title: Ligand-protein docking using a quantum stochastic tunneling optimization method.
abstract: A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.
authors.affiliation: De Novo Pharmaceuticals, Compass House, Vision Park, Histon, Cambridge CB4 9ZR, United Kingdom. Ricardo.Mancera@denovopharma.com
authors.forename: Ricardo L
authors.initials: RL
authors.lastname: Mancera
authors.forename: Per
authors.initials: P
authors.lastname: Källblad
authors.forename: Nikolay P
authors.initials: NP
authors.lastname: Todorov
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D015195:Drug Design; D008024:Ligands; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D008024:Ligands; D011506:Proteins