Exploring the chemical space of influenza neuraminidase inhibitors

Abstract

The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value in which IC50 < 1µM and ≥ 10µM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure–activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.

Structure-Based Optimization of N-Substituted Oseltamivir Derivatives as Potent Anti-Influenza A Virus Agents with Significantly Improved Potency against Oseltamivir-Resistant N1-H274Y Variant

“…3 ). 13 C NMR (100 MHz, CDCl 3 ): δ 192. 30,159.35,137.80,130.10,123.08,121.96,114.32,80.43,25.94, (192.2580).…”

“…. 13 C NMR (100 MHz, CDCl 3 ): δ 170. 62, 166.52, 158.90, 141.92, 137.21, 129.39, 129.34, 120.10, 115.87, 114.39, 81.75, 80.03, 74.65, 60.83, 55.78, 53.69, 50.61, 30.46, 26.16, 26.05, 26.03, 25.77, 23.68, 14.22, 9.57, 9.49, 9.39 85, 158.69, 137.82, 131.38, 128.76, 115.94, 81.47, 79.44, 75.32, 73.12, 56.65, 54.38, 52.14, 46.74, 26.05, 25.98, 25.52, 23.82, 9.88, 9.82, 9.33.…”

“…Among them, oseltamivir (1a), the first orally available NAI, was marketed in 2000, and has been stockpiled by many countries as a precaution against a new pandemic, primarily because of its convenient oral availability. 13 Despite the success of oseltamivir, resistance is a growing concern. Oseltamivir resistance is generally conferred via a single amino acid mutation (H274Y) in strains possessing the N1 subtype of NA (exemplified by H1N1pdm09-H274Y and H5N1-H274Y).…”

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties

“…Rigorous 10-fold CV and independent validation test with ten independent rounds of these classifiers based on the optimal feature subset are reported in Table 6 and Figure 5. The more details of the parameter optimization of these three classifiers were described in the works [37,38,[55][56][57][58][59][60][61]. Based the independent validation test, we noticed that the Ac, MCC and auROC values of iQSP were higher than those of other classifiers by >2%, >4%, and >2%, respectively, suggesting that iQSP holds very high potential to provide an accurate and reliable result in unseen peptides when compared to the existing methods and the conventional classifiers developed in this study.…”

Potential New H1N1 Neuraminidase Inhibitors from Ferulic Acid and Vanillin: Molecular Modelling, Synthesis and in Vitro Assay

“…Both FA and OTV have alkoxy group (methoxy in FA and isopentoxy in OTV) at C1. This feature is suggested to interact with the nonpolar side chains of Ile222, Arg224 and Glu276 in the hydrophobic pocket of NA28. At C5, there is also a similar feature between FA and OTV in term of the presence of carboxylate group (FA–propenoate and in OTV–methanoate) which may contribute to either electrostatic or hydrogen bond interactions (HBA/HBD).…”