*4.1.2 Sampling algorithms*

Sampling plays the next crucial role in any molecular docking program. With a set therapeutic target, the sampling algorithm will generate a number of conformations (poses) of the small molecule within the docked site of the target. The knowledge of the docked site is considered either from experimental data or predicted with the aid of active site prediction software. As the speed and accuracy of molecular docking plays a role in large virtual screening research works, the area of developing and/or improving existing sampling algorithms have provided ample opportunities for computational scientists. The sampling algorithms can be categorized as: shape matching, systematic search algorithm and stochastic algorithm.

## i.Shape matching

One of the earliest methods designed was the shape matching algorithm for sampling. The criterion implemented in this algorithm is that the molecular surface of the small molecule needs to complement the molecular surface of the binding region of the target. The three translational and three rotational (six degree of freedom) of the small molecule led to spans many probable orientations. Thus, the goal of this algorithm is to place as smoothly and quickly the small molecule into the binding site based on shape complementarity. In this method, the conformation of the small molecule is usually fixed and therefore, this method along with

flexible-docking is usually preferred rather than only shape matching. DOCK [61], LigandFit [70] and Surflex [70] are few examples of docking programs where shape matching algorithm is used.

#### ii.Systematic Search

With the help of systematic search algorithm, the ligand can explore all the degrees of freedom and it can generate all probable conformations. Unlike in shape matching algorithm, the conformations of ligands are not fixed here. Systematic search technique can be categorized into three types: exhaustive search, fragmentation and conformational ensemble.

In exhaustive search method, all the rotatable bonds of the small molecules are scanned in a systematic manner. However, to avoid a huge combinatorial explosion & to make the docking procedure practical, the search space is limited by geometric constraints criterion. Glide docking program implements this method.

The fragmentation method as the name suggests implements the idea of fragmenting the ligands into smaller rigid fragments. The incremental construction is one such mode wherein one fragment is placed first in the binding site and other fragments were attached incrementally. FlexX [70] utilizes this algorithm.

In the conformational ensemble algorithm, small molecule flexibility is signified by rigidly docking an ensemble of pre-generated conformers of the small molecule. Next the binding modes were collected from different docking runs then binding energy values are used to rank them. FLOG [71] and MS-DOCK [70] implements this algorithm.

#### iii.Stochastic Search

In the stochastic search, the sampling of the small molecule conformations is carried out by making random changes at every step in both the rotational/ translation space and conformational space of the small molecule respectively. A probabilistic criterion is placed to either accept or reject the random change. Within stochastic search, there are four subtypes viz., Monte Carlo method, evolutionary algorithms (EA), Tabu search methods and swarm optimization (SO) methods. Genetic algorithm, one type of EA is implemented in AutoDock [63] and GOLD docking programs.

It is imperative to mention here that different docking programs/servers apply variety of algorithms in multi-phase wise in their docking pipeline.

#### **4.2 Application examples of molecular docking**

The molecular docking can be seen applied regularly in academic labs and pharmaceutical companies to find effective solutions and thwart deadly diseases [72]. The identification of hit molecules in the preliminary stage of drug discovery is today heavily relied upon high throughput screening. Moreover, the availability of small molecule databases such as PubChem, ZINC, MayBridge etc. along with the growth of experimental structures of targets (proteins, membrane proteins, protein-ligand complexes) have made the use of molecular docking to screen millions of compounds and made it possible to test only lead molecules.

G protein-coupled receptors (GPCR) are the attractive targets of drug design regimes because of their importance in cell signaling and functions. Kolb et al. have considered β2-adrenergic receptor, a GPCR found in the smooth muscle tissue to investigate the structure-based approach for ligand discovery. In their study, they have utilized DOCK molecular docking program to screen approx. 1 million

**45**

*Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

followed by experimental validations [75].

the receptor [73].

**5. Conclusion**

compounds from ZINC database. They were able to test experimentally the resultant 25 high ranked molecules from docking; of which 6 molecules showed binding affinity <4 μM. And the best compound showed 9 nM of inhibition constant against

Rajkhowa et al. have utilized the structure-based drug design (SBDD) method along with MD simulations to design inhibitors against malaria, one of the most devastating infectious diseases. They have considered 178 compounds similar to known anti-malarial imidazopyrazine from the PubChem database to carry out the work. The target of the inhibitor is the phosphatidylinositol-4-OH kinase which is a lipid kinase involved in the membrane ingestion process of the erythrocytic stage of the life cycle of the plasmodium and recognized as a drug target. AutoDock 4.2 has been utilized in their work. They have reported three potential inhibitors based on

Our group had worked in the direction of SBDD to tackle insulin resistance and type-2 diabetes (T2D). We have considered 142 anti-diabetic compounds spanning various categories of phytochemicals such as flavonoids, alkanoids, sulfonylurea and terpenes. The target of the study is A2A adenosine receptor which had been shown in reports that it can be utilized to counteract insulin resistance and adipocyte inflammation. Numerous computational tools were utilized to carry out the work such as druglikeness filtering, QSAR modeling, ADMET profiling to molecular docking. The different level of screenings led to 6 molecules which were docked with the help of two different molecular docking approaches viz. AutoDock and AutoDockFR to get optimal receptor-ligand conformations. From the 142 compounds finally we got one molecule "indirubin-3′-monoxime" which is then

In this era of high-performance computing technology, there is hardly any field

of science which is not touched upon by some amount of significant computational works. The potential of computing power is much reliant on advancement in hardware and algorithms. Substantial number of computational tools and techniques were developed and applied in the fascinating area of proteomics also. Mathematical models were devised in the form of FF parameters and implemented in various algorithms. Here, we have discussed the inevitable role of FF in protein structure prediction/modeling, conformational dynamics and their functional aspects along with the applications in virtual screening programs. As discussed in the chapter, a lot of programs with variety of FFs are available for structure prediction, MD simulations etc., but there is still a scope of further developments. For example, till now it is a challenge for accurately predicting protein structures of larger sizes or the protein sequences having low amount of similarity with sequences of known structures. Also, the existing software are in use for transmembrane protein structure prediction but it is an hour need to develop different program to model the trans-membrane segments. Although MD simulations were utilized for validating predicted structures of membrane proteins and/or for getting insights of their mechanism, challenge remains in the forms of FF as at times it is difficult to get the parameters for membrane proteins, lipids in which they were embedded, any bound coordinated metal ions in a single FF. The accuracy of models depends upon pH and dynamic charge environment instead of static electrostatic charges, and polarizable water models, requires further development and testing of polarizable force fields. The existing FF were designed with aid of experimental data for globular proteins and applied for studying IDPs whereas disordered

molecular docking, MD simulations and ADMET studies [74].

*Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

*Homology Molecular Modeling - Perspectives and Applications*

matching algorithm is used.

ii.Systematic Search

tion and conformational ensemble.

this algorithm.

docking programs.

iii.Stochastic Search

flexible-docking is usually preferred rather than only shape matching. DOCK [61], LigandFit [70] and Surflex [70] are few examples of docking programs where shape

With the help of systematic search algorithm, the ligand can explore all the degrees of freedom and it can generate all probable conformations. Unlike in shape matching algorithm, the conformations of ligands are not fixed here. Systematic search technique can be categorized into three types: exhaustive search, fragmenta-

In exhaustive search method, all the rotatable bonds of the small molecules are scanned in a systematic manner. However, to avoid a huge combinatorial explosion & to make the docking procedure practical, the search space is limited by geometric

The fragmentation method as the name suggests implements the idea of fragmenting the ligands into smaller rigid fragments. The incremental construction is one such mode wherein one fragment is placed first in the binding site and other fragments were attached incrementally. FlexX [70] utilizes this algorithm.

In the conformational ensemble algorithm, small molecule flexibility is signified by rigidly docking an ensemble of pre-generated conformers of the small molecule. Next the binding modes were collected from different docking runs then binding energy values are used to rank them. FLOG [71] and MS-DOCK [70] implements

In the stochastic search, the sampling of the small molecule conformations is carried out by making random changes at every step in both the rotational/ translation space and conformational space of the small molecule respectively. A probabilistic criterion is placed to either accept or reject the random change. Within stochastic search, there are four subtypes viz., Monte Carlo method, evolutionary algorithms (EA), Tabu search methods and swarm optimization (SO) methods. Genetic algorithm, one type of EA is implemented in AutoDock [63] and GOLD

It is imperative to mention here that different docking programs/servers apply

The molecular docking can be seen applied regularly in academic labs and pharmaceutical companies to find effective solutions and thwart deadly diseases [72]. The identification of hit molecules in the preliminary stage of drug discovery is today heavily relied upon high throughput screening. Moreover, the availability of small molecule databases such as PubChem, ZINC, MayBridge etc. along with the growth of experimental structures of targets (proteins, membrane proteins, protein-ligand complexes) have made the use of molecular docking to screen mil-

G protein-coupled receptors (GPCR) are the attractive targets of drug design regimes because of their importance in cell signaling and functions. Kolb et al. have considered β2-adrenergic receptor, a GPCR found in the smooth muscle tissue to investigate the structure-based approach for ligand discovery. In their study, they have utilized DOCK molecular docking program to screen approx. 1 million

variety of algorithms in multi-phase wise in their docking pipeline.

lions of compounds and made it possible to test only lead molecules.

**4.2 Application examples of molecular docking**

constraints criterion. Glide docking program implements this method.

**44**

compounds from ZINC database. They were able to test experimentally the resultant 25 high ranked molecules from docking; of which 6 molecules showed binding affinity <4 μM. And the best compound showed 9 nM of inhibition constant against the receptor [73].

Rajkhowa et al. have utilized the structure-based drug design (SBDD) method along with MD simulations to design inhibitors against malaria, one of the most devastating infectious diseases. They have considered 178 compounds similar to known anti-malarial imidazopyrazine from the PubChem database to carry out the work. The target of the inhibitor is the phosphatidylinositol-4-OH kinase which is a lipid kinase involved in the membrane ingestion process of the erythrocytic stage of the life cycle of the plasmodium and recognized as a drug target. AutoDock 4.2 has been utilized in their work. They have reported three potential inhibitors based on molecular docking, MD simulations and ADMET studies [74].

Our group had worked in the direction of SBDD to tackle insulin resistance and type-2 diabetes (T2D). We have considered 142 anti-diabetic compounds spanning various categories of phytochemicals such as flavonoids, alkanoids, sulfonylurea and terpenes. The target of the study is A2A adenosine receptor which had been shown in reports that it can be utilized to counteract insulin resistance and adipocyte inflammation. Numerous computational tools were utilized to carry out the work such as druglikeness filtering, QSAR modeling, ADMET profiling to molecular docking. The different level of screenings led to 6 molecules which were docked with the help of two different molecular docking approaches viz. AutoDock and AutoDockFR to get optimal receptor-ligand conformations. From the 142 compounds finally we got one molecule "indirubin-3′-monoxime" which is then followed by experimental validations [75].

## **5. Conclusion**

In this era of high-performance computing technology, there is hardly any field of science which is not touched upon by some amount of significant computational works. The potential of computing power is much reliant on advancement in hardware and algorithms. Substantial number of computational tools and techniques were developed and applied in the fascinating area of proteomics also. Mathematical models were devised in the form of FF parameters and implemented in various algorithms. Here, we have discussed the inevitable role of FF in protein structure prediction/modeling, conformational dynamics and their functional aspects along with the applications in virtual screening programs. As discussed in the chapter, a lot of programs with variety of FFs are available for structure prediction, MD simulations etc., but there is still a scope of further developments. For example, till now it is a challenge for accurately predicting protein structures of larger sizes or the protein sequences having low amount of similarity with sequences of known structures. Also, the existing software are in use for transmembrane protein structure prediction but it is an hour need to develop different program to model the trans-membrane segments. Although MD simulations were utilized for validating predicted structures of membrane proteins and/or for getting insights of their mechanism, challenge remains in the forms of FF as at times it is difficult to get the parameters for membrane proteins, lipids in which they were embedded, any bound coordinated metal ions in a single FF. The accuracy of models depends upon pH and dynamic charge environment instead of static electrostatic charges, and polarizable water models, requires further development and testing of polarizable force fields. The existing FF were designed with aid of experimental data for globular proteins and applied for studying IDPs whereas disordered

proteins are having non-structural segments. Thus, it necessitates designing and developing different set of FF parameters for simulating exclusively IDPs. In summary, there is always space for improvement in existing ones and developing new models with higher accuracy in any field of science.
