**3. Molecular Docking**

One application of molecular docking is virtual screening, in which a library of com‐ pounds is compared to one or more targets, thereby providing an analysis of compounds ranked by potential.

Virtual screening computational techniques are applied to the selection of compounds that can be active in a target protein.

Monte Carlo simulations, and genetic algorithms, among others, are all suitable for molecu‐

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Scoring functions must be able to discriminate between different ligand-receptor interac‐ tions. These can be grouped into field-force, empirical, and knowledge-based methods.

The algorithms can be classified into rigid body docking and flexible docking algorithms. In rigid-body docking, both the ligand and receptor are rigid. These methods are faster, but do not allow ligand and receptor to adapt to the binding. In flexible methods, the computation‐ al cost is higher compared to rigid methods. However, in these cases, the flexibility of the

Another important factor to be considered in ligand-receptor interactions is the presence of water. Some methods allow water molecules to be positioned. In cases where this is not possible, the position of water molecules can be predicted using a software program such as GRID [17].

GRID calculates the interactions between chemical groups and small molecules with known 3-dimensional structures. The energies are calculated using Lennard-Jones interactions, elec‐ trostatic and hydrogen bonding between the compounds, and 3-dimensional structures, us‐

Examples of tools available for docking proteins include AUTODOCK4.2 [29], GOLD [16],

GOLD uses a genetic algorithm that seeks solutions through docking that propagates multi‐ ple copies of flexible models of the ligand in the active site of the receptor and recombining

The process of searching the databases can be time consuming; a way to reduce the search space is filtering databases by performing a search with the fastest algorithms, selecting the best candidates ranked. Subsequently, within this selection, a search algorithm slowly gen‐ erates a new ranking of the ligands. Another way to reduce the number of ligands being studied in the database is to perform a search for ligands that offer the greatest possibility of being used in drug design. In this case, it is possible to filter the database by using the AD‐

Lipinski´s rule of 5 [25] can be used. The rule of 5 is a set of properties that characterise com‐ pounds that exhibit good oral bioavailability. It states that, in general, an orally active drug

**Lipinski´s Rule Not more than 5 hydrogen bond donors (nitrogen or oxygen atoms with one or more hydrogen atoms**

segments of copies at random until a converged set of structures is generated.

MET (absorption, distribution, metabolism, excretion, and toxicity) filter.

has no more than 1 violation of the rules (Table 1):

**An octanol-water partition coefficient log P not greater than 5**

**A molecular mass less than 500 daltons**

**Table 1.** Lipinski's Rule of Five

**Not more than 10 hydrogen bond acceptors (nitrogen or oxygen atoms)**

lar docking.

and GLIDE [10].

ligand and/or receptor is considered.

ing a position-dependent dielectric function.

In molecular docking, a ligand is usually placed in the binding site of a predetermined struc‐ ture of a receptor (Figure 8). In other words, this is a method based on structure. The recep‐ tor is typically a protein and the ligand is a small molecule or a peptide. The optimal position and orientation of the ligand are determined using a search algorithm and a scoring function that ranks the solutions.

The first step of the process of molecular docking is to determine the binding sites of the protein. This can be done by software programs such as Q-Sitefinder [24].

The metaPocket method [13] predicts binding sites using 4 methods: LIGSITEcs [12], PASS [5], Q-Sitefinder, and SURFnet [23] – which in combination increase the success rate of pre‐ diction. The methods LIGSITEcs, PASS, and SURFnet use only the geometrical characteris‐ tics of the protein structure, detecting regions that have the potential to be binding sites. Such methods do not require prior knowledge of the ligands.

In Q-Sitefinder, the surface of the protein is covered with a layer of methyl probes for the calculation of Van der Waals interactions between the protein and the probe. Probes with favourable interaction energies are retained, and are classified into groups based on the number of probes per group. The largest and most energetically favourable group is ranked first and considered the best potential binding site.

Another step is to define the position of the ligand in the pocket. This can be predicted by molecular docking algorithms.

Several methods have developed different scoring functions and different search methodol‐ ogies.

The search algorithms have to be able to present different configurations and orientations of the ligand in a short time. Search algorithms, such as those used in molecular dynamics, Monte Carlo simulations, and genetic algorithms, among others, are all suitable for molecu‐ lar docking.

Scoring functions must be able to discriminate between different ligand-receptor interac‐ tions. These can be grouped into field-force, empirical, and knowledge-based methods.

The algorithms can be classified into rigid body docking and flexible docking algorithms. In rigid-body docking, both the ligand and receptor are rigid. These methods are faster, but do not allow ligand and receptor to adapt to the binding. In flexible methods, the computation‐ al cost is higher compared to rigid methods. However, in these cases, the flexibility of the ligand and/or receptor is considered.

Another important factor to be considered in ligand-receptor interactions is the presence of water. Some methods allow water molecules to be positioned. In cases where this is not possible, the position of water molecules can be predicted using a software program such as GRID [17].

GRID calculates the interactions between chemical groups and small molecules with known 3-dimensional structures. The energies are calculated using Lennard-Jones interactions, elec‐ trostatic and hydrogen bonding between the compounds, and 3-dimensional structures, us‐ ing a position-dependent dielectric function.

Examples of tools available for docking proteins include AUTODOCK4.2 [29], GOLD [16], and GLIDE [10].

GOLD uses a genetic algorithm that seeks solutions through docking that propagates multi‐ ple copies of flexible models of the ligand in the active site of the receptor and recombining segments of copies at random until a converged set of structures is generated.

The process of searching the databases can be time consuming; a way to reduce the search space is filtering databases by performing a search with the fastest algorithms, selecting the best candidates ranked. Subsequently, within this selection, a search algorithm slowly gen‐ erates a new ranking of the ligands. Another way to reduce the number of ligands being studied in the database is to perform a search for ligands that offer the greatest possibility of being used in drug design. In this case, it is possible to filter the database by using the AD‐ MET (absorption, distribution, metabolism, excretion, and toxicity) filter.

Lipinski´s rule of 5 [25] can be used. The rule of 5 is a set of properties that characterise com‐ pounds that exhibit good oral bioavailability. It states that, in general, an orally active drug has no more than 1 violation of the rules (Table 1):


**Table 1.** Lipinski's Rule of Five

Virtual screening computational techniques are applied to the selection of compounds that

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

In molecular docking, a ligand is usually placed in the binding site of a predetermined struc‐ ture of a receptor (Figure 8). In other words, this is a method based on structure. The recep‐ tor is typically a protein and the ligand is a small molecule or a peptide. The optimal position and orientation of the ligand are determined using a search algorithm and a scoring

**Figure 8.** Diagram illustrating the docking of a ligand to a receptor to produce a complex.

protein. This can be done by software programs such as Q-Sitefinder [24].

Such methods do not require prior knowledge of the ligands.

first and considered the best potential binding site.

molecular docking algorithms.

ogies.

The first step of the process of molecular docking is to determine the binding sites of the

The metaPocket method [13] predicts binding sites using 4 methods: LIGSITEcs [12], PASS [5], Q-Sitefinder, and SURFnet [23] – which in combination increase the success rate of pre‐ diction. The methods LIGSITEcs, PASS, and SURFnet use only the geometrical characteris‐ tics of the protein structure, detecting regions that have the potential to be binding sites.

In Q-Sitefinder, the surface of the protein is covered with a layer of methyl probes for the calculation of Van der Waals interactions between the protein and the probe. Probes with favourable interaction energies are retained, and are classified into groups based on the number of probes per group. The largest and most energetically favourable group is ranked

Another step is to define the position of the ligand in the pocket. This can be predicted by

Several methods have developed different scoring functions and different search methodol‐

The search algorithms have to be able to present different configurations and orientations of the ligand in a short time. Search algorithms, such as those used in molecular dynamics,

can be active in a target protein.

Applications

82

function that ranks the solutions.

Analysis of the metabolic fate and chemical toxicity of the compounds can be accomplished using the software programs DEREK and METEOR [11]. DEREK predicts whether a given chemical is toxic to humans, mammals, and bacteria. METEOR uses the knowledge of me‐ tabolism rules to predict the metabolic fate of chemicals, assisting in the choice of more effi‐ cient molecules.

[36] evaluated the inhibitory effect of 1-(3-dimethylaminopropyl)-1-(4-fluorophenyl)-3 oxo-1,3-dihydroisobenzofuran-5-carbonitrile (DFD) on viper venom-induced haemorrhagic and PLA2 activities. Molecular docking studies of DFD and snake venom metalloproteases (SVMPs) were performed to understand the mechanism of inhibition by DFD, since SVMPs constitute one of the protein groups responsible for venom-induced haemorrhage. The docking results showed that DFD binds to a hydrophobic pocket in SVMPs with the K*i* of

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*In silico* approaches used in protein structure prediction and in drug discovery research have

Computational methods used in the search for inhibitors play an essential role in the process

The application of protein modelling methods has contributed significantly in cases where the structure of the target protein has not been solved, allowing the SBVS process be completed.

Good results obtained by virtual screening depend on the quality of structures, databases to be scanned, the search algorithms, and scoring functions. Therefore, there must be a good interaction and exchange of information between *in silico* and experimental methods. Care‐

**Summary Tools**

ful application of these strategies is necessary for successful drug design.

Table 2 presents a list of software tools and server web sites.

**PROCHECK** http://www.ebi.ac.uk/thornton-srv/software/PROCHECK/

**PROVE** http://www.doe-mbi.ucla.edu/Software/PROVE.html

**PDB** http://www.rcsb.org/pdb/home/home.do

**HHpred** http://toolkit.tuebingen.mpg.de/hhpred **ClustalW** http://www.ebi.ac.uk/Tools/msa/clustalw2/

**BLAST** http://blast.ncbi.nlm.nih.gov/

**SWISS-MODEL** http://swissmodel.expasy.org/ **MODELLER** http://salilab.org/modeller/ **SCRWL4** http://dunbrack.fccc.edu/scwrl4/

**WHAT IF** http://swift.cmbi.ru.nl/whatif/

**Verify3D** http://nihserver.mbi.ucla.edu/Verify\_3D/ **ERRAT** http://nihserver.mbi.ucla.edu/ERRATv2/

19.26 x 10 -9 (kcal/mol) without chelating Zn2+ in the active site.

**6. Conclusions**

been presented in this chapter.

of discovering new drugs.
