Identification of sulfonamide based butyrylcholinesterase inhibitors through scaffold hopping approach.

Butyrylcholinesterase (BChE), a hydrolytic enzyme, is responsible for the termination of the action of acetylcholine besides acetylcholinesterase (AChE) in the synaptic cleft of the brain. The alteration in the enzyme level, in patients with the progression of Alzheimer's disease, makes it a therapeutic target. In the present study, we developed BChE inhibitors through scaffold hopping by exploring two previously reported compounds, i.e., 1,4-bis((4-chlorophenyl) sulfonyl)-3,6-diphenylpiperazine-2,5-dione and N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide, to afford scaffold and pharmacophore fragments, respectively. The N,2-diphenyl-2-(phenylsulfonamido)acetamide derivatives, thus designed, were synthesised and screened for the inhibition of AChE and BChE enzymes. Compounds 30 and 33 were found to be most active against BChE among the derivatives, with IC50 values of 7.331 ± 0.946 and 10.964 ± 0.936 µM, respectively. The compounds displayed a non-competitive mode of inhibition along with BBB permeability and good cell viability on SH-SY5Y cell line. The molecular docking analysis of the compounds with BChE showed interactions with Trp82, Trp231, Leu286, and His438. The molecular dynamics study revealed the stability of the protein-ligand complexes.

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors.

Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC50 values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC50 values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.

Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors.

Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced [Formula: see text] and [Formula: see text] values of 0.94, 0.635 and 0.634, respectively.