A Comparative Evaluation of Docking Programs using Influenza Endonuclease as Target Protein

Numerous diseases affects millions of people worldwide and scientists from different field of specializations have join hands to find effective solutions. The field of drug design and discovery is one such area where scientists come together to deal with design, optimize, develop and test drug molecules against various targets. One key tool to screen dataset of molecules and also to analyze the atomic interactions between protein-ligand molecules, in the area of structure based drug design is molecular docking. The molecular docking tools are governed by different sampling algorithm and scoring functions. The docking programs have performed well enough individually for structure based drug design; there is a need to check the consensus among different programs at the level of their docking results. A set of small molecules were considered and they were filtered based on their druglikeness properties. The target considered is the influenza endonuclease protein classified as a RNA binding protein. Three docking programs based on different algorithms were considered viz. Molegro Virtual Docker, Glide and GOLD. The resulting protein-ligand complexes were listed based on the respective scoring functions. We found that among the top20 compounds, 10 were common among the considered three softwares. However their ranking among the individual programs were different. Although there are differences at the level of algorithms and the numerical scores generated by docking programs but the results indicate that there is some level of consensus among them.