Abstract
Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC50). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/.
Highlights
Nipah virus infection is an emerging zoonotic infectious disease caused by Nipah virus (NiV)
The data, representing inhibitory concentration 50 (IC50), effective concentration 50 (EC50), percentage inhibition and viral titers against NiV was obtained from 17 PMIDs and 01 patent
We used the inhibitors with IC50, because it is considered as a standard for calculating the inhibition efficiency of any inhibitor and used in developing various algorithms (Chauhan et al, 2014; Qureshi et al, 2018; Rajput and Kumar, 2018)
Summary
Nipah virus infection is an emerging zoonotic infectious disease caused by Nipah virus (NiV). It is one of the important public health concerns in the South East Asian Region. The NiV is a negative sense single stranded RNA virus, belongs to genus Henipavirus and is a member of Paramyxoviridae family (Wang et al, 2001). The first outbreak of NiV was reported form Malaysia during 1998–1999 and thereafter-yearly outbreaks have been reported from Bangladesh or India (http://www.searo.who.int/entity/emerging_diseases/links/nipah_virus_outbreaks_sear/en/). NiV is known to infect various hosts viz., bats, pig, dog, cat, horse, and humans whereas fruit bats (genus Pteropus) remain as the main reservoir. The human-to-human transmission can be seen within families and in health care workers (Chadha et al, 2006; Luby et al, 2009)
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