ABSTRACT
AT1 receptor is an interesting biological target involved in several important diseases, such as blood hypertension and cardiovascular pathologies. In this study we investigated the main electrostatic and steric features of a series of AT1 antagonists related to hypertensive activity using structure and ligand-based strategies (docking and CoMFA). The generated 3D model had good internal and external consistency and was used to predict the potency of an external test set. The predicted values of pIC50 are in good agreement with the experimental results of biological activity, indicating that the 3D model can be used to predict the biological property of untested compounds. The electrostatic and steric CoMFA maps showed molecular recognition patterns, which were analyzed with structure-based molecular modeling studies (docking). The most and the least potent compounds docked into the AT1 binding site were subjected to molecular dynamics simulations with the aim to verify the stability and the flexibility of the ligand-receptor interactions. These results provided valuable insights on the electronic/structural requirements to design novel AT1 antagonists.
Subject(s)
Angiotensin II Type 1 Receptor Blockers/chemistry , Angiotensin II Type 1 Receptor Blockers/pharmacology , Antihypertensive Agents/chemistry , Antihypertensive Agents/pharmacology , Molecular Docking Simulation , Molecular Dynamics Simulation , Receptor, Angiotensin, Type 1/chemistry , Receptor, Angiotensin, Type 1/drug effects , Static Electricity , Angiotensin II Type 1 Receptor Blockers/metabolism , Antihypertensive Agents/metabolism , Binding Sites , Blood Pressure/drug effects , Computer-Aided Design , Drug Design , Ligands , Molecular Structure , Protein Binding , Protein Conformation , Quantitative Structure-Activity Relationship , Receptor, Angiotensin, Type 1/metabolism , Renin-Angiotensin System/drug effectsABSTRACT
A quantitative structure-activity relationship analysis was employed to explore the relationship between the molecular structure of thiosemicarbazone analogues and the inhibition of the cysteine protease cruzain, a validated target for Chagas' disease treatment. A data set containing 53 thiosemicarbazone derivatives was used to produce a quantitative model for activity prediction of unknown compounds. Several electronic descriptors were obtained through DFT calculations, along with a large amount of Dragon descriptors. The ordered predictor selection (OPS) algorithm was employed to select the most relevant descriptors to perform PLS regressions. With this procedure, significant correlation coefficients (r(2) = 0.85, q(2) = 0.78) were achieved. Furthermore, predicted values for an external test set are in good agreement with the experimental results, indicating the potential of the model for untested compounds. Additional validation tests were carried out, indicating that a robust and reliable model was obtained to be used in the design of new thiosemicarbazones with improved cruzain inhibition potential.
