Naslov
Molecular docking study of quinuclidine derivatives against cholinesterases using machine learning

Autori
Mikelić, Ana ; Primožič, Ines ; Hrenar, Tomica

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book of Abstracts / Marković, Dean ; Meštrović, Ernest ; Namjesnik, Danijel ; Tomašić, Vesna - Zagreb : Hrvatsko kemijsko društvo, 2021, 155-155

Skup
27. hrvatski skup kemičara i kemijskih inženjera (27HSKIKI)

Mjesto i datum
Veli Lošinj, Hrvatska, 05.10.2021. - 08.10.2021

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
molecular docking ; ab initio molecular dynamics ; machine learning, principal component analysis ; quinuclidine derivatives ; cholinesterases

Sažetak
Quinuclidine-based carbamates proved to be potent dual inhibitors of both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) and can therefore be considered as potential central nervous system agents [1]. To gain better understanding of the drug-biomolecular interactions, extensive molecular docking simulations of quinuclidine derivatives and cholinesterases were performed using ab initio molecular dynamics (MD) coupled with extensive machine learning protocol. Binding modes of the following 3-substituted quinuclidine compounds were investigated: 3-(N, N- dimethylcarbamoyloxy)quinuclidine and 3-(N, N- diethylcarbamoyloxy)quinuclidine, as well as their quaternary N-methyl derivatives . The dimensionality of the MD trajectories was first reduced by extracting only the necessary coordinates to describe the movement of the substrate within the enzyme's active site. Obtained trajectories were then further reduced in dimension by the 2nd-order tensor decomposition. In this reduced space, probability distributions (PD) of molecular geometries were calculated [2]. By finding all strict local maxima in PD functions, every possible binding mode of investigated compounds was determined. During the course of simulations strict local maxima plateaus in PD functions were calculated for each compound. The points at which the full configuration spaces were sampled were found by the machine learning protocol. For each molecule the minimal number of steps in MD simulations and principal components to obtain full configurational spaces was determined. For chosen Michaelis complexes QM/QM optimizations were performed and standard Gibbs energies of binding were calculated.

Izvorni jezik
Engleski

Znanstvena područja
Kemija

POVEZANOST RADA