Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.

Peng Wang,Lihong Peng,Wen Zhu,Jialiang Yang,Bo Liao,Lijun Cai

doi:10.3389/fmicb.2018.02500

Abstract

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies.

Highlights

According to the United States Centers for Disease Control and Prevention (CDC), seasonal influenza and its linked respiratory diseases cause approximately 650,000 deaths annually worldwide, posing a serious threat to human health and socio-economic environment (WHO, 2017)
We propose a novel algorithm called matrix completion with antigen and antiserum similarity (MCAAS), which integrates antigen sequence information and antiserum information in a low-rank matrix completion model to predict influenza antigenicity
H3N2 influenza data are used in this study (Smith et al, 2004), which is a partially revealed hemagglutination inhibition (HI) table consisting of 253 viruses and 79 vaccine from 1967 to 2003, i.e., a matrix of 253 rows and 79 columns

Summary

INTRODUCTION

According to the United States Centers for Disease Control and Prevention (CDC), seasonal influenza and its linked respiratory diseases cause approximately 650,000 deaths annually worldwide, posing a serious threat to human health and socio-economic environment (WHO, 2017). Neher et al proposed an optimization model for interpreting known antigen data and studied its ability to predict future influenza virus population composition (Neher et al, 2016) These methods rely on the reliability of rapidly changing antigen-associate sites (Sun et al, 2013). Smith et al proposed antigenic cartography for visualizing and predicting antigenic evolution of influenza viruses (Smith et al, 2004) They first transformed the known values in the HI table to Euclidean distances and embedded them into a 2D map using the modified multidimensional scaling (MDS) method. We explored the relationship between the genetic and antigenic evolution of the influenza virus in H3N2 data

MATERIALS AND METHODS

A Sliding Window Method

RESULTS