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Abstract

Benchmarking Docking Protocols for Virtual Screenings of Novel Acetylcholinesterase Inhibitors

Author(s): E. Mateev*, M. Georgieva and A. Zlatkov
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Sofia, Sofia 1431, Bulgaria

Correspondence Address:
E. Mateev, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Sofia, Sofia 1431, Bulgaria, E-mail: [email protected]


Molecular docking is emerging as a frequently applied structure-based virtual technique in the drug design processes. The method could significantly reduce the time required for the development of novel and effective molecules compared to high-throughput screening. However, a major drawback of the docking simulations is the high number of false-positive ligands in the top-ranked solutions. Thus, this work focuses on the optimization of genetic optimization for GOLD, and Glide docking protocols in the active sites of crystallographic acetylcholinesterase proteins, which could be used in the processes of design and optimization of novel acetylcholinesterase inhibitors. The performance of GOLD and Glide was assessed by their ability to reproduce known inhibitor conformations of co-crystallized ligands, and to efficiently detect known active compounds seeded into a decoy set. In addition, ensemble docking and molecular mechanics with generalised Born and surface area solvation (MM/GBSA) recalculations were introduced to observe the alterations in the enrichment factors. The variances in the enrichment values between both docking softwares were significant, considering the weak performance of Glide. In all of the employed crystallographic structures, GOLD 5.3 showed drastically better results. Interestingly, the enrichment factors were not increased after utilizing the ensemble docking simulations and the free binding energy recalculations with MM/GBSA. Overall, it was noted that the application of ChemPLP (GOLD 5.3) scoring function in a single acetylcholinesterase protein structure (PDB: 1Q84) displays the most reliable docking results. The obtained data will be beneficial for future virtual screenings of novel acetylcholinesterase inhibitors.

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