CHAPTER 5 IN SILICO MOLECULAR DOCKING STUDIES

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CHAPTER 5

IN SILICO MOLECULAR DOCKING STUDIES

5.1 COMPUTATIONAL TOOLS

A series of computational tools overcomes the limitations of

experimental techniques in studying enzymes, assisting to predict the novel possible

interactions among the pollutant and oxidizing enzyme and assists us in gaining a

better understanding of the fate of these compounds in the environment. Uncovering

the catalytic activities among bio-catalysis/enzymes is most studied among bio-

molecules (Ramanathan et al., 2009). Molecular docking is a computational method

helps in predicting the bound conformation of a protein to another ligand. Docking

algorithms targets in finding best orientation of these two molecules such that they

have the minimum energy as scored by a predefined scoring function (Atilgan and

Hu 2011). Successful docking algorithm relies on predicting the correct placement

of ligands/ small molecules within the binding pocket of the target receptor protein.

Protein docking is a method that predicts the bound conformation of one protein to

another protein or a ligand. A docking algorithm aims to find the best orientation of

these two molecules such that they have the minimum binding energy as scored by a

predefined scoring function. There are two key components in a docking algorithm:

a good scoring function with high selectivity and efficiency that distinguishes

between correctly or incorrectly docked structures and a search algorithm that can

efficiently do global minimization of the scoring function.

Most extensively used search algorithms used in docking analysis are

based on Monte Carlo, Genetic Algorithm, Fragment-based and Molecular

Dynamics (Mohan et al., 2005). Molecular docking software ranks the interactions

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of chemical compounds with the receptor proteins by scoring functions, calculates

the free energy of binding between a ligand and a receptor, which is based on the

estimates of the total energy of intermolecular forces of attraction including Van der

Waals, hydrogen bonding, electrostatic, and hydrophobic. Ligands are scored or

ranked based on Gibbs free energy ( G), lower values correspond to more favorable

ligand binding, while higher DG values are less favorable (Jacob et al., 2012).

Table 5.1 represents the application of molecular docking software’s in the field of

science. AutoDock programs are widely-used docking programs developed at the

Scripps Research Institute, released under open source licenses (GNU General

Public License and Apache Open Source License) (Chang et al., 2010).

Table 5.1 Application of various molecular docking software’s in field of science

S.No. Docking Software Application Reference 1. PatchDock Drug discovery Shanthi and Ramanathan 2013 2. AutoDock Bioremediation Sridhar et al., 2013

3. AutoDock, iGEMDOCK, SwissDock Bioremediation Chaudary et al., 2013

4. GEMDOCK Parallel Screening Strategy Hsu et al., 2013 5. AutoDock Drug discovery Kothandan et al., 2012 6. Molegro Virtual Docker Drug discovery Gupta Udatha et al., 2012 7. AutoDock 4.0 Drug discovery Ganatra et al., 2012 8. MolDock Drug discovery Morya et al., 2012 9. Surflex-Dock GeomX Drug discovery Raghavendra et al., 2012 10. ArgusLab Drug discovery Sanghani et al., 2012 11. AutoDock Drug discovery Suvannang et al., 2011 12. GOLD Drug discovery Klepsch et al., 2011 13. GOLD Bioremediation Prasad et al., 2011 14. PARADOCKS Virtual Ligand Screening Pippel et al., 2011 15. AutoDock Vina Virtual Ligand Screening Sharma et al., 2011 16. FlexX (FlexiDock) Drug discovery Rahim et al., 2010 17. AutoDock Drug discovery Archana et al., 2010 18. ICM Dock Drug discovery Katritch et al., 2010 19. PatchDock Bioremediation Ramanathan et al., 2009 20. CDOCKER Bioremediation Kim et al., 2008 21. AutoDock Drug discovery Gowthaman et al., 2008 22. GOLD Drug discovery Kirtay et al., 2007 23. AutoDock Drug discovery Våbenø et al., 2006 24. AutoDock Screening Hetényi and Der Spoel 2002 25. ICM docking program Drug discovery Totrov and Abagyan 1997

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Autodock accurately predicts by grid based energy evaluation and

efficient search algorithm, using enhanced scoring function based on the principles

of Quantitative Structure-Activity Relationship (QSAR) to score protein-ligand

complex (Morris et al., 2009). Autodock comprises two main programs: AutoDock

and AutoGrid. AutoDock performs performs the actual docking of the ligand to a

pre-calculated grids describing target protein. AutoGrid, which is run prior to

AutoDock, pre-calculating grid maps of interaction energies between macromolecule,

such as protein, and various atom types, such as aliphatic carbons or hydrogen-

bonding oxygen atoms. Autodock ranks the docked conformation by calculating a

binding energy and sorting the results from lowest to highest energy (Holt et al.,

2008). AutoDock calculation is essentially a two-step process in which first the

interactions between atom types in the ligand and the target structure are pre-

calculated in a grid surrounding the binding region, followed by grid interaction

energies are used as look-up table to speed ligand energy evaluations (Goodsell and

Oslon 1990). Three search methods are employed by AutoDock to search and

predict efficient protein-ligand interaction in the space: a Genetic Algorithm (GA); a

Local Search (LS); and a novel, adaptive global local search method based on

Lamarckian genetics, the Lamarckian Genetic Algorithm (LGA) (Morris et al.,

1998). Local search methodology is an adaptive, performs torsional space search,

does not require prior information about local energy landscapes (Sollis and Wet,

1981). The hybrid of the GA method with the adaptive LS method together form the

so-called Lamarckian genetic algorithm LGA., which has enhanced performance

relative to simulated annealing and GA alone.

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5.2 GENETIC ALGORITHM (GA)

Genetic Algorithm (GA) is a population-based search technique used to

find appropriate solutions to optimization and search problems. GAs is based on

natural genetics and biological evolution, where each state variable corresponds to a

gene (Holland 1975). The ligand’s state corresponds to the genotype, whereas its

atomic coordinates correspond to the phenotype. In molecular docking, the fitness is

the total interaction energy of the ligand with the protein, and is evaluated using the

energy function. Random pairs of individuals are mated using a process of

crossover, in which new individuals inherit genes from either parent. In addition,

some offspring undergo random mutation, in which one gene changes by a random

amount. Selection of the offspring of the current generation occurs based on the

individual’s fitness: thus, solutions better suited to their environment reproduce,

whereas poorer suited ones die (Morris et al., 1998).

5.3 LAMARCKIAN GENETIC ALGORITHM (LGA)

LGA in Autodock uses Solis-Wets local search after each generation of

genetic algorithm search for energy minimization, where the output is used to update

the fitness value and its representation associated with an individual (Morris et al.,

1998; Atilgan and Hu 2011). The LGA is faster than both simulated annealing and

the standard genetic algorithm, and it allows the docking of ligands with more

degrees of freedom. LGA predicts the bound conformations of flexible ligands to

macromlecular targets applying genetic models, in which environmental adaptations

of an individual’s phenotype are reverse, transcribed into its genotype and become

heritable traits (Morris et al., 1998). To conclude, Lamarckian genetic algorithm is

the most efficient, reliable, and methodology in studying protein-ligand interactions.

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5.4 STIMULATED ANNEALING (SA)

Stimulated annealing is based on statistical mechanisms, widely applied

in the areas of genetic algorithms, molecular optimization using classical or semi-

empirical methods, geophysical problems, material science, traveling salesman

problem, numerical data fitting and in structural proteomics such as peptides

(Goodsell and Olson, 1990; Morris et al., 1998; Lee et al., 2004; Da Rocha Pita et

al., 2008).

5.5 SCORING AND MOLECULAR REPRESENTATIONS OF

DOCKING

There are three basic representations of the receptor: atomic, surface and

grid. Among these, atomic representation is generally only used in conjunction with

a potential energy function13 and often only during final ranking procedures

(Halperin et al., 2002; Kitchen et al., 2004). Prediction and identification of suitable

scoring schemes by docking methods for accurate protein-ligand binding affinities,

remains a considerable challenge (Kirtay 2007). The scoring function must

accurately measure both intramolecular conformational strain energy and

intermolecular interaction energy. Docking software’s are associated with scoring

functions, computing free energy associated with protein-ligand interactions

(Docking score), rank the ligands in a virtual environment. Scoring methods range

from estimating the binding of ligand by simple shape and electrostatic

complementarities to the estimation of free energy of protein and ligand complex.

Main potential applications of scoring functions in molecular docking are

determination of the binding mode and site of a ligand on a protein, predict the

absolute binding affinity between protein and ligand, and to identify the potential

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drug hits/leads for a given protein target by searching a large ligand database

(Shoichet et al., 2002; Seifert et al., 2007; Rajamani and Good, 2007, Huang et al.,

2010)

Docking algorithms uses an empirical equation to model the free energy

of binding, adding entropic terms to the molecular mechanics (Equation (5.1))

G= Gvdw Ghbond Gelec Gconform Gtor Gtor (5.1)

where the first four terms are the typical molecular mechanics terms for dispersion,

repulsion, hydrogen bonding, electrostatics, and deviations from covalent geometry,

respectively; Gtor models the restriction of internal rotors and global rotation and

translation; and Gsol models desolvation upon binding and the hydrophobic effect.

Wide ranges of scoring functions are available to calculate the binding

between the protein and virtual ligand (Table 5.2). Currently three main types of

existing scoring functions are applied: Force field-based, empirical scoring functions

and knowledge based scoring functions as represented in Figure 5.1.

Figure 5.1 Classification of scoring functions

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Table 5.2 Types of scoring functions

Type Scoring function

Force field-based

DOCK, DOCK 3.5 (PB/SA), DOCK/GBSA(SDOCK), AutoDock, GOLD, SYBYL/D-Score, SYBYL/G-Score

Empirical-force-field

FlexX, Glide, ICM,LUDI, PLP, ChemScore, SCORE, X-Score, Surflex, SYBYL/F-Score, LigScore

Knowledge-based ITScore, PMF, DrugScore, DFIRE, BLEEP, GOLD/ASP, KScore

5.6 MATERIALS AND METHODS

5.6.1 Data Structures

Nine synthetic textile and non-textile dye structures such as Acid Blue

113(AB113), Orange G (OG), Acid Blue 9 (AB9), Direct Blue 14 (DB14), Reactive

Blue 19 (RB19), Reactive Orange 122 (RO122), Reactive Blue RGB (RGB),

Reactive Black B (BB) and Acid Red 88 (AR88) was selected based on their wide

application in various potential applications in manufacturing industries.

5.6.2 Ligand and Protein Preparation

Eight target reference proteins for our study was downloaded from

Protein Data Bank (PDB). Input file (target receptor protein) was generated by

removing water molecules, ions, ligands and subunits from the original structure

file. Kollman charges and polar hydrogen atoms are added into the receptor PDB

file for the preparation of receptor protein in docking simulation. The dye structures

were downloaded from PubChem in SDF format and converted in to standard MOL

file format using ChemSketch 12.01 (Freeware version, ACD labs). Energy

minimization of the ligands was performed using Open Babel software by steepest

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descent using universal force fields and then converted to PDBQT format

(Geldenhuys et al., 2006; O’Byle et al., 2011).

5.6.3 Molecular Docking

Molecular docking analysis was performed using Autodock module

available in PyRx Version 0.8 software (Morris et al., 2009; Wolf 2009). Blind

docking and active site docking was performed to study the insights of molecular

interaction among ligand and the target receptor protein. Blind docking was

performed against laccase from Rigidoporus lignosus (PDB ID: IV10) structure

from protein data bank. Docking grid size was increased to accommodate the entire

protein inside the grid with dimensions 53, 55 and 50 A° (X, Y and Z). Genetic

Algorithm (GA) was used for screening for the best possible conformers among

blind docking. During molecular docking, a maximum of 50 conformers were

considered for each compound to predict the best conformers in Genetic Algorithm.

The population size was set to 150 and the individuals were initialized randomly.

Maximum number of energy evaluation was set to maximum of 2500000 energy

evolutions, maximum number of top individual that automatically survived to 1,

with a mutation rate of 0.02 and a crossover rate of 0.80. The pose with the lowest

score was chosen as representative for each cluster. Later, results were analyzed

with the help of Autodock tools 1.4.5. The interactions between the ligand and the

target are given in figures

5.7 RESULTS AND DISCUSSION

Latest advancement in computational techniques and in parallel

hardware support have enabled in silico methods to speedup new target selection,

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identification, prediction and optimization of lead compounds in all forms of bio-

prospecting process (Talele et al., 2010).High throughput Virtual Screening (HTVS)

programs such as PyRx with graphical user interfaces (GUIs) that use either DOCK

or AutoDock for the prediction of receptor-ligand interactions is useful for ligand

comparison studies because it offers well integrated storage and visualization of

HTVS results that facilitate binding analysis (Jacob et al., 2012).

AutoDock 4.0 module (present in PyRx 0.8 Python prescription

0.8 package) was utilized for molecular docking studies. The correct pose (based on

the energy) without prior knowledge of the binding site (Morris et al., 1998; Hetényi

and Der Spoel 2002). Table 5.1 represents binding number of hydrogen bonds

formed, distance among the active copper atoms (T1 and T2/T3 site), and interacted

amino acids. Figure 5.2 represents structures of nine representative prototypical dyes

under investigation in our study. Seven dyes reported negative binding energy, while

two dyes (BB and RGB) reported positive binding energy. The order of negative

binding energies are OG>AB9>RO122>RB19>AB113>AR88>DB14. Figure 5.3

represents the molecular crystal structure of laccase from Rigidoporus sp., active site

of molecular crystal structure of laccase from Rigidoporus sp. After molecular

docking analysis, the docking log file (.dlg) and the macromolecular structure

(.PDBQT format) was submitted into AutoDock 4.2, to visualize the molecular

interactions among the ligand and the receptor. Figure 5.4 and Figure 5.5 represents

the molecular docking of prototypical textile dyes -AB113, RB19 and RO122 with

receptor protein (PDB ID: IV10), docking analysis of the target receptor (PDB ID:

IV10) with AR88, RGB and RB.

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Figure 5.2 Structure of prototypical textile dyes

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Table 5.3 In silico and in vitro analysis of crude enzyme mediated dye decolorization

S. No. Dye Binding

Energy RMS No. of

H Bonds

Amino Acid

Copper Ion Sites (values are in Å)

CU1500 CU1501 CU1502 CU1503

1. Acid Blue 113 (AB113) -5.37 81.81 2 ALA1 17.642 21.133 18.923 30.071

SER194 28.923 33.986 31.744 35.98

2. Reactive Blue 19 (RB19) -5.33 91.26 3

ARG272 29.286 32.593 33.093 26.275 ARG272 28.176 31.388 32.052 25.36 THR276 31.093 34.488 34.745 27.705

3. Reactive Orange 122 (RO122) -5.57 88.56 2 ALA1 17.642 21.133 18.923 30.071

THR30 19.911 23.814 22.923 29.104 4. Reactive Black B 1.99 106.36 1 ARG336 22.131 20.715 23.638 9.22

5. Reactive Blue RGB (Blue RGB) 2.04 91.87 2 ARG336 22.131 20.715 23.638 9.22

ARG336 19.933 18.584 21.542 7.246

6. Acid Red 88 (AR88) -4.87 82.26 1 ALA1 17.642 21.133 18.923 30.071

7. Orange G (OG) -6.97 83.43 1 ALA33 12.468 16.211 15.261 23.012

8. Acid Blue 9 (AB9) -6.31 86.6 2 ALA1 17.642 21.133 18.923 30.071

9. Direct Blue 14 (DB14) -1.07 98.76 2 LYS 165 21.954 23.681 25.705 15.369

ASN494 31.912 29.954 32.622 35.57

Figure 5.3 Molecular crystal structure of Laccase from Rigidoporus sp. (PDB: IV10)

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Figure 5.4 Molecular docking of prototypical textile dyes (AB113, RB19 and RO122) with laccase from Rigidoporus sp.

Figure 5.5 Molecular docking of prototypical textile dyes (AR88, RGB and RB) with laccase from Rigidoporus sp.

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Figure 5.6 Molecular docking of prototypical textile dyes (OG, AB9 and DB14) with laccase from Rigidoporus sp.

Figure 5.6 represents the docking analysis of OG, AB9 and DB14 with

laccase (PDB ID: IV10). Distance between the active site and the ligand interacting

amino acid near the T1, T2/T3 sites play a major role in electron shuttling during

oxidation-reduction process.

Table 5.2 represents the results performed after blind docking. Table 5.2

provides valuable information relating the distance among interacted amino acids

with the copper ions in the mononuclear sites and can be correlated, in part, with

decolorization potential. During the in silico docking process, conformers were

ranked according to their estimated free energy of binding. The best docked

solutions were based on the energy scores, as docking program and scoring function

are good at eliminating compounds that do not fit the active site well

electrostatically or sterically (Kroemer et al., 2007; Huang et al., 2010). A lower

binding free energy indicates a more stable protein-ligand system and a higher

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affinity between protein and ligand. A combination of pose identification and

scoring algorithms constitute the foundation of docking engines, including DOCK

and AutoDock (Kuntz et al., 1982; Goodsell and Olson 1990; Morris et al., 1998;

Jacob et al., 2012).

The T1 site of the enzyme is the primary acceptor of electrons from

reducing substrates, and then they transfer electrons onto the three nuclear T2/T3

sites, where molecular oxygen is activated and reduced to water (Solomon et al.,

1996; Santhanam et al., 2011). Laccases can directly oxidize only compounds with

ionization potential not higher or slightly higher than the redox potential of the T1

copper site (Morozova et al., 2007).

Moreover, the catalytic efficiency (kcat/KM) for some reducing substrates

depends on the redox potential of the T1 copper (Xu et al., 1996, 2000). As

mentioned above, the mononuclear copper site (Cu1503) plays a pivotal role in

oxido-reduction. Barring exceptions (AB9), as the distance between the

mononuclear copper site (Cu1503) and the amino acids becomes less than 25 Å, the

percentage of decolorization falls below 20 %. Structural hindrance and delayed

electron shuttling resulting in low decolorization might be the possible mechanism

for the observed low decolorization of AB9. However, these findings need to be

verified /explored further.

Dye decolorization increases when the distance between the Cu1503 site

and the amino acids is greater than 25 Å. The hypothetical mechanism might be

larger space for interaction of the textile dye with the amino acid in the reference

protein (PDB: IV10), resulting in an efficient shuttling of electrons, stabilizing the

transition state and thereby increasing decolorization (Sridhar et al., 2013). T1 site

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acts as a catalytic binding site for redox reactions, when the dyes (Ligands)

interacted amino acid is >25 A° away from the T1 site, provides a conducive

environment for efficient interaction with the amino acid, thereby facilitating

efficient electron shuttling during redox reactions. On contrary, if the T1 site is close

to the amino acid of interest, isoform and steric factors influence low decolorization

percentage might be due to competition/delay in electron shuttle from the active site

to the textile dye and vice versa. RGB and BB are interacting closer to T1 site,

resulting in low decolorization, which might be due to poor electron shuttling and

stabilization of the transition state. OG resulting in increase in the absorbance

leading to polymerization reactions may possibly be due to the enhanced laccase

action on the dye. All other dyes in our investigation are found to follow the above-

mentioned mechanism (Zille et al., 2005).

5.8 CONCLUSION

In our study, we incorporated molecular docking analysis to study the

interaction among nine prototypical dyes and the target receptor protein, to study the

insights of interaction, hydrogen bond formation, interacting amino acids, , distance

between the interacting amino acid of the receptor with the active site and interms of

binding energy. Molecular docking analysis was performed of the nine dyes with the

receptor molecule using laccase from Rigidoporus lignosus (PDB ID: 1V10) was

performed using Autodock module available in PyRx Version 0.8 software. Seven

dyes reported negative binding energy, while two dyes, BB and RGB reported

positive binding energy. The order of negative binding energies are OG>AB9>

RO122> RB19> AB113> AR88 > DB14. Binding energy, hydrogen bonds and

distance between the interacting amino acid present in the protein with the dye and

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the active site was used to explain and navigate the insights and mechanisms

involved in laccase mediated decolorization. Inspite of success, in-silico tools are at

the backstep, because the predictions made by algorithm are only successful in

exactly mimicking the natural environment. So more validation is required in terms

of binding sites, transition states and the nature of oxidation-reduction reactions

(involving the electron shuttle) in order to bettercomprehend the data and validate

the mechanism, experimental potentially paving the way for algorithms that taken

into account the complexities in laccase-mediated textile dye effluent bio-

remediation processes.