High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
Matched MeSH terms: Influenza A Virus, H1N1 Subtype/enzymology*
The 2009 pandemic influenza A(H1N1) infection in Malaysia was first reported in May 2009 and oseltamivir was advocated for confirmed cases in postexposure prophylaxis. However, there are cases of oseltamivir-resistance reported among H1N1-positive patients in other countries. Resistance is due to substitution of histidine by tyrosine at residue 275 (H275Y) of neuraminidase (NA). In this study, we have employed Sanger sequencing method to investigate the occurrence of mutations in NA segments of 67 pandemic 2009 A(H1N1) viral isolates from Malaysian patients that could lead to probable oseltamivir resistance. The sequencing analysis did not yield mutation at residue 275 for all 67 isolates indicating that our viral isolates belong to the wild type and do not confer resistance to oseltamivir.
Matched MeSH terms: Influenza A Virus, H1N1 Subtype/enzymology
We report the computational and experimental efforts in the design and synthesis of novel neuraminidase (NA) inhibitors from ferulic acid and vanillin. Two proposed ferulic acid analogues, MY7 and MY8 were predicted to inhibit H1N1 NA using molecular docking. From these two analogues, we designed, synthesised and evaluated the biological activities of a series of ferulic acid and vanillin derivatives. The enzymatic H1N1 NA inhibition assay showed MY21 (a vanillin derivative) has the lowest IC50 of 50 μM. In contrast, the virus inhibition assay showed MY15, a ferulic acid derivative has the best activity with the EC50 of ~0.95 μM. Modelling studies further suggest that these predicted activities might be due to the interactions with conserved and essential residues of NA with ΔGbind values comparable to those of oseltamivir and zanamivir, the two commercial NA inhibitors.
Matched MeSH terms: Influenza A Virus, H1N1 Subtype/enzymology*