Magdalena Bacilieri

Ligand-Based Homology Modeling as Attractive Tool to Inspect GPCR Structural Plasticity

G protein-coupled receptors (GPCRs) represent the largest family known of signal-transducing molecules. They convey signals for light and many extracellular regulatory molecules. GPCRs have been found to be dysfunctional/dysregulated in a growing number of human diseases and they have been estimated to be the targets of more than 40% of the drugs used in clinical medicine today. The crystal structure of rhodopsin provides the first three-dimensional GPCR information, which now supports homology modeling studies and structure-based drug design approaches. Here, we review our recent work on adenosine receptors, a family of GPCRs and, in particular, on A(3) adenosine receptor subtype antagonists. We will focus on an alternative approach to computationally explore the multi-conformational space of the antagonist-like state of the human A(3) receptor. We define ligand-based homology modeling as new approach to simulate the reorganization of the receptor induced by the ligand binding. The success of this approach is due to the synergic interaction between theory and experiment.

Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs.

A database of 106 human A3 adenosine receptor antagonists was used to derive two alternative PLS models: one starting from CoMFA descriptors and the other starting from the autocorrelation descriptors. The peculiarity of this work is the introduction of autocorrelation vectors as molecular descriptors for the PLS analysis. The autocorrelation allows comparing molecules (and their properties) with different structures and with different spatial orientation without any previous alignment. In particular, Molecular Electrostatic Potential (MEP) was the property computed and its information encoded in autocorrelation vectors. The 3D spatial distribution and the values of the electrostatic potential is in fact largely responsible for the binding of a substrate to its receptor binding site. Validation was done with an external test set and the results of the two models were compared. Interestingly, our preliminary results seem to indicate that this new alternative approach could robustly compete with the already well consolidated CoMFA approach. In particular, we have suggested that it could be a very interesting tool to filter large structural database in several virtual screening applications.

A2B adenosine receptor antagonists: recent developments.

There are pharmacological evidences that A(2B) receptors are involved in inflammatory processes, such as asthma. For this reason, many efforts has been made for identifying selective A(2B) antagonists as anti-asthmatic agents. The updated material related to this field has been rationalised and arranged in order to offer an overview of the topic.

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor.

The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven homology modeling approach, we have built a model of the human adenosine A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine antagonists finding a good agreement between the theoretical and the experimental predictions.

Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine antagonists binding sites.

G Protein-coupled receptors (GPCRs) selectivity is an important aspect of drug discovery process, and distinguishing between related receptor subtypes is often the key to therapeutic success. Nowadays, very few valuable computational tools are available for the prediction of receptor subtypes selectivity. In the present study, we present an alternative application of the Support Vector Machine (SVM) and Support Vector Regression (SVR) methodologies to simultaneously describe both A(2A)R versus A(3)R subtypes selectivity profile and the corresponding receptor binding affinities. We have implemented an integrated application of SVM-SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential (autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolo-pyrimidine analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and receptor binding affinity profiles.

Prediction of the aqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis

Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.

Ligand-based drug design methodologies in drug discovery process: an overview.

Ligand-based drug design represents an important research field in the drug discovery and optimisation process. This review provides an overview about the theoretical background of the quantitative structure activity relationship (QSAR) models.

Pyrazolo-triazolo-pyrimidines as adenosine receptor antagonists: A complete structure–activity profile

In the last 5 years, many efforts have been conducted searching potent and selective human A3 adenosine antagonists. In this field several different classes of compounds, possessing very good affinity (nM range) and with a broad range of selectivity, have been proposed. Recently, our group synthesized a new series of pyrazolo-triazolo-pyrimidines bearing different substitutions at the N5 and N8 positions, which have been described as highly potent and selective human A3 adenosine receptor antagonists. The present review summarizes available data and provides an overview of the structure–activity relationships found for this class of human A3 adenosine receptor antagonists.

Tandem 3D-QSARs approach as a valuable tool to predict binding affinity data: design of new Gly/NMDA receptor antagonists as a key study.

Quantitative structure-activity relationships (QSARs) represent a very well consolidated computational approach to correlate structural or property descriptors of chemical compounds with their chemical or biological activities. We have recently reported that autocorrelation Molecular Electrostatic Potential (autoMEP) vectors in combination to Partial Least-Square (PLS) analysis or to Response Surface Analysis (RSA) can represent an interesting alternative 3D-QSAR strategy. In the present paper, we would like to present how the applicability of in tandem linear and nonlinear 3D-QSAR methods (autoMEP/PLS&RSA) can help to predict binding affinity data of a new set of N-methyl-d-aspartate (Gly/NMDA) receptor antagonists.

G protein-coupled receptors as challenging druggable targets: insights from in silico studies

The successful identification of hundreds of G protein-coupled receptors (GPCRs) represents the single greatest opportunity for novel drug development today. The crystal structure of rhodopsin provides the first information on the three-dimensional structure of GPCRs, which now supports homology modeling studies and structure-based drug-design approaches. We review our recent work on adenosine receptors, a family of GPCRs. Focusing our attention on A3 adenosine receptor, we have demonstrated that the reciprocal integration of different theoretical and experimental disciplines can be very useful for the successful design of new, potent and selective receptor ligands.