title: Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation.
abstract: The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.
authors.affiliation: University of Grenoble Alpes, LJK, 38000, Grenoble, France. Sergei.Grudinin@inria.fr.|CNRS, LJK, 38000, Grenoble, France. Sergei.Grudinin@inria.fr.|Inria, 38000, Grenoble, France. Sergei.Grudinin@inria.fr.
authors.forename: Sergei
authors.identifier: 0000-0002-1903-7220
authors.initials: S
authors.lastname: Grudinin
authors.affiliation: University of Grenoble Alpes, LJK, 38000, Grenoble, France.|CNRS, LJK, 38000, Grenoble, France.|Inria, 38000, Grenoble, France.
authors.forename: Maria
authors.initials: M
authors.lastname: Kadukova
authors.affiliation: University of Grenoble Alpes, LJK, 38000, Grenoble, France.|CNRS, LJK, 38000, Grenoble, France.|Inria, 38000, Grenoble, France.
authors.forename: Andreas
authors.initials: A
authors.lastname: Eisenbarth
authors.affiliation: Université Côte d'Azur and Inria, Sophia Antipolis, France.|Virology and Molecular Immunology, INRA, Jouy-en-Josas, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Marillet
authors.affiliation: Université Côte d'Azur and Inria, Sophia Antipolis, France.
authors.forename: Frédéric
authors.initials: F
authors.lastname: Cazals
mesh_terms: D001665:Binding Sites; D030562:Databases, Protein; D015195:Drug Design; D019277:Entropy; D018841:HSP90 Heat-Shock Proteins / Q000737:chemistry*; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods*; D011446:Prospective Studies; D011485:Protein Binding; D011487:Protein Conformation; D012044:Regression Analysis; D013329:Structure-Activity Relationship; D013816:Thermodynamics
chemical_list: D018841:HSP90 Heat-Shock Proteins; D008024:Ligands
keywords: Machine learning; Parameter estimation; Protein-ligand docking; Ridge regression; Scoring function