Drug design of class D GPCR Ste2 by artificial intelligence and classical algorithm

The detailed process of drug design of class D GPCR Ste2: see https://doi.org/10.1016/j.csbj.2021.06.017 

The Saccharomyces cerevisiaepheromone receptor Ste2 that belongs to one member of the class D GPCRs family exists as an essential dimer for signaling and functional endocytosis [35] in yeast cells. The drugs targeted to Ste2 can be used to treat intractable fungal diseases. The cryo-EM structure of Ste2 contains the high-affinity agonist tridecapeptide pheromone α-factor (WHWLQLKPGQPMY) in the orthosteric binding site……………

An example of packaging deep learning model "AIGenMols" for MolAICal

1. Introduction
Sometimes, the "AIGenMols" of MolAICal (https://doi.org/10.1093/bib/bbaa161) have not good compatibility in Linux operating system. In this case, it needs to generate the binary "AIGenMols" again. In this tutorial, the ORGAN (https://github.com/gablg1/ORGAN) is chosen for the installation of “AIGenMols”. Besides, you can build the "AIGenMols" from your trained deep learning model.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io
2) Anaconda: https://www.anaconda.com

You can choose the free version of individual edition of Anaconda. The version of Anaconda should be chosen based on Pyhon 3.x rather than Python 2.x, etc.

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/specialtopic/tree/master/012-AIGenMols

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                        Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorial of splitting ligands into smaller pieces by MolAICal

1. Introduction
Sometime, to study the fragment properties of ligands, the ligands need to be split into smaller pieces. Here, MolAlCal (https://doi.org/10.1093/bib/bbaa161) provides a way to split ligands into small fragments according to rotatable bonds. In this tutorial, the method of fragment split is introduced based on the protocol of MolAICal.  

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/013-fragmentSplit 
 
For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                   Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorial of ligand similarity search or comparsion based on the fingerprint similarity and 3D structural similarity

1. Introduction
Sometimes, the similarity search of ligands can help scientists to find potential compounds fastly. In this tutorial, the fingerprint similarity and 3D structural similarity searches are introduced for molecular comparison or simple virtual screening based on appointed ligand structure. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/011-similaritySearch

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                  Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorial of binding free energy based on the value of Kd, Ki, pKd or pKi by MolAICal

1. Introduction
Sometimes, it needs to calculate the binding free energy (e.g. training Vinardo score) according to the Ki, Kd, pKd or pKi values from PDBBind database. Before calculating binding free energy, the International System of Units (SI) is introduced. Most laboratory and literature use mol/dm3, which is the same as mol/L (also named “M”). For example:
mol/m3 = 10−3 mol/dm3 = 10−3 mol/L = 10−3 M = 1 mmol/L = 1 mM.

The "millimolar" and "micromolar" refer to mM and μM (10−3 mol/L and 10−6 mol/L), respectively. About the detail relative information of molar concentration units, please see the website: https://en.wikipedia.org/wiki/Molar_concentration

In this tutorial, the MolAICal (https://doi.org/10.1093/bib/bbaa161) provides an easy way to calculate binding free energy if the value of Ki, Kd, pKd or pKi is given.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/010-pkdEnergy

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                          Figure 1


You can repeat this tutorial according to the content of pdf file.

Tutorials of the calculations of Synthetic Accessibility, Lipinski's rule of five and Pan-assay interference compounds (PAINS) by MolAICal

1. Introduction
In this tutorial, the calculations of synthetic accessibility, Lipinski's rule of five and PAINS are introduced. The synthetic accessibility (SA) can be used to evaluate the synthesis difficulty of the compound. The Lipinski's rule of five also known as the rule of five (RO5) is a rule to estimate drug-like or determine if a chemical compound has pharmacological or biological activity that would be likely orally active drug1. Pan-assay interference compounds (PAINS)2 are the compounds which often show the false positive results in the biological assay. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.
 
2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/009-SA_Ro5_Pains

3. Procedure
3.1. SA calculation
You can calculate the SA by SMILES sequence directly.
#> molaical.exe -tool sa -i "FC(F)(F)c1cc(ccc1)N5CCN(CCc2nnc3[C@H]4CCC[C@H]4Cn23)CC5"

Or you can calculate SA of ligands in one file that contains many SMILES sequences.
#> cd “009-SA_Ro5_Pains/SA”
#> molaical.exe -tool sa -i SmilTest.smi

Note: the higher value of SA indicates the compound is easier to be synthesized.

3.2. RO5 calculation
The RO5 of single ligand can be calculated as below:
#> cd “009-SA_Ro5_Pains/ro5”
#> molaical.exe -tool ruleoffive -f mol2 -n zinc_1879871.mol2

If you want to calculate Lipinski's rule of five for many ligands in bulk, you can do it below steps:
 
1. Using command “ls > mol2List.dat” in Linux console, or “dir /b > mol2List.dat” in DOS console of Window. Open generated file “mol2List.dat” and delete no useful characters. Make sure the file “mol2List.dat” only contains the ligand names.

2. Run command as below:
#> molaical.exe -tool ruleoffive -f mol2list -i mol2List.dat -o result.dat
It will generate the file named “result.dat” which contains RO5 values in bulk. More detail about RO5, please see the manual of MolAICal.

3.3. PAINS calculation
The single ligand with SMILES or mol2 format can be calculated as below:
#> cd “009-SA_Ro5_Pains/pains”
#> molaical.exe -tool pains -f smi -n "c1ccccc1N=Nc1ccccc1"
#> molaical.exe -tool pains -f mol2 -n ZINC00154323.mol2

If you want to calculate PAINS for many ligands in bulk, you can do it below steps:
 
For SMILES format:
#> molaical.exe -tool pains -f smilist -i painsTest.txt -o smiResult.txt

For Mol2 format:
#> cd “009-SA_Ro5_Pains/pains/mol”

1. Using the command “ls > mol2List.dat” in Linux console, or “dir /b > mol2List.dat” in DOS console of Window. Open generated file “mol2List.dat” and delete no useful characters. Make sure the file “mol2List.dat” only contains the ligand names.

2. run command as below:
#> molaical.exe -tool pains -f mol2list -i mol2List.dat -o mol2Result.txt

It will generate the file named “mol2Result.txt” which contains PAINS information in bulk.

Note: recommend the SMILES format for PAINS calculation.

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial.



References
1    Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46, 3-26 (2001).
2    Dahlin, J. L. et al. PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS. J Med Chem 58, 2091-2113 (2015).

Tutorial of assessing vinardo score of ligands based on the grid file of SARS-CoV-2 Mpro by MolAICal

1. Introduction
In this tutorial, the SARS-CoV-2 Mpro which leads to the rapid spread of coronavirus disease 2019 (COVID-19) throughout the world is selected as the example target. The vinardo score of ligands is calculated based on the grid file of SARS-CoV-2 Mpro. This example can be generalized to other proteins.  Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/008-vinardoScore

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorial of the simple regression model of QSAR for drugs by MolAICal

1. Introduction
The quantitative structure-activity relationship (QSAR) models are regression or classification models used in drug design. In this tutorial, the simple regression model of QSAR is introduced based on the ligands of signal transducer and activator of transcription 3 (STAT3) protein which is considered as a potential drug target of cancer. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io
2) DRAGON: http://www.talete.mi.it/index.htm
Note: You can use any molecular descriptor calculator besides DRAGON software for this tutorial.

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/006-QSAR

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                        Figure 1


You can repeat this tutorial according to the content of pdf file.

Tutorials of the channel radii calculation of protein or nanotube by MolAICal

1. Introduction
In this tutorial, the radii calculations of nanotube and protein are introduced. This tutorial is divided into three parts: nanotube radii calculation, protein radii calculation and advance radii calculation in peptide channel. The advance radii calculation is based on the PDB and PSF files which are produced by the CHARMM force field. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io
2) VMD: https://www.ks.uiuc.edu/Research/vmd

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/005-radiiCal

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                Figure 1


You can repeat this tutorial according to the content of pdf file.

MM/GBSA tutorial by NAMD and MolAICal

1. Introduction
In this tutorial, the MolAICal (https://doi.org/10.1093/bib/bbaa161) is used to calculate the MM/GBSA between ligand N3 and SARS-CoV-2 Mpro based on molecular dynamical (MD) simulated results by NAMD. This tutorial is just a demo. To save running and storage space, only 25 frames of MD simulated trajectories of SARS-CoV-2 Mpro in complex with N3 are selected for this tutorial. 

2. Materials
2.1. Software requirement
1) MolAICal : https://molaical.github.io
2) NAMD: https://www.ks.uiuc.edu/Research/namd/

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/004-MMGBSA

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                             Figure 1

You can repeat this tutorial according to the content of pdf file.

Two steps for virtual screening in the pocket of SARS-CoV-2 Mpro

1. Introduction
In this tutorial, we introduce the fast way for drug virtual screening of SARS-CoV-2 Mpro based on the known database such as ZINC database. The premise for this tutorial is that you can deal with protein structure for Autodock Vina. If you are not familiar with it, you can learn from the previous tutorial https://github.com/MolAICal/documents/tree/master/tutorials/002-AIVS. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/003-VS
2) The file named “ligandSet.mol2” which contains 16 ligands obtained from ZINC database is chosen for demo. You can select your ligand database.
3) The protein file named “pro.pdbqt” that is PDBQT format structure of SARS-CoV-2 Mpro is used for molecular docking.

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                            Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorials of 3D drug design by AI and virtual screening method

1. Introduction
A new drug development may cost about 2.6 billion USD. However, about 90% of drugs are failure in the process of clinical trial and approval for marketing even though a lot of capital is used to drug development1. In this tutorial, the standard protocol of MolAICal (https://doi.org/10.1093/bib/bbaa161) is introduced for the drug design of SARS-CoV-2 Mpro by artificial intelligence and molecular docking method. It will help the pharmacologist, chemists and other scientists design rational drugs according to the three-dimensional active pocket of proteins.

2. Materials
2.1. Software requirement
1)    MolAICal (win64 or linux64): https://molaical.github.io
2)    UCSF Chimera: https://www.cgl.ucsf.edu/chimera/
3)    MGLTools: https://ccsb.scripps.edu/mgltools/downloads/
4)    Python: https://www.python.org/
5)    Pymol: http://www.lfd.uci.edu/~gohlke/pythonlibs

It is easily to install the first four software. They can be easily installed by following step tips. For pymol install, it needs modules numpy, pmw, pymol_launcher and pymol. The numpy, pmw, pymol_launcher and pymol should choose the same version and correspond to your installed version of Python in your operating system. They can be downloaded from the below website:
 
https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
https://www.lfd.uci.edu/~gohlke/pythonlibs/#pymol-open-source
 
Then install Pymol by following command:
#> pip install --no-index --find-links="%CD%" pymol_launcher

The Pymol named “pymol.exe” will be installed in the directory “Scripts” in your installed directory of Python. You can make a shortcut on your desktop of operating system.
Make sure all software is installed rightly.

2.2. Example files
All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/002-AIVS

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                             Figure 1

You can repeat this tutorial according to the content of pdf file.

Tutorials of 3D drug design by AI and de novo method

1. Introduction

In this tutorial, the standard protocol of MolAICal (https://doi.org/10.1093/bib/bbaa161) is introduced for the drug design of glucagon receptor (GCGR) by artificial intelligence and de novo method. It will help the pharmacologist, chemists and other scientists design rational drugs according to three-dimensional active pocket of proteins.


2. Materials
2.1. Software requirement
1)  MolAICal: https://molaical.github.io
2)  UCSF Chimera: https://www.cgl.ucsf.edu/chimera/

2.2. Protocol files
All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/001-AIGrow


For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about this tutorial (see Figure 1):

                                                Figure 1



You can repeat this tutorial according to the content of pdf file.

Quick start for de novo drug design of COVID-19 Mpro by MolAICal

Introduction

SARS-CoV-2 caused the rapid spread of coronavirus disease 2019 (COVID-19) throughout the world. In this tutorial, the SARS-CoV-2 main protease (Mpro) which plays an important role on the replication of coronavirus is selected as the example target. The crystal structures of SARS-CoV-2 Mpro have been reported (PDB ID: 6LU7, 6Y2F, etc) [1,2]. Here, the built structure of SARS-CoV-2 Mpro provided by the group of Prof. Zihe Rao is selected as our quick start example (see Figure 1). Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.
   
Figure 1. The structure of SARS-CoV-2 Mpro

 

Materials

1. Software requirement
1) MolAICal: https://molaical.github.io
2) UCSF Chimera: https://www.cgl.ucsf.edu/chimera/
Make sure every software is installed rightly.

2. Example files
All the necessary tutorial files are downloaded from: QuickStart

 

Procedure

1. Open “InputParFile.dat” file, it needs to modify four parameters for this tutorial:
------------------------------------------------------------------------------------------
receptorPDB                          mproNolig.pdb
startFragFile                          startFrag.mol2
centerPoints                          -10.733 12.416 68.829
boxLengthXYZ                       30.0 30.0 30.0
-------------------------------------------------------------------------------------------
The “receptorPDB” is set to the PDB format structure of SARS-CoV-2 Mpro­ without ligand. The “startFragFile” is set to the initial fragment. The “centerPoints” and “boxLengthXYZ” represent the box center and length, respectively.

2. Download and open tutorial folder “000-quickStart”, then run command as below:

#> molaical.exe -denovo grow -i InputParFile.dat

 

Results

The results are stored in the folder named “001-AIGrow/results”. The “AstatisticsFile.dat” records the information of designed ligands that contain ID, Name, Cluster, Affinity, Formula, InChIKey. The results are just a simple demo example that does not contain complete running results. The complete results can be obtained by finishing this task.
 
Open crystal ligand N3 named “ligand.mol2” of SARS-CoV-2 Mpro and generated ligand named “lig_11.mol2” (see Figure 2):


Figure 2. The results of generated ligand (white) and inhibitor N3 (red) of SARS-CoV-2 Mpro.

The detailed tutorial can be referred in the first tutorial of “1. Drug design tutorials of MolAICal” in https://molaical.github.io/

References

[1]  Jin, Z. et al. Structure of Mpro from COVID-19 virus and discovery of its inhibitors. Nature (2020).
[2]  Zhang, L. et al. Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved alpha-ketoamide inhibitors. Science (2020).

Tutorial of potential of mean force by MolAICal

1. Introduction
 
The potential of mean force (PMF) can be used to calculate the free energy landscape with principal components. The PMF along the coordinate is computed from the average distribution function (see below equation).

∆G=-kB*T*lnρ(x,y)

Where T and kB is the temperature and Boltzmann constant, respectively. The x and y represent two principal components. In this tutorial, the molecular dynamics (MD) simulated results of glucagon receptor (GCGR) are selected for this example (Front Chem. 2019 Dec 17;7:851.) [1]. Here, MolAICal (https://doi.org/10.1093/bib/bbaa161) is employed for this tutorial.

2. Materials
2.1. Software requirement
1) MolAICal: https://molaical.github.io

2.2. Example files
1) All the necessary tutorial files are downloaded from:
https://github.com/MolAICal/tutorials/tree/master/007-PMF

For more detailed procedures, please go to https://molaical.github.io, and click the section named "Tutorials" in the left part of webpage. Once "Tutorials" open, you can freely find pdf file about PMF tutorial (see Figure 1):

                                                         Figure 1

You can repeat this tutorial according to the content of pdf file.

References
1    Bai, Q. et al. Conformation Transition of Intracellular Part of Glucagon Receptor in Complex With Agonist Glucagon by Conventional and Accelerated Molecular Dynamics Simulations. Front Chem 7, 851 (2019).

https://doi.org/10.3389/fchem.2019.00851