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ABSTRACT: Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A2A receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative view point, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A2A receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.
Journal of Chemical Information and Modeling 06/2013; · 4.68 Impact Factor
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ABSTRACT: Molecular docking represents an important technology for structure-based drug design. Docking is a computational technique aimed at the prediction of the most favorable ligand-target spatial configuration and an estimate of the corresponding complex free energy, although as stated at the beginning accurate scoring methods remain still elusive. In this chapter, the state of art of molecular docking methodologies and their applications in drug discovery is summarized.
Methods in molecular biology (Clifton, N.J.) 01/2013; 924:339-60.
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ABSTRACT: The recent availability of X-ray structures for diverse ligand-bound Family A G protein-coupled receptors (GPCRs) in multiple conformations (inactive form with an antagonist/inverse agonist bound and active form with an agonist bound) now enables rational drug design efforts that have historically been applied to soluble enzyme targets. Here, we review properties of these GPCR binding sites, using a unique combination of calculated physicochemical properties and water energetics (GRID, WaterMap and SZMAP) to provide a new perspective and rational assessment of druggability for each GPCR target binding site. Examples are described from several well-studied enzyme systems to support this advanced structure-based approach to assessing druggability and to contrast their properties with those of GPCRs. Changes in receptor conformations between the GPCR inactive and active forms evident from the protein structures are discussed, yielding important pointers for rational drug design of antagonists and agonists and a better understanding of GPCR activation.
Trends in Pharmacological Sciences 03/2012; 33(5):249-60. · 10.93 Impact Factor
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ABSTRACT: Background: Drug approval applications to the FDA have shown a remarkably small increment compared with what was expected. In the last few years several efforts have been made to improve the results of rational drug design approaches and in particular to predict inhibitor-target structure and to evaluate the free energy of binding. Virtual database screening, combined with other computational methods, is one of the most promising methods to overcome this key issue. Objective: It is possible to understand how computational medicinal chemistry is changing, improving from its errors and moving towards becoming a more important tool for drug development. Methods: Some of the most recent modeling techniques have been presented and in particular the benefits of combining these techniques are highlighted. Results/conclusion: At present computational chemists can understand the peculiar problems associated with the study of biological systems and on this basis they can choose the right collection of complementary in silico approaches to solve the medicinal chemistry problem in a better manner.
Expert Opinion on Drug Discovery 05/2008; 3(5):579-90. · 2.12 Impact Factor
