The model of prediction of the inhibition of neuraminidases of influenza A and B based on a reduced set of energy terms

Abstract

The overall model for prediction of IC₅₀ values for inhibitors of neuraminidase influenza virus A and B has been created. It combines data about IC₅₀ values of complexes of 40 variants of neuraminidases of influenza A (7 serotypes) and B and three known inhibitors (oseltamivir, zanamivir, peramivir). The model also uses only data of enthalpy contributions to the potential energy of inhibitor/protein and substrate (MUNANA)/protein complexes. The calculation procedures are ported to use software with support of GPU accelerators, that significant decrease the computation time. The corresponding correlation coefficient (R²) for pIC₅₀ prediction was within 0.45-0.58, the SEM values of around 0.7 (the range of used pIC₅₀ data set is from 4.55 to 10.22).

Prediction of the Inhibition of Influenza Virus Neuraminidase Various Strains by Means of a Generalized Model Constructed Using the Data on the Position of Known Ligands

“…This work represents a logic continuation of our earlier studies aimed at the development of models for predicting the IC 50 value (concentration causing 50% inhibition) for influenza virus neuraminidase inhibitors [1][2][3]. The search for new anti-influenza drugs continues.…”

“…The following data on the structure and inhibitory activity (the IC 50 value) of chemical compounds have been used in this study: The general plan on modeling the complexes was described earlier [2,3]. Development of predictive models was carried out using the parameters of the complexes calculated by the MM-PBSA (MM-GBSA) method.…”

“…Earlier, we have shown that it is possible to construct predictive equations based on a set of experimentally determined (by X-ray structural analysis) and modeled variants of the structure of neuraminidases of various strains [2]. In addition, it was shown [3] that the use of the componentwise enthalpy contributions to the change in the free energy of the complex, calculated by the MMPBSA method [5], was sufficient to create models that allow dividing the set of ligands into weak, medium, and strong inhibitors. This work considers the possibility of using a priori information about the position of already known ligands to improve the predictive power of the model.…”