BMS-manual//////////////////////////.pdf

Brief simulations are short, focused reenactments of real-world situations designed for experiential learning, training, or decision-making in a safe, controlled environment. Also known as simulation scenarios, these are artificial but realistic representations of events or systems used to develop skills like communication, leadership, and procedural

1. Biomolecular Simulation Laboratory

VI Semester, Department of Biotechnology, SIT, Tumakuru Page 1

EXPERIMENT 1
PROTEIN PREPARATION AND PROTEIN MODELLING USING MODELLER

Aim: To prepare the protein using modeller and other tools.

Theory:
Protein data bank is a resource contains archive-information about the 3D shapes of proteins,
nucleic acids, and complex assemblies. PDB is the most reliable source to download the structures
to perform molecular dynamics simulation. However, one needs to understand the PDB file format
throughly to comment on the crystallisation process of the selected biomolecule. The following list
of points are extremely important while considering the crystal structure from PDB.
1. Always look for missing residues in the structure file. They should be added back to the
structure before proceeding further with simulation.
2. Understand the use of crystal water present in the structure file. If the crystal water do not play
any role in the simulation, then remove them to avoid modelling complications.
3. Sometimes, to get good quality crystals, the crystallographers mutate the structure in the non
functional site of the protein. This information can be fetched by reading the corresponding
literature. If the structure is mutated, then they should be back mutated before proceeding with
the dynamics.
To add missing residues and to back mutate, we have to perform protein modelling. Here, we use
the application of MODELLER software to model the protein.

Useful links:
1) https://www.uniprot.org/
2) https://www.ncbi.nlm.nih.gov/
3) https://www.rcsb.org/
4) https://salilab.org/modeller/

Procedure
Part 1:
# Target and Template identification: The protein of interest is SARS-CoV-2 (COVID-19) main
protease
1) Search for protein 3C-like proteinase enzyme (Chain C) in NCBI databases by choosing
appropriate search options.
2) What is the difference between Proteinase and Protease?
3) What is the accession number of the shortlisted search result.
4) Retrieve the FASTA sequence of the enzyme.
5) How many amino acids are present in the shortlisted protein?
6) Perform NCBI Blast of the protein sequence against PDB database.
7) What is the percentage of Identity and Query coverage in the first hit of the search result.
8) What is the accession number of the PDB ID?
9) Using the accession number open the structure in PDB.
10) Download FASTA sequence file and PDB structure file to your local desktop.

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11) How many chains are present in the Resultant structure file?
12) Which chain is required to proceed further for model building?
13) Identify the missing residues in the structure file.
14) Is the crystal water is having any catalytic role in the enzyme? Comment on the same.
15) Is Model building is necessary?
16) List out the missing residues in the selected chain of the protein.
17) Prepare protein structure file by deleting unwanted sections in the PDB file.
18) Points to remember while preparing the structure file
a) Delete water molecule, if they are not playing any role in catalytic activity of the
proteinase
b) The missingresidues in the protein should be added back in the modelled structure.
c) The mutated residue in the non catalytic site should be back mutated (if any).
19) The modified template is now ready for modelling.

Part 2:
# Download and install modeller in your system

Part 3:
Working With modeller
1. Preparation of structure file.
The structure file should be in the form of .pdb file format. The PDB file of the Template will serve
as structure file in Modeller.
2. Preparation of alignment file.
Alignment file should be in .ali extension file format. The aligned sequence in the alignment file
should in PIR format.
3. Preparation of script file.
Python based script is required run the modeller with .py file format. Modeller is not having any
GUI interface, it will run based on command prompt. (Sample script file is available in modeller’s
example folder)
4. Run modeller using the command.

Part 4:
Selection of the target structure:
1. One of the modelled structure is used as target for further analysis.
2. Run the following command to convert the ligand file from .pdb to .gro
/usr/local/gromacs/bin/gmx pdb2gmx -f protein.pdb -o protein.gro -water spce

3. Note down the list of newly created files.
4. Understand each and every file generated and comment on the same.

Result:

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EXPERIMENT 2
LIGAND PREPARATION AND GENERATION OF LIGAND FORCE FIELD

Aim: To prepare the ligand for molecular dynamics simulation and generation of the force field

Theory:
Ligands are the most important aspect in understand the kinetics of an enzyme. The ligands can
classified generally as substrates or inhibitors or enhancers of the enzymes. However, it's
important to understand the role of ligands in enzyme action. In molecular dynamics simulations,
the structure of the ligands are obtained form any ligand database. PubChem is one of the
important repository of the chemical structures.
In molecular dynamics simulation, the force filed (library of parameters) for proteins is very
standard and it was developed by many scientific communities around the world. It is very straight
forward to design the force field of proteins, as every protein in nature is made of 20 amino acids.
If one develops the parameters of 20 amino acids, which can easily adopted for all proteins. But, in
case of ligand this is not the case. Ligands are structurally very diverse and we don’t have the force
field (Library of parameters) ligands. Thus, one has to develop the force filed of ligand of interest
before use. Automated topology builder (ATB server) is one of the important tool in designing the
force field of ligands. Thus, SMILES notation form the PubChem can be used in ATB server to
develop the force filed of ligands.

Useful links:
1. https://pubmed.ncbi.nlm.nih.gov/
2. https://pubchem.ncbi.nlm.nih.gov/
3. https://atb.uq.edu.au/

Procedure
Part 1:
# Identification of ligand: The protein of interest is SARS-CoV-2 (COVID-19) main protease
1. Identification of the suitable ligand against SARS-CoV-2 (COVID-19) main protease using
literature survey.
2. The main point while searching the ligands is to screen the possible leads, which are found to be
effective against the family of proteases.
3. Identification of the ligand using PubChem: Go to PubChem server, enter the ligand name in the
search box.
4. Copy the canonical smiles.
5. Sign in to ATB server (use your academic email ID to register)
6. In the submit page, select “heteromolecule”as Molecule Type
7. Enter the net charge (Use PubChem website to identify the charge on the ligand molecule)
8. Paste the canonical smiles obtained from the PubChem in the “Provide smiles” section.
9. Click on Translate 2D structure/smiles to 3D structure.
10. Click on Transfer to submit page and then click Next.

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11. The ATB database will also display any closest molecule present for the ligand in the result
sheet. If ATB displays so, then check for RMSD deviation and molecular composition. If the
displayed structure of ATB database is same as of ligand, then directly select the conformation.
12. If the ATB database doesn’t display any closest conformation of the ligand, then click on
submit the structure. The ATB database then generates topology of the new ligand. The result
will be sent to the registered e-mail address.
13. Select a confirmation and then click on the Show Molecule page.
14. Then click on Molecular Dynamics (MD) files.
15. Select the GROMACS format.
16. Download the following files: GROMOS96 topology file [Ex: GROMACS G54A7FF United-
Atom (ITP file)], structure file [Ex:United-Atom PDB (optimised geometry)] and force field
Gromacs 4.5x-5.x.x 54a7
17. Suitably edit the residue names in both .itp and .pdb files. (Do not proceed on your own:
Ask your instructor about this step)
18. Add hydrogen molecules to the docked ligand file. (If needed)
19. Retain the topology of ATB ligand file and confirmation of the docked file to which
hydrogens added.
20. Suitably edit the atom numbers of the ligand.pdb (Do not proceed on your own: Ask your
instructor about this step)
21. Arrange the ligand coordinates according to the ATB.itp file.
22. Run the following command to convert the ligand file from .pdb to .gro
/usr/local/gromacs/bin/gmx editconf -f 2OB.pdb -o 2OB.gro
23. Note down the list of newly created files.
24. Understand each and every file generated and comment on the same.

Part-2
Questions

1) What is ATB?
2) Which confirmation did you choose in ATB and why?
3) What does the .itp file contain?
4) What is force field?
5) What is -f and -o indicated in the command?
6) What do you mean by editconf ?
7) What is the difference between .pdb and .gro file?

Results

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EXPERIMENT 3
PREPARING OTHER BIOMOLECULES FOR MOLECULAR DYNAMICS SIMULATION FOR GROMACS
SIMULATION PACKAGE.

Aim: To prepare the input file of DNA molecule to run in GROMACS simulation package.

Theory:
The important biological macromolecules in nature are Proteins, Lipids, Carbohydrates and
Nucleotides (DNA or RNA). The computer simulations are not restricted to Protein’s alone, the
simulations can be performed on other biological molecules also. Thus, in this section, we perform
simulations on DNA and Lipid molecule. Thus, it is also possible to perform computer simulations
of hybrid molecules like Proteins in conjugate with lipids or nucleic acids.

Useful links:
1. https://www.rcsb.org/

Procedure
1. Download the PDB file 3u2n (DNA structure) from PDB server, open it in VMDand have a look at
your DNA structure.
2. How many bases are there in the DNA structure?
3. Identify the role of crystal water in the structure.
4. If the role of crystal water is not required, delete the water molecules from the PDB file.
5. How to delete the water molecules from the PDB files?
6. Use the knowledge of experiment number 1 to remove the water molecules.
7. After preparing the DNA structure file, convert the .pdb file into .gro file using the PDB2GMX
command of GROMACS.
/usr/local/gromacs/bin/gmx pdb2gmx -f 3u2n.pdb -o dna.gro -water spce -inter
8. Why “-inter” is given? Give the reason behind giving “-inter” in the command. (Use GROMACS
command list to get the details of using “-inter”)
9. Which force field is preferred and Why during force field selection?
10. Comment on the output files generated.

Results

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EXPERIMENT 4
PREPARATION OF THE PROTEIN LIGAND COMPLEX AND VACUUM MINIMIZATION, PERIODIC
BOUNDARY CREATION, SYSTEM SOLVATION, ADDING IONS AND ENERGY MINIMIZATION

Aim: To prepare protein ligand complex followed by vacuum minimization, periodic boundary
condition, system solvation, adding ions and energy minimization

Theory:
Once the protein and ligands are ready, it is important to prepare the protein-ligand complex. The
force filed of protein alone or the force filed of ligand alone cannot be used for the simulation of
the complex. Thus, we need to have the force field of protein-ligand complex. This, force field can
be obtained form ATB server. Once the topology of Protein ligand complex is ready, we have to
perform vacuum minimization.
In the field of computational chemistry, energy minimization (also called energy
optimisation, geometry minimization, or geometry optimisation) is the process of finding an
arrangement in space of a collection of atoms where, according to some computational model of
chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the
position on the potential energy surface (PES) is a stationary point (described later). In general,
finding global energy minimised state of a protein ligand complex.
Here, we perform energy minimisation of the complex under vacuum condition followed by
minimisation under solvent condition. To have solvent condition we add water molecule in the
defined periodic boundary and neutralise the system with Na+ and Cl- ions. After minimization the
system will be ready for equilibration.

Procedure:

Part 1:
# Complex preparation
1. Create a new file in the text editor as complex.gro.
2. Paste Protein.gro (complete) and ligand.gro (exclude first two lines and last line of ligand.gro) in
complex.gro file.
3. Update the total number of atoms in the complex.gro and retain the cartesian coordinates in
the last line.
4. SAVE.
5. How many atoms are present in the complex.gro file?

# Preparation of topol.top file.
1. topol.top file is referred as topology file. The complete topology of the protein is present in the
tool.top file. However, the ligand topology is missing in the topology file.
2. The second step is to add the ligand topology in the tool.top file.
3. Open the ligand.itp file as prepared in the second experiment in the note pad.
4. Slowly scan through entire ligand.itp file (topology file of ligand) and compare with the
proteins’s topology file present in the tool.top.

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5. Identify the different sections present in the topology file of both ligand and protein.
6. Next, to import ligand topology into protein topology file (topol.top) to prepare topology of
complex.gro file.
7. For this, add following lines in topol.top file
; Include ligand topology
#include “ligand.itp”
8. Add the ligand name and its molecule number in the appropriate section (molecule type) of the
topology (topol.top) file.
9. SAVE and EXIT.

#Preparation of new_box.gro file.
1. Open complex.gro in VMD
2. In VMD, Go to Extension→ TK console → type “pbc box”
3. Will the periodic box covers the entire protein within it?
4. If no, How to rectify the same.
5. We use the following command to keep protein at the centre of the periodic box.
➢ /usr/local/gromacs/bin/gmx editconf -f complex.gro -o newbox.gro -bt cubic -d 2.0 -c
6. Again, open the newbox.gro file in VMD, in VMD Go to Extension→ TK console → type “pbc
box”
7. Will box cover the entire protein? Comment.
8. Identify the different box types (other than Cubic) which can be adopted in MD simulations.

Part-2
#Vacuum Minimization
1. Files required for vacuum minimization is vacuum.mdp file, topology files, .gro file.
2. .mdp file is referred as molecular dynamics parameter file which hold the parameters of
dynamics run.
3. Comment on the various parameters present in the .mdp file.
➢ /usr/local/gromacs/bin/gmx grompp -f vacuum.mdp -c newbox.gro -p topol.top -o protein-
EM-vacuum.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -v -deffnm protein-EM-vacuum -c protein-EM-
vacuum.pdb
➢ /usr/local/gromacs/bin/gmx mdrun -v -deffnm protein-EM-vacuum
4. Use the following command prompt to run the vacuum minimization.
5. Understand the role of various keywords used in the command prompt.
6. What is energy minimization and why it is essential in MD simulations.

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Part-3
#System solvation, adding ions and energy minimization with solvent.
1. The required files system solvation are .mdp file, .gro file, topol.top file
2. To solvate the system use the following command.
➢ /usr/local/gromacs/bin/gmx solvate -cp protein-EM-vacuum.gro -cs spc216.gro -p topol.top
-o solv.gro
3. Open solv.gro file in VMD.
4. In VMD, Go to Extension→ TK console → type “pbc box”
5. Can you see the solvation in the periodic boundary box?
6. Can you see the protein/DNA of interest along with water?
7. How many water molecules are added in this step? (To get the answer refer topol.top file)
8. After, adding water, it's time to bring the structure for required pH.
9. For this, we add ions to the system using following command
➢ /usr/local/gromacs/bin/gmx grompp -f em.mdp -c solv.gro -p topol.top -o ions.tpr
➢ /usr/local/gromacs/bin/gmx genion -s ions.tpr -o solv_ions.gro -p topol.top -conc 0.15 -
neutral -pname NA -nname CL
10. How many ions are added in this step? (To get the answer refer topol.top file)
11. Open solv_ions.gro file in VMD.
12. In VMD, Go to Extension→ TK console → type “pbc box”
13. Visualize ions in VMD.
14. Finally, perform system minimization under solvent condition.
15. Use the following command to run energy minimisation.
➢ /usr/local/gromacs/bin/gmx grompp -f em.mdp -c solv_ions.gro -p topol.top -o em.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -v -deffnm em
16. How many steps it took to minimise?
17. Analyse the parameters of .mdp file? Comment on the parameters.
(Note: All .mdp files can be obtained by GROMACS tutorials: Protein-ligand simulation)
QUESTIONS
1) Expand VMD and PBC.
2) What does the complex.gro file contain?
3) What was the difference observed when complex.gro, newbox.gro, solv.gro and
solv_ions.gro were visualised in VMD?
4) Which file will be created using the keyword grompp?
5) What is -bt, -d, -c, -p, -cp and -cs indicated in the command?
6) Did you observe any changes in the topology file after the solvation and neutralisation?

Results

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EXPERIMENT 5
SYSTEM EQUILIBRATION USING NVT AND NPT ENSEMBLE SYSTEM AND PERFORMING MD RUN.

Aim: To perform system equilibration and running simulation

Theory:
In MD simulations, atoms of the macromolecules and of the surrounding solvent undergo a
relaxation that usually lasts for tens or hundreds of picoseconds before the system reaches a
stationary state. The initial nonstationary segment of the simulated trajectory are typically
discarded in the calculation of equilibrium properties. This stage of the MD simulation is called
equilibration stage.
Equilibration protocols are still largely a matter of personal preference. Some protocols call for
very elaborate procedures involving gradually increasing temperature in a step-wise fashion while
other more aggressive approach simply use a linear temperature gradient and heat the system up
to the desired temperature.
In our example, we'll follow the protocol of equilibration in two stages. In the first stage, we will
start the system from a low temperature of 100 K and gradually heat up to 300 K over 10
picosecond of simulation time. We will perform this stage of equilibration with the volume held
constant. This type of equilibrium is referred as NVT equilibrium. In the second stage we gradually
maintain the required atmospheric pressure and keep pressure as constant throughout the
equilibration phase. This type of equilibration is referred as NPT equalisations. Also, we use the
position restrain on the atoms initially, which are gradually reduced to zero over multiple NPT
equilibration simulations.
Useful links:
1. https://www.rcsb.org/

Procedure
Part-1
#System equilibration
1. Understand the concept of ensemble, NPT ensemble and NVT ensemble systems.
2. Download nvt.mdp file and npt.mdp file from suitable GROMACS tutorial.
3. Glance through both .mdp files.
4. Comment on the parameters of .mdp file and understand its use while running GROMACS.
5. Position restraining is an important aspect in MD simulations.
6. To position restrain the atoms, 1000 KJ/mol of external energy is used.
7. In MD simulation, we initially keep position restrain for all atoms followed by step by step
release of restrain on atoms. Finally, all atoms will be set free to run without any restrain
8. Generate the position restrain file of ligands using the following command.
➢ /usr/local/gromacs/bin/gmx genrestr -f ligand.gro -o posre_ligand.itp -fc 1000 1000 1000

9. Include position restrain in the topology file topol.top

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;Lipid position restraints
#ifdef POSRES
#include “posre_ligand.itp”
#endif
10. Merging protein and ligand using the following command
➢ /usr/local/gromacs/bin/gmx make_ndx -f em.gro -o index.ndx
➢ Select 1|13
➢ Press q and press enter
11. System equilibration in NVT ensemble (position restrain 1000 KJ/mol is maintained in NVT
ensemble)
12. Use the following command to run NVT ensemble.
➢ /usr/local/gromacs/bin/gmx grompp -f nvt.mdp -c em.gro -p topol.top -n index.ndx -o
nvt.tpr -r em.gro
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm nvt -v
13. The output of NVT ensemble equilibration will be used as input for NPT ensemble
14. To run NPT ensemble use following command
15. As mentioned earlier, the restrain should be released slowly.
16. For this, we perform NPT ensemble simulation for multiple times by reducing the position
restrain force gradually.
➢ /usr/local/gromacs/bin/gmx grompp -f npt.mdp -c nvt.gro -t nvt.cpt -p topol.top -n
index.ndx -o npt-1000.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm npt-1000 -v
17. Release restrain slowly decreasing the numbers from 1000 to 100, from 100 to 10, from 10
to 1 and then from 1 to 0 position restrain its file.
❏ vi posre.itp
:%s/1000 1000 1000/100 100 100/g
:wq
❏ vi posre_ligand.itp
:%s/1000 1000 1000/100 100 100/g
:wq
Next run the following command for next NPT run with reduced force.
➢ /usr/local/gromacs/bin/gmx grompp -f npt.mdp -c npt-1000.gro -t npt-1000.cpt -p topol.top

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-n index.ndx -o npt-100.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm npt-100 -v
❏ vi posre.itp
:%s/100 100 100/10 10 10/g
:wq
❏ vi posre_ligand.itp
:%s/100 100 100/10 10 10/g
:wq
Next run the following command for next NPT run with reduced force.
➢ /usr/local/gromacs/bin/gmx grompp -f npt.mdp -c npt-100.gro -t npt-100.cpt -p topol.top -n
index.ndx -o npt-10.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm npt-10 -v
❏ vi posre.itp
:%s/10 10 10/1 1 1/g
:wq
❏ vi posre_ligand.itp
:%s/10 10 10/1 1 1/g
:wq
Next run the following command for next NPT run with reduced force.
➢ /usr/local/gromacs/bin/gmx grompp -f npt.mdp -c npt-10.gro -t npt-10.cpt -p topol.top -n
index.ndx -o npt-1.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm npt-1 -v
❏ vi posre.itp
:%s/1 1 1/0 0 0/g
:wq
❏ vi posre_ligand.itp
:%s/1 1 1/0 0 0/g
:wq

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Next run the following command for next NPT run with reduced force.
➢ /usr/local/gromacs/bin/gmx grompp -f npt.mdp -c npt-1.gro -t npt-1.cpt -p topol.top -n
index.ndx -o npt.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm npt -v
18. Finally, after running all the steps, the system is ready for MD run.

Part-2
#Production run
1. The production run should be performed in NPT ensemble for the whatever nano second is
required.
2. For this, download md.mdp file form GROMACS tutorials.
3. However, the parameters of md.mdp and npt.mdp file will be almost similar.
4. Finally use the following command for production run.
5. Use servers for the production run.
➢ /usr/local/gromacs/bin/gmx grompp -f md.mdp -c npt.gro -r em.gro -t npt.cpt -p topol.top -
n index.ndx -o md_out.tpr
➢ /usr/local/gromacs/bin/gmx mdrun -deffnm md_out -v

1. Preparation of .mdp file for NVT equilibration
2. Running NVT simulation
3. Preparation of .mdp file for NPT equilibration
4. Running NPT simulation
5. Preparation of .mdp file for production run in NPT ensemble
6. Production run.

QUESTIONS

1) What is the full form of NPT and NVT?
2) What is an ensemble system?
3) Give an example for the macroscopic and microscopic entities.
4) Are there any variations present in the microscopic entities when the protein is in its static
state?
5) By keeping NPT and NVT constant, what are the other macroscopic parameters you can
calculate?
6) Mention the difference between .xtc and .trr files?

Result:

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EXPERIMENT 6
RMSD, RMSF, RG, SASA AND SECONDARY STRUCTURE ANALYSIS ON MD TRAJECTORY

Aim: To perform trajectory analysis using various analysis utilities of GROMACS

Theory:
RMSD: RMSD is used to measure the time evolution stability of a protein. In bioinformatics, the
root-mean-square deviation of atomic positions, or simply root-mean-square deviation (RMSD), is
the measure of the average distance between the atoms (usually the backbone atoms) of
superimposed proteins.
The formula of RMSD is as follows



where δi is the distance between atom i and either a reference structure. This is often calculated
for the backbone heavy atoms C, N, O, and Cα.

RMSF: RMSF measures the residue flexibility by calculating the extent of movement of each atom
around its average position. Usually the C alpha atoms are considered for the measure of RMSF.
The formula of RMSD is as follows



where deviation is the is the distance between atom i and either a
reference atom or the mean position of the N equivalent atoms. This is often calculated for
the Cα atoms.

Radius of gyration (Rg): Measures the protein compactness, often suitable for globular proteins.
Radius of gyrationof a body about the axis of rotation is defined as the radial distance to a point
which would have a moment of inertia the same as the body's actual distribution of mass, if the
total mass of the body were concentrated there.





K is radius of gyration (Rg), I is mass moment of inertia and M is the total mass.

SASA: The accessible surface area (ASA) or solvent-accessible surface area (SASA) is the surface
area of a biomolecule that is accessible to a solvent. Measurement of ASA is usually described in
units of square Ångstroms (a standard unit of measurement in molecular biology).

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Secondary structure analysis: This analysis determines the presence of secondary structural units
in the protein over simulation time. This helps to identify the stable as well as flexible regions of
the protein.

Procedure
Part1
# RMSD analysis (ROOT MEAN SQUARE DEVIATION)
1. RMSD is a measure of stability of a given protein over simulation time.
2. The RMSD uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The first step is to convert .xtc file into .pdb file (both are trajectory files) using the following
command
➢ /usr/local/gromacs/bin/gmx trjconv -s protein.tpr -f protein.xtc -o trj-protein.pdb -n
index.ndx -pbc cluster
4. The RMSD can be calculated over the simulation trajectory using following command.
➢ /usr/local/gromacs/bin/gmxrms -s protein.tpr -f trj-protein.pdb -n index.ndx -o RMSD.xvg
➢ Always select backbone atoms for plotting RMSD graph.

5. The RMSD graph can be visualised using GRACE software ( In command prompt type ‘xmgrace
RMSD.xvg’)
6. Why we have to select backbone atoms of proteins to plot RMSD graph?

# RMSF analysis (ROOT MEAN SQUARE FLUCTUATION)
1. RMSF is a measure of protein flexibility over simulation time.
2. The RMSF uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The generated simulation trajectory should be used for all analysis.
4. The RMSF can be calculated over the simulation trajectory using following command.
➢ /usr/local/gromacs/bin/gmxrmsf -s protien.tpr -f trj-protein.pdb -n index.ndx -o RMSF.xvg -
res
➢ Always select C alpha atoms for plotting RMSF graph.

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5. The RMSF graph can be visualised using GRACE software ( In command prompt type ‘xmgrace
RMSF.xvg)
6. Why we have to select C alpha atoms of proteins to plot RMSF graph?

# Rg Analysis (RADIUS OF GYRATION)

1. Rg is a measure of protein compactness over simulation time.
2. The Rg uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The Rg can be calculated over the simulation trajectory using following command.
➢ /usr/local/gromacs/bin/gmxgyrate -f protien.xtc -s protein.tpr -n index.ndx -o gyrate.xvg
➢ Always select complete protein for plotting Rg graph.
4. The Rg graph can be visualised using GRACE software ( In command prompt type ‘xmgrace
gyrate.xvg)
5. Why we have to select complete protein to plot Rg graph?

# SASA analysis (SOLVENT ACCESSIBLE SURFACE AREA)

1. SASA is a measure of bulkiness protein over simulation time.
2. The SASA uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The SASA can be calculated over the simulation trajectory using following command.
➢ /usr/local/gromacs/bin/gmx sasa -f protein.xtc -s protein.tpr -o solvent-accessible-
surface.xvg -oa atomic-sas.xvg -or residue-sas.xvg -dt 10
➢ Always select complete protein for plotting Rg graph.
4. The SASA graph can be visualised using GRACE software ( In command prompt type ‘xmgrace
solvent-accessible-surface.xvg)
5. Why we have to select complete protein to plot SASA graph?


# SECONDARY STRUCTURE ANALYSIS

1. Secondary structure analysis is a measure of presence of secondary structural units over
simulation time.
2. The secondary structure analysis uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The Secondary structure analysis can be calculated over the simulation trajectory using
following command.
➢ /usr/local/gromacs/bin/gmx do_dssp -f protein.xtc -s protein.tpr -o ss.xpm -sc ss-rmsd.xvg -
dt 10
➢ Always select complete protein for plotting Secondary structure analysis graph.
4. The Secondary structure analysis graph can be visualised using .eps software
5. To get the .eps image, we need to convert .xpm format to .eps format using the following
command.

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➢ /usr/local/gromacs/bin/gmx xpm2ps -f ss.xpm -o ss.eps
6. Why we have to select complete protein to plot Secondary structure analysis graph?

QUESTIONS

1) Do RMSD and RMSF plots are interrelated (Like: if RMSD increases, RMSF also increases)?
2) Do Rg and SASA plots are interrelated?

Result:

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EXPERIMENT 7
HYDROGEN BOND AND PROTEIN POCKET ANALYSIS ON MD TRAJECTORY

Aim: To perform trajectory analysis using various analysis utilities of GROMACS

Theory:
Hydrogen bond analysis: A hydrogen bond (often informally abbreviated H-bond) is a
primarily electrostatic force of attraction between a hydrogen (H) atom which is covalently
bound to a more electronegative atom or group, particularly the second-row
elements nitrogen (N), oxygen (O), or fluorine (F)—the hydrogen bond donor (Dn)—and another
electronegative atom bearing a lone pair of electrons—the hydrogen bond acceptor (Ac). Such an
interacting system is generally denoted Dn–H···Ac, where the solid line denotes a polar covalent
bond, and the dotted or dashed line indicates the hydrogen bond
This analysis helps us to compute the interaction ability of protein with ligand.

CAVER: CAVER is a software tool for analysis and visualisation of tunnels and channels in protein
structures. Tunnels are void pathways leading from a cavity buried in a protein core to the
surrounding solvent. Unlike tunnels, channels lead through the protein structure and their both
endings are opened to the surrounding solvent. Studying of these pathways is highly important for
drug design and molecular enzymology.CAVER provides rapid, accurate and fully automated
calculation of tunnels and channels in static and dynamic structures. The molecules amendable to
analysis of CAVER include proteins, nucleic acids, or inorganic materials.

Useful links:
1. CAVER User manual: http://www.caver.cz/fil/download/manual/caver_userguide.pdf

Procedure
Part1
# HYDROGEN BOND analysis
1. Hydrogen bond is a measure of protein ligand interaction over simulation trajectory.
2. The Hydrogen bond analysis uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
3. The Hydrogen bonds can be calculated over the simulation trajectory using following command.
➢ /usr/local/gromacs/bin/gmx hbond -f protein.xtc -s protein.tpr -num hbnum_Pro-Pro.xvg -hx
hbhelix.xvg
➢ Always select both proteins and ligands for plotting Hydrogen bond graph.

4. The Hydrogen bond analysis graph can be visualised using GRACE software ( In command
prompt type ‘hbnum_Pro-Pro.xvg’)
5. Why we have to select proteins and ligands for plotting Hydrogen bond graph?

# PROTEIN POCKET analysis
1. Protein pocket analysis is a measure of proteins cavities or protein pockets.
2. It is usually assumed to be binding pockets of the ligand.

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3. CAVER is a software tool for analysis and visualisation of tunnels and channels, cavities, pockets
in protein structures.
4. The Protein pocket analysis uses the .pdb file as trajectory file.
5. CAVER has to downloaded and installed form the CAVER website before working.
6. Refer CAVER user manual for running CAVER
7. CAVER has both VMD and PyMol plugins to work with.
8. Did you identified any pockets in the protein?
9. Name the residues present in the protein pocket.

QUESTIONS

1) Identify few other tools which can find the protein pockets over simulation trajectory.

Result:

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EXPERIMENT 8
THE MOLECULAR MECHANICS POISSON-BOLTZMANN SURFACE AREA (MMPBSA) ANALYSIS ON
SIMULATION TRAJECTORY

Aim: To perform trajectory analysis using various analysis utilities of GROMACS

Theory:
MMPBSA: The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has
been widely applied as an efficient and reliable free energy simulation method to model molecular
recognition, such as for protein-ligand binding interactions.
Binding free energies (ΔGbinding) of any ligandcan be calculated by Molecular Mechanics Poisson
Boltzmann Surface Area (MMPBSA) method (Kumari, R., et al., 2014). The binding energy was
obtained by taking the difference between the free energies of the protein-ligand complex
(Gcomplex), the unbound protein (Gprotein), and the unbound ligand (Gligand):
ΔG binding = Gcomplex – (Gprotein + Gligand) (Eq.1)
The ΔGbinding values were computed using the g_mmpbsa script available in GROMACS
programme. The average free energy of each entity in the right-hand side of Eq. 1 can be
calculated using
????????????= ??????
???????????? −��+ ??????
???????????????????????? (Eq.2)
EMM = Ebonded + Enonbonded (Eq.3)
Gsolv = Gnon-polar + Gpolar (Eq.4)
where, x represents the protein, ligand or protein-ligand complex. Here, EMM stands for the
molecular mechanics potential energy in vacuum, TS for the entropic contribution in vacuum, and
Gsolv for free energy of solvation. The EMMincludes the energy of both bonded as well as
nonbonded interactions, where Ebonded consists of bond, angle, dihedral interactions and Enonbonded
includes electrostatic and van der Waals interaction. Gsolv is defined as the energy needed to
transfer the ligand from vacuum to solvent and consists of polar and nonpolar components. The
Gpolar contribution was estimated by solving the linearized Poisson Boltzmann equation, and
solvent accessible surface area (SASA) model was used to estimate Gnon-polar that assumes a linear
relationship between Gnon-polar and SASA.

Useful links:
1. https://rashmikumari.github.io/g_mmpbsa/Download-and-Installation.html
2. https://rashmikumari.github.io/g_mmpbsa/single_protein_ligand_binding_energy.html

Procedure
Part1: # MMPBSA analysis
1. MMPBSA analysis is a measure of finding protein-ligand interaction energy over the simulation
trajectory.
2. The binding energy depicts the protein ligand bonding strength.
3. Use the above mentioned link (1st one) to install MMPBSA in your local system.
4. To run Only molecular mechanics (vdw and electrostatic) vacuum energy with energy
decomposition use the following command.

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➢ /usr/local/gromacs/bin/gmxg_mmpbsa -f protein.xtc -s topol.tpr -n index.ndx -mme -mm
energy_MM.xvg -decomp -mmcon contrib_MM.dat
5. To run Only polar solvation energy with energy decomposition use the following command.
➢ /usr/local/gromacs/bin/gmxg_mmpbsa -f protein.xtc -s topol.tpr -i mmpbsa.mdp -n
index.ndx -nomme -pbsa -decomp -pol polar.xvg -pcon contrib_pol.dat

6. To run Only non-polar solvation energy with energy decomposition use the following command.
➢ /usr/local/gromacs/bin/gmxg_mmpbsa -f protein.xtc -s topol.tpr -i mmpbsa.mdp -n
index.ndx -nomme -pbsa -decomp -apol apolar.xvg -apcon contrib_apol.dat
7. Individual .mdp files to run the above mentioned commands can be obtained for the above
mentioned link (Link 1).
8. All three commands as mentioned above can be run as a single command as shown below. But,
one should be extremely careful while choosing suitable .mdp file
➢ /usr/local/gromacs/bin/gmxg_mmpbsa -f protein.xtc -s protein.tpr -n index.ndx -i pbsa.mdp
-pdie 2 -pbsa -decomp`

9. Download pbsa.mdp form the above mentioned link (2nd one) which has the parameters to run
MMPBSA in single step.
10. Download and paste MmPbSaStat.py and MmPbSaDecomp.pypython scripts in your local
directory form the given second link.
11. Use the following commands to obtain the results.
➢ python2.7 MmPbSaStat.py -m energy_MM.xvg -p polar.xvg -a apolar.xvg

➢ python2.7 MmPbSaDecomp.py -bs -nbs 2000 -m contrib_MM.dat -p contrib_pol.dat -a
contrib_apol.dat > res.dat
12. Comment on the graph
13. Identify the residues which is very important in binding with ligands.

QUESTIONS

1) Identify few other tools which can protein ligand binding energy over simulation trajectory.
2) Understand the parameters of pbsa.mdp file

Result:

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EXPERIMENT 9
THE PRINCIPAL COMPONENT ANALYSIS (PCA) ON SIMULATION TRAJECTORY

Aim: To perform trajectory analysis using various analysis utilities of GROMACS

Theory:
PCA: The dominant motions of a dynamic protein are generally traced by principal component
analysis (PCA). In PCA, the protein-correlated motions are reduced to a space of independent
motions. First, a covariance matrix (Cij) was generated by measuring the degrees of collinearity of
atomic motions of each pair of C atoms. The generated covariance matrix was subsequently
diagonalised to yield the matrices of eigenvectors and eigenvalues. While the eigenvectors reflect
the directions, the corresponding eigenvalues define the mean-square fluctuations of the
collective motions.The dominant motions of proteins were captured using g_covar, a module of
GROMACS.

Procedure
Part1
# PCA analysis (PRINCIPAL COMPONENT ANALYSIS)
1. PCA analysis is a measure of finding principal motions of a protein over the simulation
trajectory.
2. It helps to understand the dynamics of the protein.
3. The PCA analysis uses the .xtc or .pdb file as trajectory file, .tpr file and .ndx file.
4. Here, in PCA gmx covar builds and diagonalises the covariance matrix. The covariance matrix
generates Eigen vectors 1 and Eigen vectors 2, which are used to plot principal motions of a
protein.
5. Only select the backbone for the calculation as we are interested in motions of the protein main
chain atoms but not of the sidechains.
➢ /usr/local/gromacs/bin/gmxcovar -f md_protein.xtc -s md_protein.tpr
➢ Select backbone
6. To view the most dominant mode (eigenvector 1), use the following command
➢ /usr/local/gromacs/bin/gmx anaeig -v eigenvec.trr -f protein.xtc -eig eigenval.xvg -s
protein.tpr -first 1 -last 1 -nframes 100 -extr ev1.pdb
➢ Select backbone
7. To view the second most dominant mode (eigenvector 2), use the following command
➢ /usr/local/gromacs/bin/gmx anaeig -v eigenvec.trr -f protein.xtc -eig eigenval.xvg -s
protein.tpr -first 2 -last 2 -nframes 100 -extr ev2.pdb
➢ Select backbone
8. With gmx anaeig one can also calculate the overlap between the principal components and the
coordinates of the trajectory.
➢ /usr/local/gromacs/bin/gmxanaeig -v eigenvec.trr -f md_protein.xtc -eig eigenval.xvg -s
md_protein.pdb -first 1 -last 2 -2d 2dproj_ev_1_2.xvg
➢ Select backbone

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9. Use XMGRACE to plot the graph of ev1 vs ev2.

QUESTIONS

1) What percent of total dynamics of the protein will be covered in first principal motion?
2) What percent of total dynamics of the protein will be covered in Second principal motion?

Result:

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EXPERIMENT 10
A SIMPLE PROTEIN SIMULATION USING DESMOND

Aim:To perform protein simulation using DESMOND.

Theory:
Desmond is a software package developed at D. E. Shaw Research to perform high-speed
molecular dynamics simulations of biological systems. The code uses novel parallel algorithms and
numerical techniques to achieve high performance and accuracy on NVIDIA GPUs.Desmond can
compute energies and forces for the standard fixed-charged force fields used in biomolecular
simulations. A variety of integrators and support for various ensembles have been implemented in
the code, including methods for thermostatting (Nose-Hoover, Antithetic, and Langevin) and
barostatting (Martyna-Tobias-Klein and Langevin).Desmond supports algorithms typically used to
perform fast and accurate molecular dynamics. Long-range electrostatic energy and forces are
calculated using particle-mesh-based Ewald techniques.The Desmond software includes tools for
robust equilibration and energy analysis; methods for restraining atomic positions as well as
molecular configurations; support for a variety of periodic cell configurations; and facilities for
accurate checkpointing and restart. Desmond supports various force fields.

Procedure
Part 1:
# Protein Preparation
1) Import the target protein structure (6LU7) file for the system of interest into Maestro
2) Prepare the structure for simulation with the “Protein Preparation Wizard”.
This step involves removing ions and molecules (which are artefacts of crystallisation),
setting correct bond orders, adding hydrogens, filling in missing side chains or whole
residues as necessary, reorienting various groups and varying residue protonation states to
optimise the hydrogen bonding network, and then checking the structure carefully. Click
process structure.

# Solvation
3) If your system is a membrane protein, embed the protein in the membrane. This step and
the next two steps are performed in the System Builder panel.
4) If your system is a globular protein, follow the steps
5) Click on the Task menu on the right side of Maestro and select system builder.
6) Generate a solvated system for simulation.
7) Select “SPC” as water model (Default is SPC)
8) Select Box shape as “Cubic” and Distances 10 A
o

9) Click on “show boundary box”
10) Click on “Minimise Volume”.

# Adding ions

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11) Distribute positive or negative counter ions to neutralise the system, and introduce
additional ions to set the desired ionic strength (when necessary).
12) Click on the “Ions” tab and select Add salt. (Salt concentration 0.15 molar), Salt Positive
ion: NA+, Salt negative ion: CL-.
13) Give Proper Job name and click “Run”
14) How many atoms were added in the simulation box ?

# The Molecular Dynamics panel
15) Import the model system into the Molecular Dynamics environment: select either Load
from Workspace or Import from file (and select a .cms file), and then click Load. The import
process may take several minutes for large systems. For this example, select Load from
Workspace.
16) In the Simulation time box, set the total simulation time to 1 ns.
17) Select Relax model system before simulation. This is a vital step to prepare a molecular
system for production-quality MD simulation. Maestro's default relaxation protocol
includes two stages of minimization (restrained and unrestrained) followed by four stages
of MD runs with gradually diminishing restraints.
18) Give Proper Job name and click “Run”

Result:

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EXPERIMENT 11
A SIMPLE PROTEIN-LIGAND SIMULATION USING GROMACS

Aim: To perform GROMACS simulation on protein ligand complex.

Theory:
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian
equations of motion for systems with hundreds to millions of particles.It is primarily designed for
biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded
interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that
usually dominate simulations) many groups are also using it for research on non-biological
systems, e.g. polymers.GROMACS provides extremely high performance compared to all other
programs.GROMACS is user-friendly, with topologies and parameter files written in clear text
format. There is a lot of consistency checking, and clear error messages are issued when
something is wrong. As the simulation is proceeding, GROMACS will continuously tell you how far
it has come, and what time and date it expects to be finished.
The development of Gromacs would not have been possible without generous funding support
from the BioExcel HPC Center of Excellence supported by the European Union Horizon 2020
Programme, the European Research Council, the Swedish Research Council, the Swedish
Foundation for Strategic Research, the Swedish National Infrastructure for Computing, and the
Swedish Foundation for International Cooperation in Research and Higher Education.

Procedure
Part 1
# Protein Preparation
1. This is an open ended experiment, where students are allowed to choose a protein of their
interest to work with.
2. Prepare the protein as discussed in the previous experiment.
Part 2
# Ligand Preparation
1. Prepare the ligand as discussed in the previous experiment.
Part 3
# Complex Preparation
1. Prepare the Protein-ligand complex as discussed in the previous experiment.
Part 4
# System minimization
1. Perform vacuum and solvent energy minimization as discussed in the previous experiment.
Part 5
# System equilibration
1. Perform NVT and NPT equilibration as discussed in the previous experiment.
Part 6
# MD run
1. Perform MD run for 20ns.
Result:

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EXPERIMENT 12
RESULT ANALYSIS OF PROTEIN SIMULATION USING DESMOND

Aim: To perform result analysis on DESMOND simulation trajectory of protein ligand complex.
Theory:
Analysing typical MD simulation data requires substantial technical skills in addition to intimate
knowledge of the molecular system being studied. This knowledge, in combination with viewing
and animating the trajectory, can be used to create a hypothesis of how a drug-like molecule
interacts with a protein. However, movements at an atomic-level are so rapid, complex, and
occurring on different timescales, that it is very difficult to identify – and easy to overlook –
important events underlying the interactions. In addition, in the new age of dynamics-aided virtual
screening (MDVS), manually analyzing multiple systems of several different compounds can
quickly become an overwhelming and time-consuming task. SID is used to automate analyses after
a MD simulation is complete. These results are then organized in the SID panel, with plots and
diagrams for easy analysis.
In addition to providing interactive plots and diagrams for exploring the data, the SID panel can
also be used to output:
● A PDF report: A multi-page PDF report, containing all the results within the panel and a full
description of the molecular system. All the plots and diagrams are accompanied by
detailed explanations. These reports can easily be shared with colleagues or stored in
electronic notebooks for further reference.
● Images of all plots: All the visual elements and plots are easily exported into PNG and SVG
image formats, providing a means to quickly insert these images into a manuscript or a
slideshow presentation.
● Raw data: All the results can be exported into a text file for further processing and plotting
with third-party tools.
Protein and Ligand RMSD (PL-RMSD)
Root mean square deviation (RMSD) of the protein and ligand, with predefined atom selections
are pre-computed and displayed in the PL-RMSD window tab (Figure 1). Monitoring RMSD of the
protein can give insights into its structural conformation throughout the simulation, providing an
indication of the stability of the protein and whether the simulation has equilibrated. Ligand RMSD
can indicate how stable the ligand is with respect to the protein, as well as the evolution of its
internal conformation.

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Protein RMSF (P-RMSF)
Root mean square fluctuations (RMSF) of protein residues are displayed in the P-RMSF window
tab (Figure 2), enabling visualization of segments along the protein that fluctuate the most during
the simulation. Typically these fluctuations should correlate with the experimental x-ray B-factor,
which can be toggled on and off for easy comparison with experimental data.

In order to explore which protein residues come into contact with the ligand, SID allows you to
highlight these residues. Additionally, since secondary-structure elements are more rigid than the
unstructured loop region(s), this panel allows you to see how fluctuations correspond to
secondary structure elements in your simulation by overlaying alpha-helix and beta-strand regions
of the protein onto the plot.

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Protein-Ligand Interactions
Protein ligand interactions can be explored in either the PL-Contacts or LP-Contacts window tabs
(Figures 3 and 4). Various specific and nonspecific protein-ligand interactions are monitored
throughout the simulations and presented in these protein- and ligand-centric tabs. Here,
interactions are categorised into four types: hydrogen bonds, hydrophobic, ionic, and water
bridges. Using the SID panel, these interaction types can be further broken down into several
subtypes.

In Figure 4, the protein-centric results (PL-Contacts) are shown in a histogram. All residues that
come into contact with the ligand throughout the trajectory are shown here and are color-coded
by interaction types. In addition, the SID panel displays a timeline representation of these
interactions where different types of interactions can be turned on and off to explore their
occurrence in the simulation.

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Procedure
Part 1:
# Generate all the following results by using the knowledge of Literature.
1. Click on Simulation Interactions Diagram from task menu
2. Upload simulation files in .cms format
3. Generate Protein and Ligand RMSD (PL-RMSD)
4. Generate Protein RMSF (P-RMSF)
5. Generate Protein-ligand interaction plot.

Result:

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EXPERIMENT 13
RESULT ANALYSIS ON PROTEIN-LIGAND SIMULATION USING GROMACS

Aim: To perform result analysis on GROMACS simulation trajectory of protein ligand complex.

Theory:
Use the knowledge of previous experiments and perform the result analysis.

Procedure
Part1
1. Use the trajectory generated in experiment number 11 to perform following analysis.
2. First convert .xtc file into .pdb file to perform following analysis.
3. Use the trjconv command to GROMACS to convert the trajectory from .xtc format to .pdb
format.
# RMSD, RMSF, Rg, SASA and Secondary structure prediction analysis
1. Use the knowledge of previous experiments to perform the analysis.
2. Report the mean values of all analysis.
Part2
# H-bond and protein pocket analysis
1. Use the knowledge of previous experiments to perform the analysis.
2. Report the mean values of all analysis.
Part3
# MMPBSA analysis
1. Use the knowledge of previous experiments to perform the analysis.
2. Report the average binding energy between protein and ligand complex.
Part4
# PCA analysis
1. se the knowledge of previous experiments to perform the analysis.
2. Report the graphs of PCA analysis.

Result: