Computational Virtual Screening Towards Designing Novel Anticancer Drugs Po-Yuan Chen1,2
of Biological Science and Technology, China Medical University, Taichung, Taiwan, 2Brain Research Centre, University of British Columbia, Vancouver, 1Republic of China 2Canada
Generally speaking, Docking is most popular and critical issue in this research field, because it contains most important information both Ligands (Drugs) and Receptors (it can be intracellular protein, trans-membrane protein or extracellular protein). However, when the ligand’s information is not sufficient, it needs other calculation strategies to design and “modify” the ligands, and theoretically improve the drug effects, and that is called De Novo Evolution Drug Design. Current methods for structure-based drug design can be divided roughly into two categories. The first category is about “finding” ligands for a given receptor, which is usually referred as database searching. In this case, a large number of potential ligand molecules are screened to find those fitting the binding pocket of the receptor. This method is usually referred as structure-based drug design. The key advantage of database searching is that it saves synthetic effort to obtain new lead compounds. Another category of structure-based drug design methods is about “building” ligands, which is usually referred as receptor-based drug design. In this case, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested. These techniques are raising much excitement to the drug design community. Above two computational methods, the first is called virtual screening by Docking (the drugs are well prepared and need to be screen out the most suitable candidates), and the other is De.Novo Evolution Drug Design (De Novo means “creates” or “building” ligands) . However, when the targeting protein is unclear, or the factors are complicated, QSAR method is implemented to help user solving these problems. Because QSAR method just needs ligands structures and IC50 datasets to unveil an unknown novel drugs. Finally, when both of Ligands and Receptors are unknown, Homology Modeling is the only method for dealing with this problem. By using Homology Modeling, the Receptors 1-D sequences similarities can be used as a tool to reconstruct the 3-D structures.
2. Methods and materials
Docking small molecules (ligands) into larger protein molecules (receptors) is a complex and difficult task. Docking programs include CDOCKER, LibDock, and LigandFit. Here, I introduce LigandFit for this research because it bases on an initial shape matched to the binding site and it is easier to observe the interaction of the ligand and the protein. There are two major parts of the LigandFit docking: 1. 2. Specify the region of the receptor to use as the binding site for docking. Site partitioning may be applied to select parts of the binding site during docking. Dock ligands to the specified site. This part consists of the following steps: a. Conformational search to generate candidate ligand conformations for docking; b. Compare the ligand shape and protein binding site shape by computing their size of possession; c. Minimize the rigid body energy of the candidate ligand pose/conformation by using the Dockscore calculation.
In the following steps, we discard the water (because it will be complicated to the calculations) and ligand from the receptor protein, and calculate the score for the ligand docking to protein. Check the interacting force between the receptor protein and drugs (Fig. 1).
Fig. 1. The procedure of LigandFit Docking procedure LigandFit: Docking and Score using Accelrys Software The...
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