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APRIL 2020 VOL 5 ISSUE 4

“The

human brain is an incredible pattern-matching machine. ” -

Jeff Bezos

Docking and Virtual Screening – Special Issue

How to install AutoDock Vina on Ubuntu?


Public Service Ad sponsored by IQLBioinformatics


Contents

April 2020

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Topics Editorial....

03 Installation

06

How to install AutoDock Vina on Ubuntu? 06

5

Video Tutorial: Site-Specific Docking

Video Tutorial: How to perform docking using Autodock Vina 14

04 Installation: Video Tutorial Video tutorial: How to install Autodock Vina on Ubuntu? 09

05 Site-Specific Docking How to perform docking in a specific binding site using AutoDock Vina? 10

07

Blind Docking

How to perform blind docking using AutoDock Vina? 15

08

Vina Output Analysis

8.1 Tutorial: Vina Output Analysis Using PyMol 19


Contents

April 2020

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Topics

10.2

8.2 Video Tutorial: Autodock Vina Result Analysis with PyMol

Raccoon2: A GUI facilitating virtual screenings with Autodock and Autodock Vina 28 21

8.3

Vina output analysis using Discovery Studio visualizer 22

09 Autodock VinaXB Autodock VinaXB for molecular docking of protein complexes containing halogen bonding interactions 24

10 Virtual Screening

10.1 How to install Raccoon plugin on Ubuntu for virtual screening using Autodock? 26

10.3 How to perform virtual screening using Autodock Vina? 30

11

FAQs

11.1 Site-specific docking: Frequently Asked Questions & answers for starters 32

11.2 MGL Tools & Autodock Vina installation: Frequently Asked Questions and Answers 34


EDITOR Dr. MUNIBA FAIZA FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR Dr. MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR PRAKASH JHA NABAJIT DAS REPRINTS AND PERMISSIONS You must have permission before reproducing any material from Bioinformatics Review. Send E-mail requests to info@bioinformaticsreview.com. Please include contact detail in your message. BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION


Volume 1, Number 1, Bioinformatics Review™ is published quarterly for one year (4 issues) by Social and Educational Welfare Association (SEWA)trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under licence by SEWA trust. Published in India


Editorial: New perspectives for BiR with a new year

EDITORIAL

Welcoming BiR into a new year full of goals, new developments, and achievements. This year’s resolution of BiR is to take this endeavor to a brand new platform of international standard and reputation necessary for spreading the knowledge of bioinformatics.

Dr. Muniba Faiza

Founding Editor

As bioinformatics is growing and gaining more and more interest in biological research, it has become an essential field in the last few years. Researchers are capable of tracking evolutionary history, designing new drugs, study the nature of organisms, and so on. Keeping that in mind, this year BiR is going to focus on advanced research in bioinformatics. BiR is hoping to organize internship programs and conferences to reach out to experts in bioinformatics. Besides, new tutorials will be provided both written and video tutorials as per the suggestions of our readers and subscribers. In this aspect, BiR thus seeks viewpoints and suggestions from all the readers. We would greatly appreciate that! We wish for a number of contributions from our readers in the form of articles or questions, and feedback to further improve the science reporting. With the support of our readers, we can easily move further. BiR is thankful to all of you and wishes you a very happy new year with great success ahead. Please share your thoughts at info@bioinformaticsreview.com. Letters and responses: info@bioinformaticsreview.com


INSTALLATION

How to install AutoDock Vina on Ubuntu?

Image Credit: Stock photos

“In this small tutorial, we will learn how to install AutoDock Vina on Ubuntu.�

utoDock Vina is one of the most popular software in Bioinformatics, known for the molecular docking simulations. I have already explained the docking procedure in my previous article. In this small tutorial, we will learn how to install AutoDock Vina on Ubuntu. Its installation on Ubuntu may be a tedious process, especially for the beginners due to the unfamiliarity of the Linux commands. It is expected to work on any version of Ubuntu but make sure whether your system supports the 32- bit or 64- bit version. If you face any problem, then feel free to ask.

A

You need a good Internet connection as you will have to download different files. We need to download the following software to install AutoDock Vina: 1. MGL Tools (AutoDock Tools are important as they provide the GUI) 2. AutoDock Vina 3. Python 4. AutoDock4 5. Autogrid Getting started It is good to update and upgrade your Ubuntu system first. Log into your

Ubuntu system and open the terminal by pressing Ctrl+Alt+T altogether. and type the following commands: $ sudo apt-get update $ sudo apt-get upgrade

Downloading the files Now we are going to download the above-mentioned files one by one. You can download them either directly by visiting the websites or you can download using the command line as follows. It basically downloads in the 'Downloads' folder, so we will enter into this directory and download the files using the command line.

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$ cd Downloads/ $ wget http://mgltools.scripps.edu /downloads/tars/releases/RE L1.5.6/mgltools_i86Linux2_1 .5.6.tar.gz $ wget http://vina.scripps.edu/dow nload/autodock_vina_1_1_2_l inux_x86.tgz

It is good to install python PIL because most of the times it prompts an error for imaging C module during the installation. $ wget http://effbot.org/downloads /Imaging-1.1.7.tar.gz

In my case, this issue was not resolved even after installing the imaging C module, then I installed the following library and it resolved my case. If you also get the same error even after installing python imaging library, then try the following: $ sudo apt-get libjpeg62:i386

install

Although AutoDock Vina can run without installing AutoDock4 and Autogrid, you will need them if you run the AutoDock4 analysis from AutoDock Vina itself. Therefore, it is better to install them. In order to download them, you will have to type the following commands: $ sudo wget http://autodock.scripps.edu

/downloads/autodockregistration/tars/dist426/a utodocksuite-4.2.6i86Linux2.tar

Installation Now that we have the tar files in the Downloads folder, we will extract and install them one by one. Let's install AutoDock suite first, then later the MGL tools, and AutoDock Vina. To install them, open the terminal and go to the Downloads folder again. $ cd Downloads $ tar xvf autodocksuite4.2.6-i86Linux2.tar

This will create a new directory called "i86Linux2" in the Downloads directory, in which we could see two executable files: "autodock4" and "autogrid". We will move them to another directory where we keep the binaries. $ cd $ sudo mv /usr/local/bin $ sudo mv /usr/local/bin

i86Linux2 autodock4 autogrid4

Now we will come back to our Downloads directory to install the rest of the files. Let's install mgltools. $ cd Downloads $ tar xvzf mgltools_i86Linux2_1.5.6.ta r.gz

This will extract files of mgl tools and create a folder named "mgltools_i86Linux2_1.5.6". Now to

install it, we will go to the mgl tools directory. $ cd mgltools_i86Linux2_1.5.6 $ ./install.sh

During installation, a dialog box will prompt on your screen, its the license agreement, so just click "Ok". If everything goes well, then the terminal will show "MGLTools installation complete". It will also ask to create aliases to run them because it's a tedious task to write the full path every time we run them. So it is convenient to create aliases which I will tell you a little later after we finish installation of AutoDock Vina. We have installed mgl tools on the system, now we will go for AutoDock Vina. To do that, let's go back to our Downloads folder, where we downloaded the tar files. $ cd Downloads/ $ tar xvzf autodock_vina_1_1_2_linux_x 86.tgz

After extracting the tar file of AutoDock Vina, you will see a folder in your Downloads directory named "autodock_vina_1_1_2_linux_x86", which consists of a bin folder where you could see two setup files for vina: "vina" & "vina_split". This ends the installation section of our tutorial. Now we have successfully installed AutoDock Vina on Ubuntu. In the following section, I

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will tell you how to create aliases for the paths. Creating aliases If you would see the README file in the bin folder of mgl tools, it shows the paths for running the autodock tools and python molecular viewer. Using these paths, we will create aliases and to use them permanently we will add them in the bashrc file. Open the terminal and type the following commands:

For any query, you can comment below, or email me at muniba@bioinformaticsreview.com. References Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

$ gedit ~/.bashrc

It will open the bashrc file, go at the end of this file and type the following: alias adt='sudo /home/username/Downloads/mg ltools_i86Linux2_1.5.6/bin/ adt' alias pmv='sudo /home/username/Downloads/mg ltools_i86Linux2_1.5.6/bin/ pmv'

Save the file, go back to the terminal and enter the following command: $ source ~/.bashrc

Now, whenever you will type 'adt' or 'pmv', it will open the autodock vina or pymol viewer respectively. We are all done! You can perform docking on your Ubuntu system using AutoDock Vina whenever you want. :)

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INSTALLATION VIDEO TUTORIAL

Video tutorial: How to install Autodock Vina on Ubuntu?

Image Credit: Stock Photos

“Autodock Vina is one of the most widely used software for docking either site-specific or blind.”

T

his is a video tutorial of Autodock Vina installation on Ubuntu, based on our existing tutorial.

Video URL: https://youtu.be/XbMwSLn0swk

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SITE-SPECIFIC DOCKING

How to perform docking in a specific binding site using AutoDock Vina? Image Credit: Stock photos

“AutoDock Vina is a bioinformatics tool which is used to perform in- silico docking of proteins with a ligand. It provides many options depending on the needs of a user.”

A

utoDock Vina is a bioinformatics tool which is used to perform in- silico docking of proteins with a ligand. It provides many options depending on the needs of a user. This tool offers blind docking and binding in a specific pocket as well, which is sometimes more demanding when the binding site is already known. This article will guide you to dock a protein with a ligand in a specific binding site/ pocket. We are docking a protein Human Serum Albumin (HSA) protein with a ligand Sodium Octanoate (SO), but HSA is already complexed with 3carboxy-4-methyl-5-propylfuranpropanoic acid (CMPF). We want to bind SO in the same site where CMPF has already bound in HSA.

We need the following files prepared for docking with AutoDock Vina: 1. Pdbqt files of protein and the ligand 2. Configuration file 3. Grid file

two options, either we can read the literature which is available on the same page of PDB from where we downloaded the structure, or we can visualize the protein structure in PyMol and note down the interacting sites of the protein. The first option is recommended.

Preparation of PDB file before docking

2. Open the PDB file and remove HETATOMS.

1. Download a protein crystal structure from PDB. We are using Human Serum Albumin complexed with 3-carboxy-4methyl-5-propyl-furanpropanoic acid (CMPF) (PDB ID: 2BXA).

The structure we are using is a crystal structure complexed with ligand(s), therefore, to dock the desired ligand with the protein in that particular position we need to remove the bound ligand by removing hetatoms from the PDB file. If we will dock our ligand without removing the already complexed ligand, then we will not get correct results. We can also easily remove ligand by visualizing the protein in PyMol.

Before proceeding further, we should make clear that whether we know the catalytic site of the protein or not. If we know then we can easily go to step 2, but if we don’t know, then we have

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3. After removing hetatoms, we will keep only one of the four chains (here, Chain A was taken) and remove rest of the three chains and save this file as “protein.pdb” The chains are removed from the protein structure just to avoid the complexity. 4. Now save the “protein.pdb”.

file

as

Now we have prepared our protein structure to proceed further for docking. Now we will prepare our ligand which we want to dock with the protein. Preparation of ligand before docking 5. Open PubChem (www.pubchem.ncbi.nlm.nih.go v) and search for the compound. We are using “sodium octanoate” as a ligand. We can download the structure from ZINC database also. 6. Click on Sodium octanoate and look under “3D Structure” section, click on “Download” and then you will see four different formats for downloading it. We will download the .SDF format. 7. Since we need the protein and the ligand to be in a .pdb format, therefore, we have to convert

.SDF to .pdb. We will use PyMol for this purpose and never use online converters because they may ruin your ligand file. 8. Open PyMol, and open the downloaded ligand. Click on “File” --> “Save Molecule” --> select the molecule --> click “OK”. You can save it to your desired folder. We will rename the ligand as “SO.pdb” just to avoid any kind of confusion. Now we have a PDB file of protein and that of ligand. In order to perform docking, we need to prepare .pdbqt files from the .pdb files of the protein and the ligand, because docking through AutoDock Vina requires .pdbqt file format to dock. Preparation of .pdbqt files First, we will prepare a .pdbqt file of the ligand. 1. Open AutoDock Vina --> click “Ligand” --> click “Input”--> click “Open” It will ask to select your ligand, we will go to the folder where we have saved our ligand’s .pdb file and click “SO.pdb”. 2. Click “Ligand” --> click “Torsion Tree” --> click “Detect Root”.

It will show the torsion angle on the ligand from where it can be rotated. 3. Click “Ligand” --> click “Output” -> Click “Save as PDBQT”. We can rename the ligand, but we will use the same name as before and will name it as “SO.pdbqt” and save it in the same folder. We have prepared a .pdbqt file of the ligand and now we will prepare the protein file. 4. Open AutoDock Vina, click “File” --> click “Read Molecule” --> select protein.pdb. 5. We will delete water molecules from the protein as they can make unnecessary bonds with the ligand. Click “Edit” à click “Delete water”. 6. We will add polar hydrogens in order to avoid any empty group/ atom left in the protein. Click “Edit” --> click “Add Hydrogens” -> click “Polar only”. 7. We will save this file as .pdbqt, click “Grid” --> click “Macromolecule” --> click “Choose” --> select the “protein.pdb” --> click “OK”. It will ask for a folder to save, then save it as "protein.pdbqt", in the same folder where the pdbqt file of ligand was saved.

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Defining binding site Now we will define the binding site in the protein. Look at the panel in the left corner of Vina window. You will see the name of the protein written there. Click on it once, then it will show you the chains present in the structure, but since we have deleted all the chains except the chain A, so there is only one chain, i.e., chain A. Click on it, then it will display all the residues present in the chain. Now scroll down and look for your desired residues. For example, in this protein, we will look for Tyr150, Lys199, Arg222, Arg257, and His242 because this is the binding site of CMPF in human serum albumin and we want to bind another ligand in the same position. On the right side of dropdown of all residues, we can see some squares and oval shaped images. By clicking these, we can see the selected residues in different types, such as on clicking “R”, the residue will be displayed in ribbon-shaped, but we will only select all the residues by clicking “S”. Now the selected residues will appear differently such as yellow in color. Now we have selected all the residues and we will define the grid box which can enclose all these residues inside itself. Defining Grid Box for docking

Now we will define the specific site for binding of a ligand in the protein. In blind docking, we do not need to define a specific site in the protein for the ligand to bind, because we do not know the binding site, so we enclose the whole protein into the grid box. But here we already know the binding site in this protein, therefore, we will define binding site for the ligand in the grid box. 1. Click “Grid” --> click “Grid Box”. You will see a small window in which you can see x, y, and z coordinates. 2. Now try to adjust the grid box by scrolling the three coordinates, such that it covers all the selected residues. 3. After adjusting the grid box, click “File” --> click “Output Grid Dimension File” --> save this file as grid.txt in the same folder. 4. Click “File” --> Click “Close saving current”. 5. Now close the Autodock Vina. You will get the grid file as follows:

Preparation of Configuration file AutoDock Vina requires an input configuration file which contains all the information of the parameters used in configuring the docking including the name of the protein and the ligand. The configuration is as follows: receptor= protein.pdbqt ligand= SO.pdbqt

center_x= 4.402 center_y= -8.060 center_z= 8.874

size_x= 66 size_y= 56 size_z= 54

out= vina_outSO.pdbqt log= logSO.txt

grid.txt exhaustiveness= 8

Protein spacing 0.375 npts center

66 56 54 4.402 -8.060 8.874

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From “grid.txt” file, we have written the center_x, y, and z coordinates, and also the size_x, y, and z of the grid box. Save this file as “conf.txt”. Perform Docking Put all the following in the same folder (i.e., dock): 1. protein.pdbqt 2. SO.pdbqt 3. conf.txt 4. All the MGL_Tools, Autodock Tools, Python.exe (for Linux) and Autodock Vina setup files. Please keep in mind that you have named the files properly and kept all the setup files in the same folder otherwise you may get errors while running the docking.

1. Open the command prompt and enter the folder where all the docking files are placed. 2. Type the following command: vina -–config conf.txt -–log logSO.txt Now grab a cup of tea/ coffee because it may take a few minutes to complete the docking, so meanwhile, enjoy your time! ;) Vina Output After the successful docking, you will get a log file, which in this case is named as “logSO.txt”.

This file consists of all the poses generated by the AutoDock Vina along with their binding affinities and RMSD scores. In the Vina output log file, the first pose is considered as the best because it has more binding affinity than the other poses and without any RMSD value, but you can choose the appropriate pose and visualize it in PyMol viewer. Please share if you like this article! If you find any difficulty, then feel free to mail me at muniba@bioinformaticsreview.com. References

The log file will be as follows:

1.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

Linux 1. Open the terminal and enter into the “dock” folder. 2. Type the following command: ./vina -–config conf.txt -–log logSO.txt 3. Press “enter”. Windows

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DOCKING VIDEO TUTORIAL

Video Tutorial: How to perform docking using Autodock Vina

Image Credit: Stock photos

“Autodock Vina is a program for molecular docking and virtual screening. It is a most widely used tool for site-specific docking.�

T

his is an video addition to our existing tutorial (How to perform site-specific docking using Autodock Vina). It is also available on YouTube. Reference Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

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BLIND DOCKING

How to perform blind docking using AutoDock Vina? Image Credit: Stock photos

“Blind docking is done when the catalytic/binding residues are unknown in a protein, hence, the binding pocket is unknown.�

B

lind docking is done when the catalytic/binding residues are unknown in a protein, hence, the binding pocket is unknown. In a previous article, we showed how to perform site-specific docking using Autodock Vina, where we bound a ligand in a catalytic pocket of a protein. Now, this article will cover the blind docking using Autodock Vina. We are docking the same protein Human Serum Albumin (HSA) with a ligand Sodium Octanoate (SO) but since the HSA is already complexed with 3-carboxy-4-methyl-5-propylfuranpropanoic acid (CMPF), therefore, it should be removed first leaving the only protein.

As mentioned previously, we need the following files prepared for docking with AutoDock Vina: 1. Pdbqt files of protein and the ligand 2. Configuration file 3. Grid file

Preparation of PDB file before docking 1. Download a protein crystal structure from PDB. We are using Human Serum Albumin complexed with 3-carboxy-4methyl-5-propyl-furanpropanoic acid (CMPF) (PDB ID: 2BXA).

2. Open the PDB file and remove HETATOMS. The structure we are using is a crystal structure complexed with a ligand, therefore, in order to know the binding position of our ligand, we need to empty all the binding pockets by removing the bound ligand which can be done by deleting all hetatoms from the PDB file. If we will dock our ligand without removing the already complexed ligand, then we will not get the correct results. We can also easily remove ligand by visualizing the protein in PyMol. 3. After removing hetatoms, we will keep only one of the four chains (here, Chain A was taken) and remove the rest of the three Bioinformatics Review | 15


chains and save this file as “protein.pdb” The chains are removed from the protein structure just to avoid complexity. But remember to read about the structure of your protein to know what chains are necessarily involved in the protein functioning. 4. Now save the “protein.pdb”.

file

as

Now we have prepared our protein structure to proceed further for docking. Now we will prepare our ligand which we want to dock with the protein. Preparation of ligand before docking 5. Open PubChem (www.pubchem.ncbi.nlm.nih.go v) and search for the compound. We are using “sodium octanoate” as a ligand. We can download the structure from the ZINC database also. 6. Click on Sodium octanoate and look under “3D Structure” section, click on “Download” and then you will see four different formats for downloading it. We will download the .SDF format. 7. Since we need the protein and the ligand to be in a .pdb format, therefore, we have to convert .SDF to .pdb. We will use PyMol

for this purpose and never use online converters because they may ruin your ligand file. 8. Open PyMol, and open the downloaded ligand. Click on “File” --> “Save Molecule” --> select the molecule --> click “OK”. You can save it to your desired folder. We will rename the ligand as “SO.pdb” just to avoid any kind of confusion. Now we have a PDB file of protein and that of the ligand. In order to perform docking, we need to prepare .pdbqt files from the .pdb files of the protein and the ligand, because docking through AutoDock Vina requires .pdbqt file format to dock. Preparation of .pdbqt files First, we will prepare a .pdbqt file of the ligand. 1. Open AutoDock Vina --> click “Ligand” --> click “Input”--> click “Open” It will ask to select your ligand, we will go to the folder where we have saved our ligand’s .pdb file and click “SO.pdb”. 2. Click “Ligand” --> click “Torsion Tree” --> click “Detect Root”.

3. Click “Ligand” --> click “Output” -> Click “Save as PDBQT”. We can rename the ligand, but we will use the same name as before and will name it as “SO.pdbqt” and save it in the same folder. We have prepared a .pdbqt file of the ligand and now we will prepare the protein file. 4. Open AutoDock Vina, click “File” --> click “Read Molecule” --> select protein.pdb. 5. We will delete water molecules from the protein as they can make unnecessary bonds with the ligand. Click “Edit” à click “Delete water”. 6. We will add polar hydrogens in order to avoid any empty group/ atom left in the protein. Click “Edit” --> click “Add Hydrogens” -> click “Polar only”. 7. We will save this file as .pdbqt, click “Grid” --> click “Macromolecule” --> click “Choose” --> select the “protein.pdb” --> click “OK”. It will ask for a folder to save, then save it as "protein.pdbqt", in the same folder where the pdbqt file of the ligand was saved.

It will show the torsion angle on the ligand from where it can be rotated.

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Again, since it is a tutorial for blind docking, there is no need to define binding residues. Defining Grid Box for docking

Preparation of Configuration file

In blind docking, we do not need to define a specific site in the protein for the ligand to bind, because we do not know the binding site, so we enclose the whole protein into the grid box. Make sure the whole protein fits inside the grid box.

AutoDock Vina requires an input configuration file which contains all the information of the parameters used in configuring the docking including the name of the protein and the ligand. The configuration is as follows: conf.txt

1. Click “Grid” --> click “Grid Box”. You will see a small window in which you can see x, y, and z coordinates. 2. Now try to adjust the grid box by scrolling the three coordinates, such that it covers all the selected residues. 3. After adjusting the grid box, click “File” --> click “Output Grid Dimension File” --> save this file as grid.txt in the same folder.

receptor= protein.pdbqt

From “grid.txt” file, we have written the center_x, y, and z coordinates, and also the size_x, y, and z of the grid box. Save this file as “conf.txt”. Perform Docking Put all the following in the same folder (i.e., dock): 1. protein.pdbqt 2. SO.pdbqt

ligand= SO.pdbqt 3. conf.txt

center_x= 0.430

4. All the MGL_Tools, Autodock Tools, Python.exe (for Linux) and Autodock Vina setup files.

center_y= 6.575 center_z= -0.235

4. Click “File” --> Click “Close saving current”.

size_x= 72

5. Now close the Autodock Vina.

size_y= 94

You will get the grid file as follows:

size_z= 72

Please keep in mind that you have named the files properly and kept all the setup files in the same folder otherwise you may get errors while running the docking. Linux 1. Open the terminal and enter into the “dock” folder. 2. Type the following command:

grid.txt protein

out= vina_outSO.pdbqt

spacing 0.375 npts 66 56 54 center 4.402 -8.060 8.874

log= logSO.txt

./vina -–config conf.txt -–log logSO.txt 3. Press “enter”.

exhaustiveness= 8

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Windows 1. Open the command prompt and enter the folder where all the docking files are placed.

Please share if you like this article! If you have any query, then feel free to contact me at muniba@bioinformaticsreview.com.

2. Type the following command: vina -–config conf.txt -–log logSO.txt Vina Output After the successful docking, you will get a log file, which in this case is named “logSO.txt”.

References 1.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

The log file will look like this:

This file consists of all the poses generated by the AutoDock Vina along with their binding affinities and RMSD scores. In the Vina output log file, the first pose is considered as the best because it has more binding affinity than the other poses and without any RMSD value, but you can choose the appropriate pose and visualize it in PyMol viewer.

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VINA OUTPUT ANALYSIS

Tutorial: Vina Output Analysis Using PyMol

Image Credit: Stock photos

“This is the basic introductory tutorial; you can always explore more to represent the complex more beautifully. There are several ways to represent the protein and the ligand such as ribbon, surface, cartoon, and more. You can also show only binding pockets of the protein as per your requirements.�

T

he analysis of Autodock Vina [1] results is a bit tricky in the sense of viewing all interactions and selecting the best pose. In our last video tutorial, we explained how to analyze docking results obtained from Vina using PyMol. This article is the written guide for the same.

2bxa.pdb) 'Open'.

-->

click

It will display the

protein. 2. Now open the vina output file.

Again go

'cmpf.pdbqt', and some letters such as 'A' (Action), 'S' (Show), 'H' (Hide), and 'L' (Label) right in front of these tabs.

to 'File' --> 'Open' --> then select the pdbqt file (here, cmpf.pdbqt) --> click 'Open'.

3. Go to the tab 'all' --> click 'A' (it will show you a drop-down menu) --> 'preset' --> 'ligand sites' --> 'cartoon'.

We need a PDB file of protein and vina output file in .pdbqt format. Here, our protein file is 2bxa.pdb and the vina output file is cmpf.pdbqt. Let's get started!

You can now see the protein and the ligand but there are no bondings/interactions between the two. Now, in order to see the interactions, follow these steps:

It will re-center your complex and show the interactions the protein is making with the ligand.

1. Open PyMol then go to 'File' --> 'Open' --> then select the PDB file of your protein (here,

Look at the middle right side in PyMol window, there you can see some tabs named as 'all', '2bxa.pdb', and

You can also measure the bond lengths following these steps: 1. Go

to 'Wizard' 'Measurement'.

Bioinformatics Review | 19

-->


It will ask to select the atoms. 2. Select two atoms from both the protein and the ligand showing interaction. It will display the length.

Similarly, you can label the residues as well by going to 'Wizard' --> 'Label'

Now, there comes the point where you need to choose the best docking pose. Well, the best pose is supposed to be the first docking pose generated by Vina which shows 'zero' RMSD

value and best binding affinity. You may also need to make sure whether your ligand is interacting to all the binding/catalytic residues you defined for docking. Sometimes, your second pose may also show the required maximum interactions within the binding pocket.

more. You can also show only binding pockets of the protein as per your requirements.

This is the basic introductory tutorial; you can always explore more to represent the complex more beautifully. There are several ways to represent the protein and the ligand such as ribbon, surface, cartoon, and

Reference

I hope this tutorial helps! For any query and suggestions, please write to us at info@bioinformaticsreview.com and tariq@bioinformaticsreview.com

1.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

Bioinformatics Review | 20


ANALYSIS VIDEO TUTORIAL

Video Tutorial: Autodock Vina Result Analysis with PyMol Image Credit: Stock photos

“This is a continuation video tutorial of docking.”

T

his is a video tutorial to demonstrate the analysis of Autodock Vina results using PyMol, in continuation of our existing docking tutorial.

Video URL: https://youtu.be/MUEf-fYmyfI

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VINA OUTPUT ANALYSIS

Vina output analysis using Discovery Studio visualizer

Image Credit: Stock photos

“In previous articles ("Tutorial: Vina Output Analysis Using PyMol" and "Video Tutorial: Autodock Vina Result Analysis with PyMol"), the analysis of Autodock Vina [2] result using the Pymol viewer [3] was explained.”

he discovery studio (DS) visualizer [1] offers several features for analyzing docking results. In previous articles ("Tutorial: Vina Output Analysis Using PyMol" and "Video Tutorial: Autodock Vina Result Analysis with PyMol"), the analysis of Autodock Vina [2] result using the Pymol viewer [3] was explained. In this article, the Autodock Vina result is being analyzed in the DS visualizer [1].

T

To visualize Autodock Vina results in DS visualizer, you need the same files as used for the Pymol viewer: protein in .pdb format (here, 2bxa.pdb) and vina output file (here, SO.pdbqt). 1. Open DS visualizer.

2. Open “2bxa.pdb” and “SO.pdbqt” files.

3. In the “pdbqt” tab, you will see a dropdown in a new left panel showing different poses of the ligand named as model_0, model_1, and so on. Click on one of these poses and the ligand will switch its position on the screen. Select one of these poses, say, “model_4”, --> rightclick on the blank space --> click “Copy”.

4. Go to the receptor tab, here, “pdb” --> rightclick on the blank space --> click “Paste”. After

that, you will see the ligand somewhere within the protein. But it is not showing any interactions yet.

5. To see the interactions between the protein and the ligand, look at the panel in the left corner for “Display receptor-ligand interactions.” Just below this, click on “Ligand interactions”. After that,

you will be able to see the interactions.

Bioinformatics Review | 22


6. To see the bond length, click “Show Distances” under the same section of “Display receptor-ligand interactions.”

7. For labeling the interacting residues, double-click the amino acid residue, it will become highlighted. Right-click on it, a menu will appear, then click “Label” --> “Add”.

3. Trott, O., & Olson, A. J. (2010). Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334 4. Schrödinger, LLC. (2015). The PyMOL Molecular Graphics System, Version{\textasciitilde}1.8. Schrödinger, LLC.

A small window will appear where you can select what you want to label, for example, one letter or three-letter amino acid residues, atoms, etc. You can also select the font and size. After setting everything, click “Ok”. You can see the receptor in different surfaces such as hydrophobic, charge, ionizability, and so on. They can be selected under the section in the left corner titled “Display receptor surfaces.”. This is article covers the basic steps for Vina output analysis. You can always look for further operations including changing the color of the receptor protein and the ligand and save them in different formats. You can do this for different poses generated by Vina. References 2. DSV3. (2010). Discovery Studio Visualizer v3.0. Accelrys software inc.

Bioinformatics Review | 23


AUTODOCK VINAXB

Autodock VinaXB for molecular docking of protein complexes containing halogen bonding interactions Image Credit: Stock photos

“Protein data bank (PDB) [3] contains more than 200 entries of such protein complexes. This halogen bonding is used for lead optimization in rational drug design [4].� olecular docking is the most widely used technique which is used to predict the binding affinities and bound conformations of a ligand and protein complex. Autodock Vina [1] is one of the most widely used docking software for structure-based drug designing. Suppose, your multifasta file is "input.fa", in which you want to search for the motif patterns.

M

Halogen bonding interactions exist between the protein and ligands containing halogens [2]. Protein data bank (PDB) [3] contains more than 200

entries of such protein complexes. This halogen bonding is used for lead optimization in rational drug design [4]. Most of the docking softwares do not include halogen bonding in their scoring functions and therefore, unable to successfully predict the bound conformations for such complexes. For this purpose, Autodock Vina has implemented a new halogen bonding scoring function called XBSF in a new Vina program known as Autodock VinaXB [5]. It even provides better results than Vina [5]. Installation

The installation of Autodock VinaXB is very simple. First, download the executable and then run it. The executable can be downloaded from here and the source code can be downloaded from here for Mac, Linux, and Windows as well. For Linux users, if you have trouble using the executable as it is, then, enter into the directory where you have downloaded the file, open a terminal (Ctrl+Alt+T) and type the following commands: $ cd Downloads $ chmod +x VinaXB Bioinformatics Review | 24


Run VinaXB Autodock VinaXb has as same command-line arguments as Autodock Vina, just replace vina with VinaXB as follows: $ ./VinaXB --config conf.txt --log log.txt

Same goes with Windows, just use vinaXB.exe instead of vina.exe in the command prompt.

References 1.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

2.

Auffinger, P., Hays, F. A., Westhof, E., & Ho, P. S. (2004). Halogen bonds in biological molecules. Proceedings of the National Academy of Sciences, 101(48), 16789-16794.

3.

Bank, P. D. (1971). Protein data bank. Nature New Biol, 233, 223.

4.

Sirimulla, S., Bailey, J. B., Vegesna, R., & Narayan, M. (2013). Halogen interactions in protein-ligand complexes: implications of halogen bonding for rational drug design. Journal of chemical information and modeling, 53(11), 2781-2791.

5.

Koebel, M. R., Schmadeke, G., Posner, R. G., & Sirimulla, S. (2016). AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. Journal of cheminformatics, 8(1), 27.

Bioinformatics Review | 25


VIRTUAL SCREENING

How to install Raccoon plugin on Ubuntu for virtual screening using Autodock? Image Credit: Stock photos

“Raccoon plugin provides a graphical user interface (GUI) to perform virtual screening using Autodock.� s mentioned in our previous articles, Autodock Vina [1] is a very useful bioinformatics tool for molecular docking and provides various options for sitespecific docking and blind docking. But it seemed to be difficult to perform virtual screening using Autodock. Recently, Autodock has developed a plugin known as 'Raccoon' [2], which serves for this purpose.

A

Raccoon plugin provides a graphical user interface (GUI) to perform virtual screening using Autodock. The users don't have to prepare pdbqt files on their own, they can easily load the ligand and protein files in the Autodock using the Raccoon GUI and perform docking. It involves other features such as ligand filtration,

generate configuration files for Autodock, automated generation of virtual screening script, multiple receptor conformations, and automated processing of ligand libraries. The installation of this plugin is very easy if you have already installed Autodock on your system, if not, then please follow this tutorial first. How to install Raccoon plugin on Ubuntu? 1. Download the tar file of Raccoon (http://autodock.scripps.edu/re sources/raccoon). 2. save it in a directory let's say, Downloads.

3. Change to the Downloads directory and untar the file: $ cd Downloads $ tar xvzf 1.0b.tar.gz

raccoon-

You will notice a new 'raccoon.py' file has been added to the same directory. 4. Open a terminal (Ctrl+Alt+T) and change to the directory where you extracted the tar file and type the following commands: $ cd Downloads $ pythonsh raccoon.py

A GUI will be displayed as shown in Fig. 1.

Bioinformatics Review | 26


$ alias raccoon='/home/user/Download s/mgltools_x86_64Linux2_1.5. 6/bin/pythonsh /home/user/Downloads/raccoon .py'

G) protospacer adjacent motif (PAM) site and zero, one, two, or three mismatches complementary to the sgRNA using Bowtie, which are regarded as off-target binding sites. The penalty score for NAG off-target is smaller than NGG off-target. The sgRNAs are finally ranked by the sum of E-score and S-score.

Save the file, go back to the terminal and type: $ source ~/.bashrc

For c-shell, command:

Fig.1 the GUI of Raccoon [2]. Now you have successfully installed raccoon plugin on your system. In most of the cases, the pythonsh command is not recognized, then change to the installed MGL tools directory (let's say, MGL Tools is a subdirectory of Downloads) and type the following commands: $ cd /home/user/Downloads/mgltool s_x86_64Linux2_1.5.6/bin/pyt honsh raccoon.py

If you don't want to type the full path every time you execute the raccoon script, then add an alias in your bashrc file using the following command: $gedit ~/.bashrc

It will open the bashrc file, go at the end of this file and type the following:

use

the

following

$ alias raccoon '/home/user/Downloads/mgltoo ls_x86_64Linux2_1.5.6/bin/py thonsh /home/user/Downloads/raccoon .py'

If you have any query, then email at muniba@bioinformaticsreview.com CRISPR-ERA looks up all targetable sites for each target gene, for patterns of N20NGG (N = any nucleotide). It then calculates E and S score.

The result it then presented according to the E and S score. References 1.

2.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461. Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905-919.

1. E-score is the efficacy score based on the sequence features such as GC content (%GC), presence of poly-thymidine and location information 2. S-score is the specificity score based on the genome-wide offtarget binding sites. For each sgRNA design, genome-wide sequences are computed that contain an adjacent NRG (R = A or

Bioinformatics Review | 27


VIRTUAL SCREENING

Raccoon2: A GUI facilitating virtual screenings with Autodock and Autodock Vina Image Credit: Stock photos

“Raccoon2 incorporates some new features such as an automatic server connection manager, loading multiple receptor structures and flexible residues, GUI for docking setup and grid box, result filtering options by energy or ligand efficiency, and interactions, easy management or remote jobs, and automatic installation of docking services (for Autodock Vina only).� n the previous article, a new plugin called 'Raccoon' was mentioned, which helps in preparing virtual screening using Autodock. It provides a simple graphical user interface (GUI) where you can easily load the ligand and protein files in Autodock and perform virtual screening. An advanced version of Raccoon called, 'Raccoon2' has been introduced by Forli et al., (2016) [1] as GUI to prepare and analyze Autodock and Autodock Vina virtual screenings.

I

Raccoon2 incorporates some new features such as an automatic server connection manager, loading multiple receptor structures and flexible

residues, GUI for docking setup and grid box, result filtering options by energy or ligand efficiency, and interactions, easy management or remote jobs, and automatic installation of docking services (for Autodock Vina only). How to install Raccoon2? Raccoon2 is added in MGLTools and the installer can be downloaded from here. It is available for Windows, Linux, and MAC OS. Once the installer is downloaded (let's say in Downloads directory), open the terminal and type the following commands:

$ cd Downloads $ chmod +x mgltools_Linuxx86_64_1.5.7rc1_install $ ./mgltools_Linuxx86_64_1.5.7rc1_install

If it shows an error "Permission denied", then try $ sudo ./mgltools_Linuxx86_64_1.5.7rc1_install

Accept the terms of the software and choose the installation folder, and click 'Finish' after the complete installation. You can see a new directory named 'MGLTools-1.5.7rc1' has been created in the Downloads directory.

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Now, in order to run this via terminal, create an alias and add it in your bashrc file using the following commands: $gedit ~/.bashrc

It will open the bashrc file, go at the end of this file and type the following: $ alias adtrc='/home/user/Downloads/ MGLTools-1.5.7rc1/bin/adt'

Save the file, go back to the terminal and type: $ source ~/.bashrc

Try running adtrc from terminal, it should be working.

your

References 1.

Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905-919.

Bioinformatics Review | 29


VIRTUAL SCREENING

How to perform virtual screening using Autodock Vina? Image Credit: Stock photos

“Autodock Vina [3] which is a freely accessible software and provides good results can also be used for screening various ligands. Recently, Autodock Vina has provided plugins [4] to facilitate virtual screening.� irtual screening is used to identify small molecules that are most likely to bind to a target protein. There is various software available for virtual screening including GOLD [1] and GLIDE [2]. Autodock Vina [3] which is a freely accessible software and provides good results can also be used for screening various ligands. Recently, Autodock Vina has provided plugins [4] to facilitate virtual screening. In previous articles, we have provided tutorials for the installation of Raccoon and Raccoon2 plugins on Ubuntu.

V

These plugins provide a graphical user interface and there is no need to prepare pdbqt files of protein and ligands. You can simply load your target protein and ligand pdb files. In

this article, we will use a Perl script or a bash script to screen several ligands for a target protein.

If you are using Perl script, then list the names of prepared pdbqt files of ligands in a text file.

Preparing input files

If you are using the bash script, save the ligands pdbqt files as "ligand_name.pdbqt", where the name could be any letter. For example, "ligand_3.pdbqt" or "ligand_hydrocl. pdbqt", and so on.

The docking procedure will be the same as mentioned in previous articles of site-specific docking and blind docking. (Video tutorials: sitespecific docking and blind docking). Prepare pdbqt files of the target protein and all ligands. Save them into the same directory. Downloading scripts For Ubuntu/Linux, download these files: Perl script and bash script.

Further, the configuration file will remain the same as mentioned in previous articles except for the second line, i.e., "Ligand = ligand.pdbqt". You can download the configuration file from here (change the coordinates as per your grid dimensions). For Windows, download this file:

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Perl script. Running Vina for virtual screening

References 1.

Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. Journal of molecular biology, 267(3), 727-748.

2.

Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., ... & Shaw, D. E. (2004). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry, 47(7), 1739-1749.

3.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

4.

Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905-919.

On Ubuntu: Open the terminal (Ctrl+Alt+T) and type the following command: $ perl vina_vs_linux.pl

It will prompt for the name of file containing all the names of ligands. Enter the name of the file and continue. or $ ./vina_vs.sh

On Windows: Open command prompt (type cmd in the search tab) and navigate to the folder where you have kept all files including vina.exe. Type the following command: >perl vina_vs_win.pl

It will also prompt for the name of file containing all the names of ligands. Enter the name of the file and continue. or > bash vina_vs.sh

Make sure you have already installed and added Vina in your path on Ubuntu. If not, then follow this tutorial.

Bioinformatics Review | 31


FAQS

Site-specific docking: Frequently Asked Questions & answers for starters Image Credit: Stock photos

“I have collected some frequently asked questions and provided the link to their answers present in our question answer section of Bioinformatics Review.� have been getting several Emails from researchers and students alike regarding insilico docking. Most questions are similar in nature, so I thought of answering them once and for all. In this article, I have collected some frequently asked questions and provided the link to their answers present in our question answer section of Bioinformatics Review.

"We cannot solve our problems with the same thinking we used when we created them"

Question: Since the protein is homo-dimer, should I get the same binding affinity values of ligand in two monomers?

In this article, I have collected some of the most Frequently Asked Questions while performing site-specific and/ or blind docking. You have to consider a lot of factors before performing an actual docking on a protein with a specific ligand.

Question: Since I know the binding sites in both monomers, so while doing docking do I necessarily need to mimic (preserve) the attachment of the ligand to these binding sites, so as to get the binding energy?

It is good to have questions in mind and they can be solved in a way as quoted by Sir Einstein:

Question: How do you predict protein's binding sites?

I

Question: What is the difference between the blind docking and binding site based docking?

Question: While doing docking, if, in addition to the known binding site attachments, the ligand is showing few more attachments with some residues. Do they contribute to the binding energy ?? if Yes, do I need to Bioinformatics Review | 32


dock the ligand in such a way so that, it shows attachments with the ligand as it is showing in PDB file when viewed in Pymol? Question: What about the conformation of the docked ligand ?? Should the docked ligand exactly fall on the ligand which we already have? Question: I have to find the 4 Armstrong neighborhood of a ligand. When I take the receptor molecule without adding hydrogen bonds and find 4A nbd of ligand, it gives one result. But when I first add hydrogens to the receptor molecule and then after finding the 4A nbd of ligand, it obviously gives different results. I want to know which one is better ?? Second thing, if I have to find the distance between a ligand and any residue in receptor molecule. Do I have to take the central atom distances or minimum distance between any two atoms of the ligand and receptor molecule can be considered ?? Question: Why are the hetatoms removed from the protein PDB file before docking? Question: Why do we choose only one chain of protein for docking?

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FAQ S

MGL Tools & Autodock Vina installation: Frequently Asked Questions and Answers Image Credit: Stock photos

“In this article, I have collected some frequently asked questions and provided the link to their answers present in our question-answer section of Bioinformatics Review.” have been receiving several emails from researchers and students alike regarding the installation of MGL Tools and Autodock Vina on Ubuntu. Most questions are similar in nature, so I thought of answering them once and for all. In this article, I have collected some frequently asked questions and provided the link to their answers present in our question-answer section of Bioinformatics Review.

I

Question: Error in running autodock vina adt mgltools on Linux.

Question: Error while trying to open Autodock tools.

Question: Error while installing mgltools.

Question: Problem in launching Autodock tools from the terminal.

If these are not the questions you are looking for, then feel free to ask in our 'Bioinformatics Discussion' section or write to info@bioinformaticsreview.com.

Question: Problem in moving Autogrid and Autodock. Question: How should I use “pmv” or “adt”? Question: Autodock terminal.

Error Vina

in running from the

Question: Unable to run Autodock using “adt” alias.

Bioinformatics Review | 34


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