Preliminary results of construction of overall model for prediction of IC50 value of ligands of influenza virus neuraminidase of any strain are presented. We used MM-PBSA (MM-GBSA) energy terms calculated for the complexes obtained after modeling of 30 variants of neuraminidase structures, subsequent docking and simulation of molecular dynamics as independent variables in prediction equations. The structures of known neuraminidase-inhibiting drugs (oseltamivir, zanamivir and peramivir) and a neuraminidase substrate (MUNANA) were used as ligands. The correlation equation based on calculated energetic parameters of inhibitor complexes with neuraminidase did not result in the prediction of IC50 with acceptable parameters (R2£0.3). However, if information about binding energy of the substrate used for neuraminidase assay (and IC50 detection) is included the resulting IC50 prediction equations become significant (R2³0.55). It is concluded that models based on IC50 values as a predictable variable and combining information about binding of different ligands to different variants of the target proteins must take into account the binding properties of the substrate (used for IC50 determination). The predictive power of such models depends critically on the quality of the modeling of the ligand-protein complexes.
Mikurova A.V., Skvortsov V.S. (2018) Creation of a generalized model prediction of inhibition of neuraminidase of influenza virus of various strains. Biomeditsinskaya Khimiya, 64(3), 247-252.
Mikurova A.V. et al. Creation of a generalized model prediction of inhibition of neuraminidase of influenza virus of various strains // Biomeditsinskaya Khimiya. - 2018. - V. 64. -N 3. - P. 247-252.
Mikurova A.V. et al., "Creation of a generalized model prediction of inhibition of neuraminidase of influenza virus of various strains." Biomeditsinskaya Khimiya 64.3 (2018): 247-252.
Mikurova, A. V., Skvortsov, V. S. (2018). Creation of a generalized model prediction of inhibition of neuraminidase of influenza virus of various strains. Biomeditsinskaya Khimiya, 64(3), 247-252.
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