interaction between lipid and protein. Molecular Docking is the virtual experiment used to measure the strength of binding between two molecules. The three dimensional structure of both the ligand and the protein are the inputs to perform docking. There are various docking tools available. Some are commercial whereas some of them are free to download.
Since particular docking tools work best for the specific targets, it is good practice to use more than one docking tool in order to obtain accurate results (Lape et al., 2010). Each docking tool varies from the other in the type of scoring function, it uses to perform molecular docking. The scoring function is a mathematical algorithm that is used to calculate the strength of non-covalent interactions, which can also be termed as binding affinity (Halperin et al., 2002).There are three types of scoring functions.
1) Knowledge based scoring functions 2) Empirical scoring functions
3) Force field, scoring functions.
All three scoring functions calculate binding affinity between two molecules by using a different algorithm.
The JCB (Jena Center for Bioinformatics) protein-protein interaction website discusses the various docking software available to study protein-ligand and protein-protein interactions. The website Click2Drug provides the information about all the available bioinformatics tools
79 to study the ligand binding sites, databases that have structural information of proteins and ligands and various docking tools. AutoDock 4.2 is a widely used molecular docking technique for which the quality of assessment (of protein-ligand binding affinity prediction) was determined (Kim et al., 2008). Glide is another docking tool developed by Schrodinger that predicts protein-ligand binding modes and ranks the ligands via high-throughput screening. Glide docking tool uses two scoring functions: Standard Precession (SP) and Extra Precession (XP). To dock multiple lipid ligands with single protein, Glide can be used. Also, there are web-based docking tools available like Docking Server, Hex Server, and Swiss Dock. These tools accept the input of protein and ligand in the form of PDB files. The output is generated from the server very quickly, from which the user can analyze the results of docking.
The results of docking could be further analyzed using software tools like Pymol, Swiss PDB, Chimera, and LigPlot. Usually the strength of binding between the protein and lipid is measured in terms of the binding energy. Binding energy is based on the intermolecular interactions between the protein and ligand. The types of bonds can be hydrogen bonds, hydrophobic interactions, electrostatic interactions and van der Waal interactions (Ross et al., 1981). The distance and type of bond between the ligand and the receptor determine the strength of binding where the lower the binding energy the stronger the affinity between the ligand and the receptor (Kim et al., 2008).
Docking is also useful in computer aided drug design. Section 2.3 discussed about the health importance of three groups of lipids. These three groups of lipids were shown to bind to different molecular targets which are used in the design of drugs. For example, tocotrienol as a pharmacological target for PPAR can be used to treat cancer, diabetes and cardiovascular diseases.It was proved through docking that tocotrienols have a strong affinity with PPARs (Gaddipati, 2012). Further use of bioinformatics tools is discussed below.
80 The docking tools can be used in this aspect to find how strongly a ligand can bind to its receptor (protein or enzyme). The development of bioinformatics tools is also significant in the field of drug discovery. The molecular docking of tocotrienol with antiperoxidative enzymes was carried using Autodock4.0 as the docking program (Khan et al., 2011). This in silico docking is also supported by an in vivo experiment and investigated the protective role of tocotrienol against infection and inflammation. The binding of tocotrienols with antioxidant enzymes was observed through molecular docking (Khan et al., 2011).
Another docking tool-Molegro Virtual Docker can be used to study the enzyme inhibition (Khan et al., 2011). The binding of PUFAs with Brain-Derived Neurotrophic Factor (BDNF) was studied through molecular docking. The results of this study suggested that the physical interaction of PUFAs with BDNF can modulate insulin resistance and regulate food intake and body weight (Vetrivel et al., 2012). The interaction of endocannabinoids with CB1 and CB2 were studied using bioinformatics tools like quantitative structural-activity relationships (a computational method that correlates the structure or property of a protein with activities), docking and molecular dynamic simulations (Reggio, 2010).
Table 2.5 Computational tools and databases for the study of lipid-protein interaction
Name of tool Type of tool URL
Click2Drug Web site http://www.click2drug.org/
DockingServer Web-Based Docking
Tool http://www.dockingserver.com/web
Hex Server Web-Based Docking
Tool http://hexserver.loria.fr/
Swiss Dock Web-Based Docking
Tool http://swissdock.vital-it.ch/docking
Ligplot Software http://www.ebi.ac.uk/thornton-
srv/software/LIGPLOT/
MD tools Website http://www.ks.uiuc.edu/Development/MDTools/
The inhibitory activities of endocannabinoids are studied by using flexible docking procedures (Romani et al., 2011). Various bioinformatics tools involved in the study of lipid- protein interactions were shown in Table 2.5.
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2.5.4.1. Limitations of Molecular Docking
Calculation of lowest binding energy or binding affinity depends on how well the ligand binds to the protein (Sousa et al., 2006). Docking protocols are the combination of search algorithm and scoring functions. There is relatively a large number of scoring functions and search algorithms available today. In order to include the true binding modes between protein and ligand, the search algorithm should allow the degree of freedom of the protein-ligand system (Sousa et al., 2006). The two critical elements of any search algorithm are fast and effectiveness. Logically, the effectiveness of any docking protocol lies in combining the best search algorithm with the best scoring function. However, several studies have confirmed that the performance of most docking tools depends on the binding site and the ligand to be investigated (Clark et al., 1993; Kuntz et al., 1982; Schulz-Gasch & Stahl, 2003).
Sometimes, inaccuracies in the energy models used to score potential ligand-receptor complexes lead to the differences in binding energies. Some docking experiments might fail due to the inability of docking method to account for conformational changes that occur during the binding process of ligand and protein (Teodoro et al., 2001). Furthermore, predicting receptor structural rearrangements is a complex problem.
Current docking methods follow the lock and key theory proposed by Emil Fischer in 1890 (Cramer, 1994). So the protein structures were considered as rigid entities and the ligand changes it’s three dimensional structures during the binding process to fit into protein binding site. Later in 1958 Koshland proposed induced fit theory to explain the mechanism of interaction between protein and ligand (Koshland, 1995). According to this theory both protein and ligand structures are flexible and both the structures change their conformation when they interact to form a complex to form a minimum energy fit. Unfortunately, the computational capability is limited to follow the exact modeling of the flexibility available to
82 protein during binding process (Teodoro et al., 2001). Modeling the full flexibility of protein requires 1000 degrees of freedom whereas conventional ligand techniques can handle up to 30 degrees of freedom approximately (Teodoro et al., 2001). This problem can be solved with MD simulations as MD simulations take into account of all the degrees of freedom available to the protein (Teodoro et al., 2001). Accurate energy calculations can also be carried out using MD simulations. However, MD simulations are computationally expensive.
The author’s study of molecular docking was supported by both MD simulations and wet laboratory experimental validations.