Abstract
Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.
Highlights
Virtual screening techniques are becoming increasingly more important in drug discovery
The similarity search is based upon the “similar property principle”, which states that molecules that are structurally similar are likely to have similar properties [9]
Based on the “lock-and-key” principle, we propose a novel pre-docking procedure that matches the sizes of the ligand with the protein binding site, and optimizes the gridbox size before docking
Summary
Virtual screening techniques are becoming increasingly more important in drug discovery. A popular method for virtual screening is molecular docking [1, 2], which selects small-molecule structures from databases such as ChemBank [3], ChemPDB [4], KEGG [5], and NCI [6] and docks them into the protein binding site [7]. The similarity search is based upon the “similar property principle”, which states that molecules that are structurally similar are likely to have similar properties [9] This technique uses a ligand with known chemical properties, inhibitory activities, or binding modes for a target of interest as a reference for searching similar ligands in the database regardless of the shape and size of the protein binding site. The extracted ligand name (X-ray ligand) pockets close to or floating over the binding site The ligands were ranked according to their protein-ligand affinity (calculated inhibitory constant, Ki)
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