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Sanjib Senapati

Publications and source records attributed to Sanjib Senapati.

3 recordsLinked to original sources

In-situ synthesis of a tacrine-triazole-based inhibitor of acetylcholinesterase: configurational selection imposed by steric interactions.

Recently, researchers have used acetylcholinesterase (AChE) as a reaction vessel to synthesize its own inhibitors. Thus, 1 (syn-TZ2PA6), a femtomolar AChE inhibitor, which is formed in a 1:1 mixture with its anti-isomer by solution phase reaction from 3 (TZ2) and 4 (PA6), can be synthesized exclusively inside the AChE gorge. Our computational approach based on quantum mechanical/molecular mechanical (QM/MM) calculations, molecular dynamics (MD), and targeted molecular dynamics (TMD) studies answers why 1 is the sole product in the AChE environment. Ab initio QM/MM results show that the reaction in the AChE gorge occurs when 3/azide and 4/acetylene are extended in a parallel orientation. An MD simulation started from the final structure of QM/MM calculations keeps the azide's and acetylene's parallel orientations intact for 10 ns of simulation time. A TMD simulation applied on an antiparallel azide-acetylene conformation flips the acetylene easily to bring it to a position that is parallel to azide. A second set of QM/MM calculations performed on this flipped structure generates a similar minimum-energy path as obtained previously. Even a TMD simulation carried out on a parallel azide-acetylene conformation could not deform their parallel arrangement. All of these results, thus, imply that inside the AChE gorge, the azide group of 3 and the acetylene group of 4 always remain parallel, with the consequence that 1 is the only product. The architecture of the gorge plays an important role in this selective formation of 1.

Acetylcholinesterase↗

Induced fit in mouse acetylcholinesterase upon binding a femtomolar inhibitor: a molecular dynamics study.

A molecular dynamics simulation of mouse acetylcholinesterase (mAChE) complexed with syn-TZ2PA6, a femtomolar AChE inhibitor, is compared to a simulation of unliganded mAChE. The simulation of the complex was initiated by placing the inhibitor in its bound conformation of the crystal complex into a structure of unliganded mAChE selected from preliminary protein-ligand docking results. During a 2 ns period, the enzyme subsequently displayed a substantial "induced fit" response to yield a conformation very similar to that obtained by crystallography (Bourne et al. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 1449-1454). In this conformation of unique nature, the Trp 286 side chain of the enzyme flips out of the hydrophobic core and becomes highly solvent exposed. The imidazole ring of His 287 is almost orthogonal relative to its position in the unliganded enzyme, creating a stable pi stacking arrangement with the Trp 286 side chain. Other major deviations among the active site residues include side chain conformational changes of Trp 86, Tyr 133, Tyr 337, and Phe 338. These residues in the complex deviate from their positions in unliganded mAChE to better accommodate the inhibitor in the active site gorge.

Acetylcholinesterase↗

Finite concentration effects on diffusion-controlled reactions.

The algorithm by Northrup, Allison, and McCammon [J. Chem. Phys. 80, 1517 (1984)] has been used for two decades for calculating the diffusion-influenced rate-constants of enzymatic reactions. Although many interesting results have been obtained, the algorithm is based on the assumption that substrate-substrate interactions can be neglected. This approximation may not be valid when the concentration of the ligand is high. In this work, we constructed a simulation model that can take substrate-substrate interactions into account. We first validated the model by carrying out simulations in ways that could be compared to analytical theories. We then carried out simulations to examine the possible effects of substrate-substrate interactions on diffusion-controlled reaction rates. For a substrate concentration of 0.1 mM, we found that the diffusion-controlled reaction rates were not sensitive to whether substrate-substrate interactions were included. On the other hand, we observed significant influence of substrate-substrate interactions on calculated reaction rates at a substrate concentration of 0.1M. Therefore, a simulation model that takes substrate-substrate interactions into account is essential for reliably predicting diffusion-controlled reaction rates at high substrate concentrations, and one such simulation model is presented here.

Computer Simulation↗