaneous.
A pathline is a single fluid particle&#39;s trajectory as it moves through space over time. For example, the path of a leaf floating downstream in a river represents a pathline. Pathlines coincide with streamlines and streaklines in a steady flow, where conditions are constant, making them indistinguishable. However, in unsteady flow, where velocity and flow direction change over time, pathlines can diverge from streamlines and streaklines as each particle follows a unique path through the fluid. These distinctions are important for analyzing fluid motion under varying flow conditions.

Consider water flowing steadily through a curved pipe. The direction of flow inside the pipe can be visualized using streamlines.
A streamline represents ...

Education

JoVE Core

Civil Engineering

Unit 1

Fluid Kinematics

KEY TERMS AND CONCEPTS

Virtual Work for a System of Connected Rigid Bodies

Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next, measure the position coordinates from a fixed point on the free-body diagram and direct them toward the forces that do the work. This allows for each virtual displacement to be expressed in terms of the independent coordinate. Next step is to write down the equation of the virtual work, which helps in the determination of the resultant forces and moments. It is also important to note here that if a force or couple moment acts along the direction of positive virtual displacement, then their work is determined as positive virtual work; otherwise, it&#39;s negative virtual work.
The main advantage of using this method is that it can be used to solve problems that involve a system of several connected rigid bodies Each of these systems is said to have only one degree of freedom since the arrangement of the links can be completely specified using only one coordinate. This greatly simplifies complex calculations and helps better understand how different variables are related to each other within complex systems.
All in all, analytically evaluating equilibrium problems involving multiple connected rigid bodies can be done effectively with virtual work, enabling not just accurate answers but also clear insight into their internal workings as well.

Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is ...

Mechanical Engineering

Virtual Work

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a flexible, transparent structure that adjusts its shape to focus light accurately onto the retina. This process, known as accommodation, enables the eye to focus on objects at varying distances. The retina, a thin layer of tissue lining the back of the eye, captures the incoming light and initiates the conversion of light into electrical signals. This conversion is performed by photoreceptors, which are specialized cells known as rods and cones. Rods are highly sensitive to low light levels and are responsible for night vision, while cones detect color and provide sharp central vision.
The electrical signals generated by photoreceptors are transmitted to bipolar cells, which serve as intermediaries, relaying the information to ganglion cells. The axons of the ganglion cells bundle together to form the optic nerve. This nerve acts as a pathway, transmitting visual information from the eye to the brain. Specifically, the optic nerve carries these signals to the primary visual cortex located in the occipital lobe of the brain.
Within the visual cortex, neurons are specialized to interpret the incoming signals, allowing for the perception of basic visual features. Feature detectors, a type of specialized neuron in the brain&#39;s visual system, respond to specific characteristics of a visual stimulus, such as edges, shapes, colors, and movements. This specialization is crucial for detailed visual analysis and recognition.
Parallel processing, a feature of the brain&#39;s visual system, enhances the speed and efficiency of sensory information processing by distributing tasks across multiple neural pathways simultaneously. This concurrent processing allows the brain to manage and interpret vast amounts of visual information rapidly. The process of binding integrates this diverse information from different neural pathways or cells, resulting in a cohesive and unified visual perception. This integration is essential for creating a seamless and comprehensive understanding of the visual environment.

Light enters the eye through the cornea, a thin transparent layer that refracts light to help focus it as it enters the eye, directing the light towards ...