3D QSAR Pharmacophore Modeling, in Silico Screening, and Density Functional Theory (DFT) Approaches for Identification of Human Chymase Inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors

Computational Approaches for the Characterization of the Structure and Dynamics of G Protein-Coupled Receptors: Applications to Drug Design

G Protein-Coupled Receptors (GPCRs) constitute the most pharmacologically relevant superfamily of proteins. In this thesis, a computational pipeline for modelling the structure and dynamics of GPCRs is presented, properly combined with experimental collaborations for GPCR drug design. These include the discovery of novel scaffolds as potential antipsychotics, and the design of a new series of A3 adenosine receptor antagonists, employing successful combinations of structure- and ligand-based approaches. Additionally, the structure of Adenosine Receptors (ARs) was computationally assessed, with implications in ligand affinity and selectivity. The employed protocol for Molecular Dynamics simulations has allowed the characterization of structural determinants of the activation of ARs, and the evaluation of the stability of GPCR dimers of CXCR4 receptor. Finally, the computational pipeline here developed has been integrated into the web server GPCR-ModSim (http://gpcr.usc.es), contributing to its application in biochemical and pharmacological studies on GPCRs

Diseño y síntesis de derivados mono- y multivalentes de 1,4-iminociclitoles: estudio de sus propiedades como inhibidores de glicosidasas.

Premio Extraordinario de Doctorado U

Computational Prediction of Structure−Activity Relationships for the Binding of Aminocyclitols to β-Glucocerebrosidase

Glucocerebrosidase (GCase, acid β-Glucosidase) hydrolyzes the sphingolipid glucosylceramide into glucose and ceramide. Mutations in this enzyme lead to a lipid metabolism disorder known as Gaucher disease. The design of competitive inhibitors of GCase is a promising field of research for the design of pharmacological chaperones as new therapeutic agents. Using a series of recently reported molecules with experimental binding affinities for GCase in the nanomolar to micromolar range, we here report an extensive theoretical analysis of their binding mode. On the basis of molecular docking, molecular dynamics, and binding free energy calculations using the linear interaction energy method (LIE), we provide details on the molecular interactions supporting ligand binding in the different families of compounds. The applicability of other computational approaches, such as the COMBINE methodology, is also investigated. The results show the robustness of the standard parametrization of the LIE method, which reproduces the experimental affinities with a mean unsigned error of 0.7 kcal/mol. Several structure−activity relationships are established using the computational models here provided, including the identification of hot spot residues in the binding site. The models derived are envisaged as important tools in ligand-design programs for GCase inhibitors.Financial support from the “Ministerio de Ciencia e Innovación”, Spain (Project CTQ2008-01426/BQU) and “Generalitat de Catalunya” (Grant 2009SGR-1072) is acknowledged. J.Å. acknowledges support from the Swedish Research Council (VR). L.D. is grateful to CSIC for predoctoral research training support within the JAE-Predoc program. H.G.T. is a researcher of the Isidro Parga Pondal program (Xunta de Galicia, Spain). Mr. Lars Boukharta is gratefully acknowledged for technical assistance and helpful discussions. Finally, the authors acknowledge the “Centre de Supercomputació de Catalunya” (CESCA) for allowing the use of its software and hardware resources.Peer reviewe

Computational Prediction of Structure−Activity Relationships for the Binding of Aminocyclitols to β-Glucocerebrosidase

Glucocerebrosidase (GCase, acid β-Glucosidase) hydrolyzes the sphingolipid glucosylceramide into glucose and ceramide. Mutations in this enzyme lead to a lipid metabolism disorder known as Gaucher disease. The design of competitive inhibitors of GCase is a promising field of research for the design of pharmacological chaperones as new therapeutic agents. Using a series of recently reported molecules with experimental binding affinities for GCase in the nanomolar to micromolar range, we here report an extensive theoretical analysis of their binding mode. On the basis of molecular docking, molecular dynamics, and binding free energy calculations using the linear interaction energy method (LIE), we provide details on the molecular interactions supporting ligand binding in the different families of compounds. The applicability of other computational approaches, such as the COMBINE methodology, is also investigated. The results show the robustness of the standard parametrization of the LIE method, which reproduces the experimental affinities with a mean unsigned error of 0.7 kcal/mol. Several structure−activity relationships are established using the computational models here provided, including the identification of hot spot residues in the binding site. The models derived are envisaged as important tools in ligand-design programs for GCase inhibitors.Financial support from the “Ministerio de Ciencia e Innovación”, Spain (Project CTQ2008-01426/BQU) and “Generalitat de Catalunya” (Grant 2009SGR-1072) is acknowledged. J.Å. acknowledges support from the Swedish Research Council (VR). L.D. is grateful to CSIC for predoctoral research training support within the JAE-Predoc program. H.G.T. is a researcher of the Isidro Parga Pondal program (Xunta de Galicia, Spain). Mr. Lars Boukharta is gratefully acknowledged for technical assistance and helpful discussions. Finally, the authors acknowledge the “Centre de Supercomputació de Catalunya” (CESCA) for allowing the use of its software and hardware resources.Peer reviewe