Sequence Analysis, Kinetic Constants, and Anion Inhibition Profile of the Nacrein-Like Protein (CgiNAP2X1) from the Pacific Oyster Magallana gigas (Ex-Crassostrea gigas)

A simple yet multifaceted 90 years old, evergreen enzyme: Carbonic anhydrase, its inhibition and activation

Advances in the carbonic anhydrase (CA, EC 4.2.1.1) research over the last three decades are presented, with an emphasis on the deciphering of the activation mechanism, the development of isoform-selective inhibitors/ activators by the tail approach and their applications in the management of obesity, hypoxic tumors, neurological conditions, and as antiinfectives.

An anion and small molecule inhibition study of the β-carbonic anhydrase from Staphylococcus aureus

Pathogenic bacteria resistant to most antibiotics, including the methicillin-resistant Staphylococcus aureus (MRSA) represent a serious medical problem. The search for new antiinfectives, possessing a diverse mechanism of action compared to the clinically used antibiotics, has become an attractive research field. S. aureus DNA encodes a β-class carbonic anhydrase, SauBCA. It is a druggable target that can be inhibited by certain aromatic and heterocyclic sulphonamides. Here we investigated inorganic anions and some other small molecules for their inhibition of SauBCA. The halides, nitrite, nitrate, bicarbonate, carbonate, bisulphite, sulphate, stannate, and N,N-diethyldithiocarbamate were submillimolar SauBCA inhibitors with K_Is in the range of 0.26 − 0.91 mM. The most effective inhibitors were sulfamide, sulfamate, phenylboronic acid, and phenylarsonic acid with K_Is of 7 − 43 µM. Several interesting inhibitors detected here may be considered lead compounds for the development of even more effective derivatives, which should be investigated for their bacteriostatic effects.

A novel proteochemometrics model for predicting the inhibition of nine carbonic anhydrase isoforms based on supervised Laplacian score and k-nearest neighbour regression

Carbonic anhydrases (CAs) are essential enzymes in biological processes. Prediction of the activity of compounds towards CA isoforms could be evaluated by computational techniques to discover a novel therapeutic inhibitor. Studies such as quantitative structure-activity relationships (QSARs), molecular docking and pharmacophore modelling have been carried out to design potent inhibitors. Unfortunately, QSAR does not consider the information of target space in the model. We successfully developed an in silico proteochemometrics model that simultaneously uses target and ligand descriptors to predict the activities of CA inhibitors. Herein, a strong predictive model was built for the prediction of protein-ligand binding affinity between nine human CA isoforms and 549 ligands. We applied descriptors obtained from the PROFEAT webserver for the proteins. Ligands were encoded by descriptors from PaDEL-Descriptor software. Supervised Laplacian score (SLS) and particle swarm optimization were used for feature selection. Models were derived using k-nearest neighbour (KNN) regression and a kernel smoother model. The predictive ability of the models was evaluated by an external validation test. Statistical results (Q²_ext = 0.7806, r²_test = 0.7811 and RMSE_test = 0.5549) showed that the model generated using SLS and KNN regression outperformed the other models. Consequently, the selectivity of compounds towards these enzymes will be predicted prior to synthesis.