Name
Title | ||
|---|---|---|
Geauxdock: A Novel Approach For Mixed-Resolution Ligand Docking Using A Descriptor-Based Force Field |
Abstract | ||
|---|---|---|
Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.loni.org/lasigma/package/dock/. (C) 2015 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1002/jcc.24031 | JOURNAL OF COMPUTATIONAL CHEMISTRY |
Keywords | Field | DocType |
molecular docking, force field development, force filed optimization, Monte Carlo simulations, mixed-resolution modelling, descriptor-based force field | DOCK,Docking (molecular),Lead Finder,Searching the conformational space for docking,Docking (dog),Computational chemistry,Molecular Docking Simulation,Chemistry,Molecular dynamics,Homology modeling | Journal |
Volume | Issue | ISSN |
36 | 27 | 0192-8651 |
Citations | PageRank | References |
1 | 0.36 | 21 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Ding | 1 | 5 | 1.15 |
| Ye Fang | 2 | 5 | 1.24 |
| Wei P Feinstein | 3 | 24 | 1.74 |
| J. Ramanujam | 4 | 701 | 51.22 |
| David M. Koppelman | 5 | 68 | 11.43 |
| Juana Moreno | 6 | 5 | 1.24 |
| Michal Brylinski | 7 | 37 | 6.10 |
| Mark Jarrell | 8 | 5 | 1.24 |