Molecular dynamic simulation for protein & ligand-protein complex: web tool free easy, only click
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- เผยแพร่เมื่อ 4 ต.ค. 2024
- In this video, I would like to show you how to perform molecular dynamic simulation for protein and ligand-protein complex: web tool free.
Simlab is a research initiative for creating web tools for various computational biology research including macromolecular simulations. The aim of WebGro is to make user friendly computational biology tools available for all types of researchers. WebGro is a fully automated online tool for performing molecular dynamics simulation of macromolecules (proteins) alone or in complex with ligands (small molecules).
WebGro uses the GROMACS simulation package for performing fully solvated molecular dynamics simulations. Users submit only their protein file (with .pdb extension), and WebGro will perform simulation as well as trajectory analysis. Parameters in the submission page are set to default values based on published work related to GROMACS simulations. Users are advised to refer to related papers and tutorials for in-depth understanding of each parameter.
Link:
Simlab
simlab.uams.ed...
VMD:
www.ks.uiuc.ed...
Videomach
videomach.en.s...
Article Related:
www.sciencedir...
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Link:
Simlab web tool
simlab.uams.edu/index.php
VMD:
www.ks.uiuc.edu/Research/vmd/
Videomach
videomach.en.softonic.com/
Article Related:
DOI: 10.1080/07391102.2023.2259482
DOI: 10.1080/07391102.2023.2238068
www.sciencedirect.com/science/article/pii/S1567576923000498?via%3Dihub
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can we perform the studies with small molecules?
Yeap. you can try. I think it is very quick to receive the result.
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Is it possible to have publication and research by only docking studies of molecules without doing their synthesis.? What studies and docking results it requires ? Pl.
yes. you can see my publication. www.sciencedirect.com/science/article/abs/pii/S1476927123000634?via%3Dihub
@@duc-hainguyen8499 great...! But as a chemist I am unable to do such detailed qsar or docking studies so how can I... Is there any other studies to get publisable data..? How did you get the ic50 values?
@@unboxing4u766
As a chemist, there are several experimental approaches you can take to generate publishable data without delving into detailed QSAR or docking studies. Here are some experimental strategies:
1. Experimental Design:
Well-Designed Experiments: Ensure your experiments are well-designed and address clear scientific questions. The quality of the experimental design is crucial for generating reliable and publishable data.
2. Biological Assays:
Bioassays: Conduct biological assays to assess the activity of compounds. You've mentioned pIC50 values, indicating you might be working with pharmacological compounds. Standard assays such as enzyme inhibition, cell viability, or receptor binding assays can provide valuable data.
3. Structure-Activity Relationship (SAR) Studies:
SAR Studies: While not as complex as QSAR, SAR studies involve systematically varying the chemical structure of a compound to understand its effect on biological activity. This can provide valuable insights into the relationship between structure and activity.
4. Mechanistic Studies:
Mechanistic Studies: Investigate the mechanisms of action of your compounds. This could involve studying their effects on specific pathways, understanding interactions with cellular components, or exploring their pharmacokinetics.
5. Toxicology and Safety Assessments:
Toxicology Studies: If relevant to your compounds, perform toxicity studies. Understanding the safety profile of your compounds is crucial for their potential development.
6. Collaboration:
Collaborate with Computational Scientists: If you're not comfortable with detailed computational studies, consider collaborating with experts in computational chemistry or bioinformatics. They can help analyze your experimental data using computational approaches.
7. Peer-Reviewed Journals:
Target Journals: Choose reputable journals in your field for publication. Review the types of studies they publish and tailor your research accordingly.
8. Reproducibility:
Reproducibility: Ensure your experiments are reproducible. Document your methods thoroughly, use appropriate controls, and provide sufficient details in your manuscripts.
9. Conferences:
Present at Conferences: Presenting your work at conferences not only helps you gain feedback but also establishes your presence in the scientific community.
10. Open Science:
kotlin
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- **Open Science Practices:** Consider adopting open science practices. Sharing data, protocols, and materials can enhance the transparency and reproducibility of your work.
IC50 Determination:
If you obtained IC50 values, it likely involved experimental work such as dose-response curves. Here's a simplified outline:
Cell Culture or Enzyme Preparation:
Grow cells or prepare enzymes as per your experimental design.
Compound Treatment:
Treat cells or enzymes with varying concentrations of your compounds.
Assay Execution:
Perform the assay (e.g., cell viability, enzyme inhibition) for each concentration.
Data Analysis:
Generate dose-response curves and determine the concentration at which the response is reduced by 50% (IC50).
Statistical Analysis:
Perform statistical analysis to ensure the reliability of your data.
Repetition and Validation:
Repeat experiments to validate your findings.
Remember, the key to publishable data is the scientific rigor, clarity, and relevance of your experimental approach and results. Always consider seeking feedback from peers and mentors throughout the research process.
Hi, I have question related to diffusion barrier. On which factors the diffusion barrier of the material depends. Usually it changes by changing the paths. If two atoms are located at equal distance and the we consider two pathways for two similar atoms but the diffusion barrier is different for two atoma , why?
The diffusion barrier, also known as the activation energy for diffusion, is influenced by various factors, and its variation between two similar atoms can be attributed to the complex interplay of these factors. Here are some key considerations:
Crystal Structure:
Lattice Structure: The crystal lattice structure plays a crucial role. In some materials, certain crystallographic directions or planes might facilitate easier diffusion due to lower energy barriers.
Chemical Bonding:
Bond Strength: The strength of the bonds between atoms in the material affects the diffusion barrier. Weaker bonds may allow for easier diffusion.
Defects and Vacancies:
Vacancy Concentrations: The presence of defects and vacancies can significantly affect diffusion. High concentrations of vacancies provide more pathways for atoms to move, potentially lowering the diffusion barrier.
Temperature:
Thermal Activation: Diffusion barriers are often temperature-dependent due to thermal activation. Higher temperatures generally lead to lower activation energies, making diffusion easier.
Pathway Geometry:
Crystallographic Direction: The specific crystallographic direction along which diffusion occurs can influence the barrier. For example, if a material has anisotropic properties, diffusion may be easier in certain directions.
External Pressure:
Pressure Conditions: Changes in external pressure conditions can impact diffusion barriers. High pressures might hinder or promote diffusion depending on the material.
Quantum Effects:
Quantum Tunnelling: Quantum mechanical effects, such as tunneling, can contribute to diffusion. In some cases, atoms can overcome a barrier by tunneling through it, especially at lower temperatures.
Surface Effects:
Surface Diffusion: On surfaces or grain boundaries, diffusion barriers may differ from bulk diffusion due to surface interactions and restructuring.
Electronic Structure:
Electronic Properties: The electronic structure of the material, including band structure and electronic states, can influence diffusion. For instance, materials with electronic states that facilitate migration may have lower diffusion barriers.
Size and Mass of Diffusing Species:
Atomic Mass: The mass of the diffusing species can influence the diffusion barrier. Lighter atoms may diffuse more easily than heavier ones.
Presence of Alloying Elements:
Alloying Effects: If the material is an alloy, the presence of different elements can affect the diffusion barrier. Alloying can modify the crystal structure and bonding, influencing diffusion.
The complex nature of diffusion barriers means that variations between seemingly similar atoms can arise from the intricate interplay of these factors. Understanding the specific characteristics of the material and the conditions under which diffusion occurs is essential for predicting and controlling diffusion behavior. Experimental observations and computational simulations are often used in tandem to gain insights into these complex phenomena.
@@duc-hainguyen8499 Its a same material with two paths. For exampke path 1 is between atom O1 and atom O2 and path 2 from O2 to O3. These are oxygens are at the same distance but the barrier of path 1 is different than path 2. Its a rhombhohedral structure
@@anila5561 In a rhombohedral structure, the difference in diffusion barriers between two paths of similar length can be influenced by a combination of factors related to the crystal structure, electronic properties, and local environment. Here are some considerations:
Crystallographic Direction:
In rhombohedral structures, the crystallographic orientation of the paths can significantly impact diffusion. Different crystallographic directions may have distinct energy barriers for diffusion.
Symmetry:
The symmetry of the crystal structure can affect diffusion. If the two paths are in different symmetry-equivalent directions, the local environment may differ, leading to variations in diffusion barriers.
Surface Effects:
If the paths are near a surface or a defect, the local environment at each path might be different, influencing the energy barrier for diffusion.
Electronic Structure:
The electronic properties of the material, such as the band structure and electronic states near the diffusion paths, can affect the migration of atoms. Different electronic structures along the paths may result in different barriers.
Defects and Vacancies:
The presence of defects or vacancies along one path but not the other could lead to variations in diffusion barriers. For instance, a higher concentration of vacancies along one path might lower the barrier for diffusion.
Local Bonding Environment:
The local bonding environment around the diffusing atoms, including coordination numbers and bond strengths, can contribute to differences in diffusion barriers.
Pathway Interaction:
The interaction between the diffusing atom and its neighboring atoms along the pathway can vary between the two paths, leading to differences in energy barriers.
Quantum Effects:
Quantum mechanical effects, such as tunneling, might play a role. The electronic structure and the specific geometry of each path can affect the likelihood of quantum tunneling, impacting the barrier.
Thermal Vibrations:
Differences in the vibrational properties along the two paths, influenced by thermal vibrations, can affect the energy required for atoms to move.
Crystal Distortions:
Local distortions in the crystal lattice along one path but not the other could result in variations in diffusion barriers.
Understanding the specific details of the rhombohedral structure, the electronic structure, and the local environment along the two paths is crucial for explaining the observed differences in diffusion barriers. Computational methods, such as first-principles calculations or molecular dynamics simulations, can be valuable tools for investigating these phenomena in detail. Experimentally, techniques like diffraction, microscopy, and spectroscopy can provide insights into the structural and electronic properties of the material.
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Thank you, sir, for clearing many doubts, but how do I use this for protein-vaccine docking complexes!!!
Molecular dynamics (MD) simulation is a powerful tool for studying the dynamic behavior of molecules over time. However, it's important to note that MD simulations are generally used to simulate the motion and behavior of molecules, such as proteins, in a dynamic environment. Protein-vaccine docking, on the other hand, is typically performed using molecular docking methods, where the binding interactions between a protein (in this case, the target protein, e.g., a viral protein) and a vaccine or ligand are predicted.
Here's a general overview of how you might use MD simulation in the context of studying protein-vaccine complexes:
Molecular Docking:
Start with a molecular docking program (e.g., AutoDock, AutoDock Vina, HADDOCK) to predict the binding modes and affinity between your vaccine and the target protein.
Identify potential binding sites and analyze the docking results to select promising candidate complexes.
MD Simulation of the Complex:
Take the docked protein-vaccine complex structure and use it as the starting structure for your MD simulation.
Prepare the system for MD simulation by adding missing atoms, assigning charges, and solvating the system in a water box.
Use a molecular dynamics simulation package such as GROMACS or AMBER to perform the simulation.
Analysis of MD Trajectories:
Analyze the trajectory generated from the MD simulation to study the dynamic behavior of the protein-vaccine complex.
Calculate structural properties such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and analyze intermolecular interactions.
Binding Free Energy Calculation:
Optionally, you may perform advanced analysis, such as binding free energy calculations, to quantify the strength of the protein-vaccine binding.
Visualization:
Use visualization tools (e.g., VMD, PyMOL) to visualize the trajectory and analyze the structural changes in the protein-vaccine complex over time.
It's important to note that this is a complex process, and expertise in both molecular docking and molecular dynamics simulations is crucial. Additionally, the predictive accuracy of the results depends on the accuracy of the initial docked structure and the force field parameters used in the MD simulation. Always validate your results and consider experimental data if available.
@duc-hainguyen8499 Thank you very much, Sir. I have used HADDOCK for the above-mentioned complex. Now, it's clear to me how to proceed. Thanks once again.
@@monojitbanerjee276 I hope it will help
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I cannot create an account. The site is saying the email of my institution is my userID
You should try it again. Maybe the system is under-maintained.
I have done MD simulation with hydrogen adsorption, but after 2500 fs the energy is changed and is slightly lower than stright line. How can i check the change in bond length or any phase transition
If you've observed a change in the energy trajectory in your molecular dynamics (MD) simulation and suspect it might be related to a phase transition or a change in bond lengths, you can use various analysis tools to investigate these changes. Here are steps you can take:
1. Visual Inspection:
Trajectory Visualization: Use molecular visualization software (like VMD, PyMOL, or Chimera) to visually inspect your trajectory. Look for sudden changes in the molecular structure, especially around the time where the energy deviation occurs.
2. Trajectory Analysis:
Trajectory Snapshots: Extract snapshots of your system at different time points, particularly before and after the observed energy change. Compare these structures visually.
RMSD Analysis: Calculate the Root Mean Square Deviation (RMSD) of your system's structure over time. Sudden spikes in RMSD might indicate a structural change.
Bond Length Analysis: Use trajectory analysis tools to extract bond lengths over time. Observe if there are significant changes in bond lengths.
3. Phase Transition Detection:
Radial Distribution Functions (RDF): Calculate RDF for specific atom pairs (e.g., hydrogen-hydrogen, hydrogen-carbon). Changes in RDF can indicate phase transitions.
Density Analysis: Monitor the density of your system over time. A sudden change might indicate a phase transition.
Order Parameters: Define order parameters that characterize the phase of your system. Analyze these parameters over the simulation time.
4. Thermal Fluctuations:
Temperature Profile: Check the temperature profile of your system. Sudden changes in temperature might be related to a phase transition.
Energy Components: Analyze different components of your system's energy (kinetic, potential, etc.) to identify which part contributes most to the change.
5. Dynamic Properties:
Dynamics Cross-Correlation: Analyze the dynamic cross-correlation matrix to understand how different parts of your system move in relation to each other.
Vibrational Modes: Examine vibrational modes and frequencies to identify any significant changes.
6. Statistical Analysis:
Statistical Tests: If you have multiple trajectories or replicates, consider statistical tests to identify significant differences.
7. Check Simulation Parameters:
Check Simulation Inputs: Ensure that there are no errors or unusual settings in your MD simulation input files.
8. Extended Simulation:
Run Longer Simulations: If possible, extend your simulation beyond the problematic region to see if the system stabilizes.
By employing these analysis techniques, you should be able to gain insights into whether the observed energy deviation is associated with a structural change, bond length alteration, or a phase transition in your system.
@@duc-hainguyen8499 thanks for your detailed answer. How can calculate the bond length from trajectory analysis. I am using perovskite with hydrogen doping.
@@anila5561
Calculating bond lengths from trajectory analysis involves extracting the coordinates of the relevant atoms and then determining the distance between them. Here's a step-by-step guide using popular tools in computational chemistry:
Using VMD (Visual Molecular Dynamics):
Load Trajectory:
Open your trajectory file in VMD.
Load the associated structure file (PDB, XYZ, etc.).
Select Atoms:
Use the graphical interface to select the atoms whose bond length you want to analyze. In your case, these might be the hydrogen atoms and the atoms they are bonded to.
Measure Bonds:
In the VMD main window, go to Extensions -> Analysis -> Tk Console.
Type the following command to measure the bond length:
tcl
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measure bond (atomselection1) (atomselection2)
Replace (atomselection1) and (atomselection2) with the appropriate atom selections. For example:
tcl
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measure bond [atomselect top "name H1"] [atomselect top "name O"]
Analyze Results:
The bond length measurements will be displayed in the Tk Console. You can also visualize the bond lengths over time using the VMD graphical interface.
Using GROMACS:
If you're using GROMACS, you can use the gmx distance command to calculate distances between specific atoms. For example:
bash
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gmx distance -s topol.tpr -f traj.xtc -n index.ndx -select 'com of group "H" plus com of group "O"'
This command calculates the distance between the center of mass of hydrogen atoms ("H" group) and oxygen atoms ("O" group) over the trajectory.
Note: Ensure that you've created an index file (index.ndx) that defines the groups of atoms you're interested in.
Using Python and MDAnalysis:
If you prefer Python, you can use the MDAnalysis library to analyze trajectory files programmatically. Here's a basic example:
python
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import MDAnalysis as mda
# Load trajectory and topology
u = mda.Universe("topology.pdb", "trajectory.xtc")
# Select atoms
hydrogen = u.select_atoms("name H1")
oxygen = u.select_atoms("name O")
# Calculate bond lengths
bond_lengths = []
for ts in u.trajectory:
distance = mda.lib.distances.calc_bonds(hydrogen.positions, oxygen.positions, box=u.dimensions)
bond_lengths.append(distance)
# bond_lengths now contains the bond lengths at each time frame
This is a basic example, and you may need to adjust the atom selections based on your system's specifics.
Remember to adapt these instructions to the specifics of your perovskite system with hydrogen doping. The atom names and selections will depend on your system's topology and trajectory files.
Does this also work for membrane integrated proteins like GPCRs?
what is the accuracy of this tool?
Thanks for your questions. Currently, this tool can only simulate systems with less than or equal to 150,000 atoms (including water and other solvent molecules or ions). Please check your protein before analysis. This tool used Gromacs to perform MDS. Absolutely, the accuracy is 100%. You can read the information on the website. Thanks.
I read the paper, its definitely impressive.
I tried logging in and check it out, but it seems the servers are down.
@@bhavikaberwal3501 yes. It will be ok soon. Currently, you can perform PROTEIN IN WATER SIMULATION. It works well.
do i upload the already docked complex as input?
This is a great question. You should upload separate ligands and proteins one by one. Good luck.
@@duc-hainguyen8499 thank you for the reply. Can I know how do I perform the simulation for protein-protein complex? Which tool should I use?
@@NurArshad A great question. You can watch my video and upload your data to this website. They will run free for you. As known, simulating protein-protein complexes is a complex task and involves the use of specialized software and molecular dynamics simulations. Here's a general outline of the process and some commonly used tools:
Steps to Perform Protein-Protein Complex Simulation:
1. Prepare the Structures:
PDB Files: Obtain the 3D structures of the proteins you want to simulate in PDB (Protein Data Bank) format.
Clean Structures: Remove water molecules, heteroatoms, and other unwanted entities from the PDB files. Ensure that the protein structures are energetically minimized and optimized.
2. Choose Simulation Software:
GROMACS: GROMACS is a widely used molecular dynamics simulation package that allows for detailed simulations of protein-protein interactions. It has a steep learning curve but is highly powerful.
AMBER: The AMBER software suite includes tools for molecular dynamics simulations and can be used for protein-protein complex simulations.
NAMD: NAMD is another popular molecular dynamics simulation software that is specifically designed for high-performance simulations of large biomolecular systems.
3. Set Up the Simulation:
Force Field: Choose an appropriate force field that describes the interactions between atoms in your system. Commonly used force fields include AMBER, CHARMM, and OPLS.
Simulation Parameters: Define simulation parameters such as temperature, pressure, and integration time step.
Solvent Model: Choose a solvent model (usually explicit water molecules) to simulate the environment around the proteins.
4. Run the Simulation:
Use the chosen simulation software to perform the molecular dynamics simulation. This step can take a significant amount of computational time, especially for large and complex systems.
5. Analyze the Results:
After the simulation, analyze the trajectory data to understand the behavior of the proteins. Common analyses include RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), hydrogen bonding analysis, and energy calculations.
Tools for Protein-Protein Complex Simulations:
GROMACS: GROMACS is an open-source simulation package known for its efficiency and flexibility.
AMBER: The AMBER software suite includes a wide variety of tools for molecular dynamics simulations, including simulations of protein-protein complexes.
NAMD: NAMD is a high-performance molecular dynamics simulation software that can handle large systems like protein-protein complexes.
CHARMM: CHARMM is a versatile and widely used molecular simulation program that can simulate various biological systems, including protein-protein complexes.
AutoDock: AutoDock is a popular tool for protein-ligand docking, which can also be used for studying protein-protein interactions.
HADDOCK: HADDOCK is a versatile integrative computational platform to model the structure of protein-protein complexes.
When performing simulations, always ensure you have a good understanding of the underlying concepts and consult the documentation and tutorials provided by the respective software packages. Additionally, consider the computational resources required, as simulating large and complex protein-protein interactions can be computationally intensive.
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For simulation pr-pr interaction, can l use tutorial pr-ligand ?
You can do it in other software, but in this webtool, you can not upload your data.
Dear Sir, Thank you for this great lesson. Can I get your paper via email?
Sure, Do you have researchgate? I am happy to share it to you.
Hi thanks it's really helpful. But the server of simlab is down and not working plus i didn't understand the user key point,moreover i tried to register but it asks for academic email. so need your your suggestion
They will recover soon. yes you need academic email for registration
why it doesn't accept my university email? it is written it is not academic email!
It needs an academic email. You can check in the spam folder or reregister. I hope it helps
@@duc-hainguyen8499 yes I use my academic email. But I get error that thanks for your interest but we need academic email!
Did it finally accept? Because I have the same problem?
@@danyngah1157 All emails from the university can use this website. you should check spam or other boxes from your emails. good luck!!!
Can you please provide the approx time the server takes to finish 100ns MD run? Of like 40000 atoms
Thanks for your questions. It takes around several days or 1-2 weeks. It depends on how many jobs are submitted.
@@duc-hainguyen8499 Thanks for the prompt reply. I am pursuing PhD and my domain of work aligns Perfectly with yours. Keep up the good work. Much appreciated.
@@binarysecrets9055 I hope that we can share knowledge together. Thanks.
Hello, I am Master student in Medicinal Chemistry. I don't work in an academic institute. I need this service to do my complementary research. Can I write my personal email,please.
This web tool is not working now. Do you have any idea why it is not working?
you can try another webtool. PRODRG server for generating ligand topology files is currently down. Please be patient while this issue is resolved.
We apologize for the inconvenience.
Meanwhile, the users may try alternative molecular simulation tools:
SiBioLead webserver (sibiolead.com/)
Gridmarkets (www.pharma.gridmarkets.com/gromacs)
Is simlab working in online mode
They will recover soon
The prodrg2 server is not working is there any other ways ?
hdock.phys.hust.edu.cn/ you can try it one.
I want to read your paper plz
here you are: Article Related:
DOI: 10.1080/07391102.2023.2259482
DOI: 10.1080/07391102.2023.2238068
www.sciencedirect.com/science/article/pii/S1567576923000498?via%3Dihub
@@duc-hainguyen8499 thank you
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here you are: DOI: 10.1080/07391102.2023.2259482 ; DOI: 10.1080/07391102.2023.2238068 ; DOI: 10.1016/j.compbiolchem.2023.107872
@@duc-hainguyen8499 thanks bro
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