Name
Abstract | ||
|---|---|---|
Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCBB.2015.2401020 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Virtual screening,high performance computing,distributed protein docking,HTCondor,Naive Bayes,Scoring function | Docking (molecular),Computer science,Docking (dog),Protein–ligand docking,Molecular Docking Simulation,Chemical library,Macromolecular docking,Artificial intelligence,Bioinformatics,Virtual screening,AutoDock,Machine learning | Journal |
Volume | Issue | ISSN |
PP | 99 | 1545-5963 |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prachi Pradeep | 1 | 2 | 0.72 |
| Craig A. Struble | 2 | 1 | 0.36 |
| Terrence Neumann | 3 | 1 | 0.36 |
| Daniel S. Sem | 4 | 1 | 0.70 |
| Stephen J. Merrill | 5 | 14 | 2.40 |