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DEVELOPMENT OF VIRTUAL SCREENING MODELS FOR THE IDENTIFICATION OF NEW SELECTIVE ACETYLCHOLINESTERASE INHIBITORS. Anuradha K., Pran Kishore, D., Poonam P., Raghuram Rao, A.

DEVELOPMENT OF VIRTUAL SCREENING MODELS FOR THE IDENTIFICATION OF NEW SELECTIVE ACETYLCHOLINESTERASE INHIBITORS.

Anuradha K., Pran Kishore, D., Poonam P., Raghuram Rao, A.


International Journal of Natural Product Science 2012: Spl Issue 1:107.

Abstract(RBIP-107)

Acetylcholinesterase (AChE) inhibition is an important research topic because of its wide range of associated health implications. Structure based drug design has emerged as a complementary method to high-throughput screening (HTS) of large chemical databases in terms of time-efficiency and cost-effectiveness. In the present work, while correlating docking scores with inhibitory potencies of known ligands, feeble robustness of scoring functions towards prediction was observed. This prompted us to develop a receptor-specific scoring function (new prediction models) using stepwise regression analysis based on consensus of different docking and their scoring methods (GOLD, LigandFit and GLIDE), for predicting binding affinities for human AChE (huAChE) inhibitors. Quantitative structure activity relationships were developed between the AChE inhibitory activity (pIC50) (n=91) and docking scores and docking descriptors. QSAR models have been validated internally [using leave-one-out cross validation method (r2cv)] and externally [using test set compounds] to ensure the predictive capacity of the models. Interestingly, from the docking studies of the acetylcholinesterase inhibitors, it has also been observed that the benzyl ring of E2020 (co-crystal structure) forms a л- л interaction with indole ring of Trp84 in the catalytic active site (CAS), indanone ring of E2020 forms a л- л interaction with the indole ring of Trp286 and charged nitrogen undergoes a cation- л interaction with the phenyl ring of Phe330 in the peripheral active site (PAS) of tcAChE. Strong stastical correlations were found between predicted and observed affinities for both the training and testing set. Results confirm the potential of our methodology for an in silico high-throughput screening and development of new chemical entities as potential AChE inhibitors.
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