Abstract
Antigenicity measurement plays a fundamental role in vaccine design, which requires antigen selection from a large number of mutants. To augment traditional cross-reactivity experiments, computational approaches for predicting the antigenic distance between multiple protein antigens are highly valuable. The performance of in silico models relies heavily on large-scale benchmark datasets, which are scattered among public databases and published articles or reports. Here, we present the first benchmark dataset of protein antigens with experimental evidence to guide in silico antigenicity calculations. This dataset includes (1) standard haemagglutination-inhibition (HI) tests for 3,867 influenza A/H3N2 strain pairs, (2) standard HI tests for 559 influenza virus B strain pairs, and (3) neutralization titres derived from 1,073 Dengue virus strain pairs. All of these datasets were collated and annotated with experimentally validated antigenicity relationships as well as sequence information for the corresponding protein antigens. We anticipate that this work will provide a benchmark dataset for in silico antigenicity prediction that could be further used to assist in epidemic surveillance and therapeutic vaccine design for viruses with variable antigenicity.
Highlights
Background & SummaryAntigenicity measurements between mutated antigens are essential for the design of immunological agents for treating infectious[1] and oncological diseases[2]
Protein antigens possessing highly similar epitopes often cross-react with the same or similar antibodies, which is commonly observed in viral pathogens such as human immunodeficiency virus (HIV)[3,4] and seasonal influenza virus (IV)[5,6,7,8]
Comprehensive serological tests have been performed on both experimental animals and vaccinated or infected patients to identify the serological relationship between the subtypes of Dengue virus (DENV)[11]
Summary
Antigenicity measurements between mutated antigens are essential for the design of immunological agents for treating infectious[1] and oncological diseases[2]. The quantification of antigenicity differences between mutated antigens relies heavily on experiments such as antibody- or antiserum-binding assays[6,10] or the counting of amino acid mutations at essential antigenic sites Among these experimental approaches, the HI test has traditionally been performed to determine the antigenic variations between current circulating influenza virus strains and candidate vaccines[6]. There have been multiple efforts aimed at antigenic distance prediction between influenza vaccines and circulating strains by generating theoretical models based on the sequence or the structure of antigen proteins. To construct an in silico model, a benchmark dataset should include two major components for antigenicity measurement: (i) sequence or structure information for protein antigens and (ii) the experimentally validated quantitative or qualitative antigenic relationship between the two protein antigens being compared. Given the extensive scope of antigenic clustering[9], vaccine failure detection[16] and broad-spectrum vaccine design[9], the benchmark datasets presented here could guide the development of in silico approaches for antigenicity monitoring and the selection of potential broad-spectrum vaccines
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