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 A_2A Receptor

Abstract

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 A2A and A3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites

“…We have already demonstrated that the autoMEP vectors can be used as interesting molecular descriptors in different 3D-QSAR applications. [24][25][26][27][28] In this context, we have also reported that pyrazolotriazolo-pyrimidine is a versatile scaffold to cover a large spectrum of the adenosine receptor selectivity. In particular, pyrazolo-triazolo-pyrimidines bearing specific substitutions at the N 5 and N 8 positions have been described as highly potent and selective human A 3 R antagonists while the position N 7 shifts the selectivity profile to the human A 2A R subtype.…”

“…[19][20][21][22][23] Moving from these examples, 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-triazolopyrimidine analogs. [24][25][26][27][28] 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. The statistical quality of both training and validation models are very encouraging.…”

GPCRHomology Model Development and Application

“…To address this, a number of groups have sought to develop docking-based QSAR models [98,109,[117][118][119][120][121][122][123][124]. A combination of ligand-supported homology modeling and 3D-QSAR models has been applied to the discovery of A2a adenosine receptor antagonists, for example [124]. The authors used autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with partial least squares (PLS) analysis as an alternative linear 3D-QSAR tool to CoM-FA [125].…”

A Novel Generalized 3D‐QSAR Model of Camptothecin Analogs

“…Otherwise, molecular docking can "exhaustively" explore the conformational space of a ligand inside its binding cavity, inferring on the possible ligand "bioactive" conformation, even if the robustness of the different scoring functions to estimate ligand-target binding affinities is still unfortunately very feeble, in particular when the 3D structure of the target molecule comes from homology modeling techniques or from low resolution X-ray data. [3] Since bioactive conformation represents the crucial starting point of all three dimensional Quantitative Structure Activity Relationship (3D-QSAR) strategies, as for example Comparative Molecular Field Analysis (CoMFA) or 3D-pharmacophore search, molecular docking might represent the natural "structural" input of a conventional 3D-QSAR when no experimental "bioactive" conformer information is available, or when any structural superimposition protocol, expected by CoMFA, is not achievable. [3] In this study, we have selected as peculiar key-study an ensemble of Camptothecin (CPT) analogs classified as human DNA Topoisomerase I (Top1) selective inhibitors.…”

“…[3] Since bioactive conformation represents the crucial starting point of all three dimensional Quantitative Structure Activity Relationship (3D-QSAR) strategies, as for example Comparative Molecular Field Analysis (CoMFA) or 3D-pharmacophore search, molecular docking might represent the natural "structural" input of a conventional 3D-QSAR when no experimental "bioactive" conformer information is available, or when any structural superimposition protocol, expected by CoMFA, is not achievable. [3] In this study, we have selected as peculiar key-study an ensemble of Camptothecin (CPT) analogs classified as human DNA Topoisomerase I (Top1) selective inhibitors. Interestingly, the dataset has been recently analyzed by Hansch and Verma using a classical 2D-QSAR study.…”