INTRODUCTION:

Docking is used to predict the binding site of small molecule drug to their protein targets in order to predict the affinity and action of small molecule. Therefore docking is important in rational drug design. Docking is a method which involves prediction of selected orientation of one molecule to another when they bound to each other to form stable complex. Information of selected orientation is used to determine the strength and binding affinity between two molecules by using scoring functions. The associations between biologically related molecules like carbohydrates, nucleic acids, proteins, and lipids play an important role in signal transduction Hence, docking is essential for determination of both strength and type of signal produced.

· Molecular Docking:

Molecular docking is process that involves interaction of molecules with receptor. It is a natural process that occurs in cell within seconds when attach to each other to form stable complex.

· Molecular Modelling:

Molecular modeling is technique for deriving, representing the structures and reactions of molecules, and those properties that are dependent on that three dimensioinal structures in molecular modelling¹.

· Types of Docking:

There are two types of docking:

1) Rigid Docking

2) Flexible Docking

1) Rigid Docking:

If we consider that, molecules are rigid then we are looking for transformation in 3D space of one of the molecules which brings it to an optimal fit with another molecule in form of scoring function. Confirmation of ligand may be generated in absence of receptor or in presence of receptor binding action. Virtual screening is applied to find out novel drug candidates from various chemical scaffolds by exploring databases^2-3.

2) Flexible Docking:

We assume molecule flexibility then in addition to transformation, our aim will be to find confirmations of receptor and ligand molecule.

· Molecular Docking Models:

1) The Lock and Key Theory:

Lock and key model was proposed by Emil Fischer. In this theory, a substrate fits into the active site of macromolecule, just like key fits into lock. The biological locks have unique stereochemical features that are needed to their function.

2) The Induced Fit Theory:

Daniel Koshland explained the Induced Fit Theory in 1958. According to this theory, both ligand and target adapt to each other by small conformational changes, until optimal fit is achieved.

3) The Conformation Ensemble Model:

It has been observed that protein can undergo much larger conformational changes. This model describes the proteins as pre-existing ensemble of conformational states. Plasticity of protein allows it to move from one state to another state.

· Molecular Docking Approaches:

There are number of approaches exist for docking like:

1. Monte Carlo Approach:

It Synthesize an initial configuration of ligand in an active site which consist of random conformation, translation and rotation. Firstly, it scores initial configuration. Then it generates new configuration and also score it. The metropolis criterion is used to check if new configuration is retained.

2. Fragment Based Method:

In this method, ligand get separated into protons or fragments, then these fragments undergoes docking and then finally these fragments linked top each other.

3. Distance Geometry:

So many types of structural information is expressed as intramolecular or intermolecular distances. This geometry helps to assembling the distance and 3D structures can be calculated.

4. Matching Approach:

In this approach, ligand atom is placed at best position in site, and it generates the ligand receptor configuration.

5. Ligand Fit Approach:

This approach helps in docking small molecule ligand into protein active sites for considering shape between ligand and protein active sites.

6. Point Complimentarity Approach:

This involves evaluating a shape and/or chemical complimentarity between interacting molecules.

7. Blind Docking:

It was identified for detection of all possible binding sites and modes of peptide ligand by scanning the entire surface of protein targets.

8. Inverse Docking:

In this use of computer method for finding toxicity and side effects protein targets of small molecule. Knowledge of these targets combined with pharmacokinetic profile can facilitate the assessment of potential toxicities side effects of drug. One of these protocol is selected for docking studies of particular ligand

Basic Requirement for Molecular Docking:

The setup for ligand docking approach requires components a target protein structure, the molecule of interest or database containing existing O components a target protein structure, the molecule virtual compounds for docking process and a computational framework that allows the implementation of desired docking and scoring procedures. Most docking algorithms assume protein to be rigid, the ligand is mostly regarded as flexible. Beside the conformational degree of freedom the binding pose in protein binding pocket must be taken in to consideration. Docking can be done by rigid molecules or fragments in to protein active sites using different approaches like the clique search, geometric hashing, pose clustering. The following are the various algorithms applied for docking analysis such as Point complementary, Monte Carlo, Fragment-based, Genetic algorithms, Systematic searches, Distance geometry etc^4-5.

Ligand Representation:

Typically, structure most likely to be dominant further adjusted by adding or removing hydrogens provided approximate pKa values. The most important is to make sure that accurate atom typing occurs.

Receptor Representation:

Quality of receptor structure employed plays important role in determining success of docking calculations. It means, higher the resolution of employed crystal structure better will be observed docking results. Recent review for accuracy, limitations and pitfalls of structure refinement protocols of protein ligand complexes in general provided a critical assessment of available structure.

Mechanism of Docking:

To study a docking screen, the first need is a structure of the protein of interest. Usually the structure has been determined using a biophysical method such as x-ray crystallography, or less often, NMR spectroscopy. This protein structure and a database of ligands serve as inputs to a docking program. The success of a docking program depends on two factors such as search algorithm and scoring function. Searching Conformational Space The search space involves all possible orientations and conformations of the protein paired with ligand. With present computing resources, it is not possible to exhaustively explore the search space this would enumerating all possible distortions of each molecule and all possible rotational and translational orientations of the ligand relative to the protein at a given level of granularity. Most docking programs in use account for flexible ligand, and several are attempting to model a flexible protein receptor.

Applications of Molecular Docking:

Applications of molecular docking in drug development: Docking is mostly used in the field of Drug design. Most drugs are small organic molecules and docking may be applied to: Hit identification: Docking combined with a scoring function can be used to quickly screen large databases of potential drugs in silco to identify molecules that are likely to bind to protein target of interest Lead optimization: Docking can be used to determine in where and in which relative sorientation a ligand binds to a protein. This information may in turn be used to design more potent and selective analogs. Bioremedation is Protein ligand docking can also be used to predict pollutants that can be degraded by enzymes.

· Identification of target site.

· Selection of best drug (based on scoring function).

· Enzymes and its mechanism.

· Protein interactions.

· Virtual Screening of compounds.

Application of Molecular Modelling in Modern Drug Development:

· It is used to screening for the side effects that can be caused by interactions with other proteins, like proteases, Cytochrome P450 and others can be done.

· It is also possible to check the specificity of the potential drug against homologous proteins through docking.

· Docking is also a widely used tool in predicting protein-protein interactions.

· Knowledge of the molecular associations aids in understanding a variety of pathways taking place in the living and in revealing of the possible pharmacological targets.

Evaluation of Structures:

1. NMR

Figure 1: Evaluation of Structures by NMR

2. X-Ray Crystallography:

Figure 2: Evaluation of Structures by X-Ray Chromatography

3. Homology Modeling:

Figure 3: Evaluation of Structures by Homology Modeling

Figure 4: Docking Program on basis of search algorithms

Receptor Preparation:

· Dependent on docking program used

· Structure selection

· Site selection

· Often have to add hydrogens, some programs more sensitive to positions than other

· Remove/include waters, cofactors, metals.

· Pre-docking remember to consider missing residues or atoms.

Ligand Preparation:

· Generate isomers if chiral centers

· Calculate charges – Predict pKa‟s for each potential charged atom – Generate a structure for each charge combination for a given pH range (e.g., 5-9).

· Minimize structures – Generally using a molecular

· Mechanics forcefield.

Available Softwares for Docking:

· DOCK (1982, 2001)

· FleX (1996)

· Hammerhead (1996)

· Surflex (2003)

· SLIDE (2002)

· AutoDock (1990, 1998)

· ICM (1994)

· MCDock (1999)

· GOLD (1997)

· GemDock (2004)

· Glide (2004)

GOLD:

· Genetic Optimisation and Ligand Docking, uses multiple subpopulations of ligand

· Force-field based scoring function, involves three terms: hydrogen-bonding term, intermolecular dispersion potential, intra molecular potential

· 1% success in identifying experimental binding mode in 100 protein complexes

AUTODOCK:

· Grid for each atom type (e.g. C, H, O, N)

· Consists of 3D lattice of regularly spaced points, surrounding and centered on region of interest in the macromolecule

· Typical spacing is 0.375

· Probe atom placed at each

FLEX-X:

· Base fragment is picked and docked by using “pose-clustering” algorithm

· Clustering algorithm is implemented to transfer similar ligand transformations into active site Flexible fragments are added incrementally by using MIMUMBA and evaluated by using overlap function, followed by energy calculations untill the ligand is completely built

· Final evaluation through Böhm‟s scoring function that involves Hydrogen-bonds, ionic, aromatic and lipophilic terms.

Polypharmacology:

Scoring Function involves one or more ligands ranking against different proteins by their binding affinity (selectivity and specificity)^6-7. 1. To avoid potentially harmful side effects, the pharmaceutical industry focused on the development of highly selective drugs. However, the high attrition rates in the late stages of clinical trials due to a lack of therapeutic efficacy have moved modern drug design towards polypharmacology, which refers to the identification of ligands that hit a set of selected, therapeutic-relevant targets^8-7. In this context, molecular docking can provide valuable opportunities because it allows the identification of chemical scaffolds that efficiently and simultaneously bind to a pool of selected targets of interest. Indeed, several studies related to the use of docking for the design of novel multi-target ligands have already been reported^12-14. Moreover, its utility for de novo polypharmacology design has also been reviewed¹⁵. The design of multi-target ligands on rational grounds is challenging. Moreover, the selection of protein conformations to be used for docking can heavily affect the success of the design^16-17.

CONCLUSION:

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. It requires an efficient way to obtain and select reliable protein structures used for docking, which means structures that the ligand can fit in should be induced in the ensembles. Besides, computational cost is another limitation for this method.

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Received on 12.05.2022 Modified on 19.08.2022

Asian J. Pharm. Res. 2023; 13(3):191-195.

DOI: 10.52711/2231-5691.2023.00036