In-silico method for predicting infectious strains of Influenza A virus from its genome and protein sequences

Abstract

AbstractInfluenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. Virus gradually undergoes host adaptive mutations in their genome and proteins, resulting in different strain s/vari ants which might spread virus from avians/mammals to humans. In this study, we have developed an in-silico models to identify infectious strains of Influenza A virus, which has the potential of getting transmitted to humans, from its whole genome/proteins. Firstly, machine learning based models were developed for predicting infectious strains using composition of 15 proteins of virus. Random Forest based model of protein Hemagglutinin, achieved maximum AUC 0.98 on validation data using dipeptide composition. Secondly, we obtained maximum AUC of 0.99 on validation dataset using one-hot-encoding features of each protein of virus. Thirdly, models build on DNA composition of whole genome of Influenza A, achieved maximum AUC 0.98 on validation dataset. Finally, a web-based service, named “FluSPred”(https://webs.iiitd.edu.in/raghava/fluspred/) has been developed which incorporate best 16 models (15 proteins and one based on genome) for prediction of infectious strains of virus. In addition, we provided standalone software for the prediction and scanning of infectious strains at large-scale (e.g., metagenomics) from genomic/proteomic data. We anticipate this tool will help researchers in prioritize high-risk viral strains of novel influenza virus possesses the capability to spread human to human, thereby being useful for pandemic preparedness and disease surveillance.Key PointsInfluenza A is a contagious viral disease responsible for four pandemics.Virus can cross species barrier and infect human beings.In silico models developed for predicting human infectious strains of virus.Models developed were build using 15 proteins and whole genome datasets.Webserver and standalone package for predicting and scanning of high-risk viral strains.