Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3
- 156 Downloads
We participated in Subchallenges 1 and 2 of the Drug Design Data Resource (D3R) Grand Challenge 3. To prepare our submissions, we performed molecular docking with UCSF DOCK 6 and binding potential of mean force (BPMF) calculations—free energy calculations between flexible ligands and rigid receptors—using our open-source software package Alchemical Grid Dock (AlGDock). For each system, submissions were based on the minimum BPMF calculated for a selected set of crystal structures. In Subchallenge 1, our workflow performed poorly. Possible reasons for the poor performance include the neglect of cooperative ligands and limited sampling of ligand binding poses. In Subchallenge 2, our workflow led to some of most highly correlated submissions (Pearson R = 0.5) for vascular endothelial growth factor receptor 2. However, our results were poorly correlated for Janus Kinase 2 and Mitogen-activated protein kinase 14. Affinity prediction could potentially be improved by systematic selection of more diverse receptor configurations.
KeywordsD3R Drug Design Data Resource Binding affinity Pose prediction AlGDock
We thank OpenEye scientific software for providing a free academic license. This research was supported by the National Institutes of Health (R15GM114781). Calculations were performed on the Open Science Grid  as well as a computing cluster managed by Illinois Tech.
- 1.D3R 2018 workshop (2018) https://drugdesigndata.org/about/d3r-2018-workshop
- 6.Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703CrossRefGoogle Scholar
- 12.Minh DDL (2018) Power transformations improve interpolation of grids for molecular mechanics interaction energies. J Comput Chem (in press)Google Scholar
- 13.Minh DDL (2015) Protein-ligand binding potential of mean force calculations with Hamiltonian replica exchange on alchemical interaction grids. arXivGoogle Scholar
- 14.Minh DDL (2017) AlGDock. https://github.com/CCBatIIT/AlGDock
- 26.Case D, Cerutti D, TE Cheatham I, Darden T, Duke R, Giese T, Gohlke H, Goetz A, Greene D, Homeyer N, Izadi S, Kovalenko A, Lee T, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Mermelstein D, Merz K, Monard G, Nguyen H, Omelyan I, Onufriev A, Pan F, Qi R, Roe D, Roitberg A, Sagui C, Simmerling C, Botello-Smith W, Swails J, Walker R, Wang J, Wolf R, Wu X, Xiao L, York D, Kollman P (2017) AMBER 2017. University of California, San Francisco. http://ambermd.org/CiteAmber.php
- 42.Pordes R, Petravick D, Kramer B, Olson D, Livny M, Roy A, Avery P, Blackburn K, Wenaus T, Würthwein F, Foster I, Gardner R, Wilde M, Blatecky A, McGee J, Quick R (2007) The open science grid. J Phys: Conf Ser 78:012–057Google Scholar