title: Cloud computing approaches for prediction of ligand binding poses and pathways.
abstract: We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.
authors.affiliation: Department of Chemistry, Stanford University, Stanford, CA 94305.
authors.forename: Morgan
authors.initials: M
authors.lastname: Lawrenz
authors.affiliation: 1] Department of Chemistry, Stanford University, Stanford, CA 94305 [2] SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305.
authors.forename: Diwakar
authors.initials: D
authors.lastname: Shukla
authors.affiliation: 1] Department of Chemistry, Stanford University, Stanford, CA 94305 [2] SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D001665:Binding Sites; D000067917:Cloud Computing; D018360:Crystallography, X-Ray; D008024:Ligands; D056004:Molecular Dynamics Simulation; D011485:Protein Binding; D015202:Protein Engineering; D022061:Tacrolimus Binding Protein 1A / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D008024:Ligands; D022061:Tacrolimus Binding Protein 1A