Computational protocol: A tool for the post data analysis of screened compounds derived from computer-aided docking scores

Similar protocols

Protocol publication

[…] It is important to visualize the docked poses of high-scoring compounds because many ligands are docked in different orientations and may often miss interactions that are known to be important for the target receptor. This sort of study becomes more difficult as the size of the dataset increases. Therefore, an alternative approach is to eliminate unpromising compounds before docking by restricting the dataset to drug-like compounds; by filtering the dataset based on appropriate property and sub-structural features and by performing diversity analysis []. Consensus scoring combines information from different scores to balance errors in single scores and improve the probability of identifying ‘true’ ligands []. In our study, we have tested five different scoring functions as used in tools such as: (i) GOLD []; (ii) Patchdock []; (iii) Molegro []; (iv) MEDock []; (v) AutodockVina []. The input for this application is Spread Sheet with an extension of .xls. The spread sheet consists of docking results of various compounds from various docking tools.The uploaded file is parsed and the data is stored in 2D array. We then use DST in a 5 step procedure. The steps used are: (1) Divide the data into 4 classes; (2) get results from Rank Sum Technique; (3) get results from DST unweighted; (4) get results from DST weighted; (5) get results from Zhang Rule. Compounds selected by steps 2 to 5 in the above procedure will be considered for further analysis and investigation in the discovery pipeline. […]

Pipeline specifications

Software tools PatchDock, MEDock
Application Protein interaction analysis