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Let’s Dock: A Smart Platform for Molecular Docking

PJZ_54_4_1577-1582

Let’s Dock: A Smart Platform for Molecular Docking

Yaruq Jabeen1, Nida Ansari1, Haroon Rasheed1, Muhammad Asif Rasheed1,*, Muhammad Awais2, Muhammad Ibrahim1, Sumaira Kanwal1, Aqsa Khalid3, Manzoor Ahmad Zahid4 and Farrukh Jamil1,*

1Department of Biosciences, COMSATS University, Sahiwal Campus, Punjab

2Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Sialkot, Sialkot

3National University of Sciences and Technology, H-12, Islamabad

4Department of Mathematics, COMSATS University, Sahiwal Campus, Punjab

ABSTRACT

In the past few years, several docking tools have been developed for molecular dockings such as LeDock, rDock, AutoDock Vina, AutoDock, UCSF DOCK, GOLD, Glide, Surflex-Dock, LigandFit and MOE-Dock. Among these, AutoDock Vina has been widely used by academia for molecular docking. This tool performs docking of a ligand into a protein by seven steps. For docking analysis of several ligands, AutoDock Vina is a time-consuming tool. In order to make AutoDock Vina more efficient and smart way, a platform has been developed in this study. It is designated “Let’s Dock”. By using this platform, we can perform a docking analysis of several ligands into a protein with AutoDock Vina by just submitting ligands and protein pdb files. Apart from thermodynamic data analysis this platform also calculates descriptors and Lipinski’s rule parameters (Rule of Five) of a ligand by a single click.


Article Information

Received 09 January 2021

Revised 03 March 2021

Accepted 18 March 2021

Available online 10 September 2021

(early access)

Published 07 April 2022

Authors’ Contribution

YJ, NA, HR, MAR and AK Performed the experiment. MA, MI, SK, MAZ and FJ reviewed the manuscript. FJ designed and supervised the study.

Key words

Molecular docking, AutoDock Vina, Virtual screening, Lipinski rule.

DOI: https://dx.doi.org/10.17582/journal.pjz/20210109170113

* Corresponding authors: farrukh@cuisahiwal.edu.pk;

farrukhccb@gmail.com; asif.resheed@cuisahiwal.edu.pk

0030-9923/2022/0004-1577 $ 9.00/0

Copyright 2022 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).



Introduction

Molecular docking is a technique which is widely used in pharmacological target identification by virtual screening of different available natural and organic compounds for a specific protein (Forli et al., 2016; Gupta et al., 2018). The docking analysis is based on the identification of different ligands conformations in the active site of the protein and the ranking of these conformations according to binding affinity (Meng et al., 2011). It provides us valuable thermodynamic data of protein-ligand interactions for suitable ligand identification against target protein (Alvarez-Garcia and Barril, 2014; Uehara and Tanaka, 2016).

Studies have shown that molecular docking can be classified into two classes. First is targeted docking in which the binding sites are known for a ligand. This way expressively increases the docking efficacy (Meng et al., 2011). Second is blind docking in which there is no information on binding sites available for receptor and ligand. So, a ligand is docked on the entire surface of a protein (Lee and Zhang, 2012). There are also many online servers like GRID (Goodford, 1985; Kastenholz et al., 2000), POCKET (Levitt and Banaszak, 1992), SurfNet (Glaser et al., 2006; Laskowski, 1995), PASS (Brady and Stouten, 2000) and MMC (Mezei, 2003) through which putative active sites can be identified in a protein 3D structure (Meng et al., 2011).

Over the last two decades, there are almost more than 60 different docking programs have been developed for academic and commercial use. Some important programs are GOLD (Jones et al., 1997), DOCK (Venkatachalam et al., 2003), LigandFit (Venkatachalam et al., 2003), MOE-Dock (Corbeil et al., 2012), AutoDock Vina (Trott and Olson, 2010), AutoDock (Österberg et al., 2002), LeDock (Zhao and Caflisch, 2013). These docking programs are tested with several different complexes and have been validated (Bodian et al., 1993; Debnath et al., 1999; Friesner et al., 2004; Jones et al., 1997; Österberg et al., 2002; Rarey et al., 1996; Shoichet et al., 1993; Trott and Olson, 2010; Venkatachalam et al., 2003). Analysis of top scored programs have shown that performance of the academic programs follows this order: LeDock (57.4%) > rDock (50.3%) - AutoDock Vina (49.0%) > AutoDock (PSO) (47.3%) > UCSF DOCK (44.0%) > AutoDock (LGA) (37.4%). The performance of the commercial programs follow this order: GOLD (59.8%) > Glide (XP) (57.8%) > Glide (SP) (53.8%) > Surflex-Dock (53.2%) > LigandFit (46.1%) > MOE-Dock (45.6%) (Pagadala et al., 2017). These results showed AutoDock Vina, GOLD, and MOE-Dock considered highest with best scores of docking (Wang et al., 2016).

For academic purpose, mostly used docking tool is AutoDock Vina which is an open-source tool and perform docking through seven number of steps (Gaillard, 2018; Helgren and Hagen, 2017; Topaz et al., 2019). These steps are: preparing ligand coordinate file, reading atomic coordinates, preparation of pdbqt file, preparing receptor coordinate file, preparing a configuration file for Vina, run Vina to start docking and visualization of results. This method interprets with single ligand and single protein molecule (Forli et al., 2016). In order to analyze several ligands with a protein, we need considerable time for repeating seven-step procedure for each pair of protein and ligand. To reduce the analysis time and human efforts, a docking platform is developed in this study. It is designated as “Let’s Dock”. Apart from thermodynamic data this platform also calculates descriptors and Lipinski’s rule parameters (Rule of Five) of a ligand by a single click.

Materials and methods

Standard inputs required for Let’s Dock are: co-ordinate file (.pdb) for protein, co-ordinate file (.sdf) for ligands and grid parameters. Grid parameters manually set by the user to provide ease, as every user didn’t prefer blind docking.

Integration and docking

Integration of MGL Tools and Auto Dock Vina has been implemented by using python subprocess module (Dale et al., 2011). A python script, consist of three sub pieces, has been written which automate Open Babel, MGL Tools and Auto Dock Vina. The first piece of the script runs MGL Tools in the background and commands it to take a protein input file and ligand input files to convert into pdbqt format, and place them into the user-defined folder. This script later runs Auto Dock Vina in the background to retrieve pdbqt files of ligand and protein from the folder and dock them. After docking, it will place results files i.e. conf file, log file and out file in the same folder.

Formation of best protein-ligand complex

The second piece of this script generates a protein-ligand complex file by taking the best model of ligand, having the lowest binding affinity, from out file (having ligand’s conformations model) and merge the best ligand model co-ordinates with protein co-ordinates. These all files are placed in a single folder which is named as same as of ligand file.

 

Table I.- Physical properties of the 14 ligands docked by Let’s Dock in NS3-NS2B. The ligands are arranged in ascending order of binding energies.

Ligands

Binding affinity (kcal/mol)

Molecular weight (Da) <= 500.0

Rotatable bonds <= 3.0

H-bond donor <= 5.0

H-bond acceptor <= 10.0

Log P

<= 5.0

1111153

-8.8

433.284

3.0

0.0

5.0

4.622

1111008

-8.5

471.313

5.0

0.0

6.0

4.382

42877561

-8.5

436.345

4.0

0.0

5.0

5.27

17021741

-8.5

431.292

5.0

2.0

4.0

4.387

17021740

-8.3

450.29

5.0

2.0

4.0

4.526

5126841

-8.2

478.3

4.0

0.0

6.0

4.525

4434656

-7.7

487.312

5.0

0.0

7.0

4.395

68956077

-7.7

407.27

2.0

0.0

5.0

3.769

4768896

-7.6

364.25

5.0

2.0

4.0

3.474

44624874

-7.4

379.256

4.0

3.0

1.0

4.294

67132806

-7.4

433.284

6.0

1.0

5.0

4.358

68956356

-7.2

442.723

2.0

0.0

5.0

4.422

67133299

-7.1

423.269

6.0

1.0

6.0

3.638

24631700

-6.5

314.243

5.0

2.0

4.0

2.239

 

 

Descriptor calculation

The third piece of script use built-in library PYDPI (Cao et al., 2013) to calculate descriptors for Rule of Five of ligands. Before calculation, a module runs Open Babel automatically in the background to convert the molecular file format (O’Boyle et al., 2011). Moreover, it determines five properties that cover the “Rule of Five” and highlight them green if the properties fall within the threshold values defined by Lipinski et al. (2001).

For a case study a pdb file NS3-NS2B protease of Zika virus and 14 ligands, PubChem IDs are shown in Table 1, has been submitted to Let’s Dock as an input. Grid parameters are set with dimension of 100×100×100 Å and center x = -0.254, y = 11.832 and z = -24.015. By executing, we obtain the list of descriptors which is shown in Table I.

Results and discussion

A platform has been developed to perform docking and to calculate Rule of Five from the protein pdb file and Ligand mol files. The input interface of Let’s Dock is illustrated in Figure 1. This figure shows different buttons that take inputs for the docking process. On click: Browse protein takes folder having protein of interest in pdb format, Browse ligands takes folder having single or multiple ligands of interest in mol format, Output folder takes the folder in which user want to store results, MGL Tools path takes the path of MGL Tools folder which will create when it is installed. MGL Tools used to convert input files into .pdbqt by adding different charges. Grid parameters take the set parameters against a defined pocket of the protein. Then, by clicking on execute button docking will start.

Docking and calculations

We submitted a zika virus NS3-NS2B protease and 14 ligands to Let’s Dock. Its results are generated showing the binding affinities of the ligands with the NS3-NS2B docking site in terms of kcal/mol and five parameters of Rule of Five as shown in Table I.

The first column shows the PubChem IDs of the ligands and the second column shows the binding affinity; thermal stability and strength of the interaction between the binding protein and ligand. The resultant binding affinities range from -8.8 to -6.5 Kcal/mol. This tool also arranges the result according to the ascending order of binding affinity. The first molecule with the lowest binding affinity of -8.8 kcal/mol is the most stable lead compound out of the given input. A previous study on the same protein also reported a score of -9.9 kcal/mol for the strongest interacting molecule (Fatima et al., 2018).

The next five columns (3-7) enlist the five parameters of Lipinski’s rule. This is based on observations that most orally active drugs are relatively small and lipophilic molecules (Lipinski et al., 2001). These rules are a set of guidelines for the structural properties of ligand molecules that have the ability of well absorbed after intake. The third column shows the molecular weight which according to Rule of Five should be less than or equal to 500 Da. An increase in molecular weight results in the formation of a large cavity in water to solubilize the ligand molecule and then solubility decreases. The fourth column shows the rotatable bonds, a single bond bound to a nonterminal heavy atom, which should be less than and equal to 3. The fifth column displays the number of hydrogen donors which should be less than or equal to 5. The sixth column displays the number of hydrogen acceptors which should be less than or equal to 10. An increasing number in hydrogen bonds decreases partitioning from the aqueous phase into lipid bilayer membrane for permeation by passive diffusion. The last column shows LogP, partition coefficient, which should be less than or equal to 5. Increasing LogP decreases aqueous solubility, which reduces absorption. Let’s Dock has colored green to these parameters which are in the allowed range (Table I). Due to this feature, the user can easily analyze which ligand is optimum. Analysis of the data showed that 1st, 8th and 12th ligand fulfill all the requirements.

 

Table II.- Comparison of binding affinities calculated by Let’s Dock and conventional method.

Ligands

Let's Dock

Conventional method

1111153

-8.8

-8.8

1111008

-8.5

-8.4

42877561

-8.5

-8.3

17021741

-8.5

-8.2

17021740

-8.3

-8.3

5126841

-8.2

-8

4434656

-7.7

-7.9

68956077

-7.7

-7.8

4768896

-7.6

-7.8

44624874

-7.4

-7.5

67132806

-7.4

-7.5

68956356

-7.2

-7.3

67133299

-7.1

-7

24631700

-6.5

-6.3

 

We validated the software working by comparing the docking results of the same 14 ligands docking on the same protein (zika virus NS3-NS2B protease) by using a conventional method involving each step to perform manually. A comparison of resultant binding affinities calculated by the conventional method of docking and Let’s Dock is given in Table II. Analysis of comparison showed that percentage difference ranges from -2.63 to 3.53 %. This difference is due to two reasons. First, our software prepare the protein and ligands files by default parameters using MGL Tools. Secondly, Vina is predicting software that is run by Let’s Dock for docking in back ground. Vina can perform analysis with a single ligand where Let’s Dock can perform multiprocessing with reliability.

Conclusion

In this study we have created a platform for several ligand docking in a protein that can perform docking analysis faster than conventional AutoDock Vina. Most importantly, it calculates binding energies and Rule of Five parameters of the ligand.

Statement of conflict of interest

The authors have declared no conflict of interests.

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