molecular dynamics simulations with visual dynamics for protein-ligand complex studies

Visuell dynamik: Förenkla simuleringar av molekyldynamik

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).

Författare i fokus: Strömlinjeformning av visuell dynamik för att förenkla simuleringar av molekyldynamik med hjälp av Gromacs

Nya funktioner i Visual Dynamics 3.0

Our objective is to provide the academic community with a tool that facilitates preparation and simulation using molecular dynamics. Is it possible to provide this goal in our web application? Is it possible for a beginner use it effectively?We seek to answer these questions with our software. There are many challenges in molecular dynamics. The ones that stand out among are the preparation in parameterization of ligands, the learning curve of simulation guidelines, the simulation time, and the implementation of new analysis.We are mainly addressing the learning curve for beginning research in creating a secret pipeline to guide all users, from beginners to advanced. The high demand for access to this tool demonstrates a gap in bioscience research. We believe that the development of this tool represents a significant advancement in the use and learning of molecular dynamics.Consequently, more people will be able to implement these tests in their research. In the future, we intend to expand the parameterization and execution of complex simulation with more force fields.

Molekyldynamiksimuleringar med visuell dynamik för studier av protein-ligandkomplex

molecular-dynamics-simulations-with-visual-dynamics-for-protein

Research

JoVE Journal

Biochemistry

Molecular Dynamics Simulations with Visual Dynamics for Protein-Ligand Complex Studies

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.

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.

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 ...

To begin, open the Visual Dynamics or VD webpage and click on login to access the system login screen. After logging in, the simulation submission area, tutorials, and usage statistics can be accessed. For APO enzymes simulation submission, click on new simulation in the sidebar and click on the button APO.Upload the free protein 4mgv. pdb file and select the AMBER94 Force field. Select the TIP3P water model, followed by the Cubic box.Select 0.5 nanometer distance between the protein and the box edge. Check the option run in our servers to execute the simulation. Using UCSF Chimera, open the protein ligand complex 4mgv.pdv, 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.Go to the Bio2Byte ACPYPE server and submit the generated ligand. pdb file from the output files. Click on new simulation, followed by protein ligand.Upload the free protein 4mgv. pdb file and select the ligand files prepared in ACPYPE followed by MBER94 Force Field. Select the TIP3P water model followed by the Cubic box, and select 0.5 nanometer distance between protein and box edge.Check the option run in our servers to execute the simulation. Click on my simulations in the sidebar, and then on download MDP files to download the simulation configuration files used. Click commands to download the list of commands executed by the platform, and click GROMACS Log to download the LOG file containing GMX command outputs.Click results to download files generated by the GMX commands, and finally, click figure graphics to download graphs for analyzing each simulation step in image and XVG format to the computer. The protein backbone exhibited a root mean square deviation of less than 2.5 angstroms, and the radius of duration showed that the protein maintained its compactness in X, Y, and Z coordinates during the five nanosecond simulation. The average fluctuation distance of each amino acid in the protein structure, as shown by root mean square fluctuation, remained consistent throughout the five nanosecond simulation.The system stabilized with a maximum force of less than 1000 kilojoules per mol per nanometer during the energy minimization process.