Name
Title | ||
|---|---|---|
Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach. |
Abstract | ||
|---|---|---|
Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10822-017-0058-x | Journal of Computer-Aided Molecular Design |
Keywords | Field | DocType |
Atomic property fields,D3R,ICM,Ligand docking,Receptor flexibility | Docking (molecular),Lead Finder,Searching the conformational space for docking,Docking (dog),Computational chemistry,Protein–ligand docking,Molecular Docking Simulation,Chemistry,Bioinformatics,Stereochemistry,Protein structure,Scoring functions for docking | Journal |
Volume | Issue | ISSN |
32 | 1 | 0920-654X |
Citations | PageRank | References |
1 | 0.36 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Polo Chun-Hung Lam | 1 | 4 | 0.82 |
| Ruben Abagyan | 2 | 430 | 55.44 |
| Maxim Totrov | 3 | 269 | 31.59 |