BlockLogo: visualization of peptide and sequence motif conservation

DiMA: sequence diversity dynamics analyser for viruses

Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic, and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. DiMA provides a quantitative overview of sequence (DNA/RNA/protein) diversity by use of Shannon's entropy corrected for size bias, applied via a user-defined k-mer sliding window to an input alignment file, and each k-mer position is dissected to various diversity motifs. The motifs are defined based on the probability of distinct sequences at a given k-mer alignment position, whereby an index is the predominant sequence, while all the others are (total) variants to the index. The total variants are sub-classified into the major (most common) variant, minor variants (occurring more than once and of incidence lower than the major), and the unique (singleton) variants. DiMA allows user-defined, sequence metadata enrichment for analyses of the motifs. The application of DiMA was demonstrated for the alignment data of the relatively conserved Spike protein (2,106,985 sequences) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the relatively highly diverse pol gene (2637) of the human immunodeficiency virus-1 (HIV-1). The tool is publicly available as a web server (https://dima.bezmialem.edu.tr), as a Python library (via PyPi) and as a command line client (via GitHub).

plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style

BACKGROUND

The visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learning, Python is currently the most popular programming language. The programming language used by bioinformaticians began to shift to Python. Providing APIs in Python that are similar to those in R can reduce the learning cost of relearning a programming language. And compared to ggplot2 in R, drawing framework is not as easy to use in Python. The appearance of plotnine (ggplot2 in Python version) makes it possible to unify the programming methods of bioinformatics visualization tools between R and Python.

RESULTS

Here, we introduce plotnineSeqSuite, a new plotnine-based Python package provides a ggseqlogo-like API for programmatic drawing of sequence logos, sequence alignment diagrams and sequence histograms. To be more precise, it supports custom letters, color themes, and fonts. Moreover, the class for drawing layers is based on object-oriented design so that users can easily encapsulate and extend it.

CONCLUSIONS

plotnineSeqSuite is the first ggplot2-style package to implement visualization of sequence -related graphs in Python. It enhances the uniformity of programmatic plotting between R and Python. Compared with tools appeared already, the categories supported by plotnineSeqSuite are much more complete. The source code of plotnineSeqSuite can be obtained on GitHub ( https://github.com/caotianze/plotnineseqsuite ) and PyPI ( https://pypi.org/project/plotnineseqsuite ), and the documentation homepage is freely available on GitHub at ( https://caotianze.github.io/plotnineseqsuite/ ).

Cryptic association of B7-2 molecules and its implication for clustering

T-cell co-stimulation through CD28/CTLA4:B7-1/B7-2 axis is one of the extensively studied pathways that resulted in the discovery of several FDA-approved drugs for autoimmunity and cancer. However, many aspects of the signaling mechanism remain elusive, including oligomeric association and clustering of B7-2 on the cell surface. Here, we describe the structure of the IgV domain of B7-2 and its cryptic association into 1D arrays that appear to represent the pre-signaling state of B7-2 on the cell membrane. Super-resolution microscopy experiments on heterologous cells expressing B7-2 and B7-1 suggest, B7-2 form relatively elongated and larger clusters compared to B7-1. The sequence and structural comparison of other B7 family members, B7-1:CTLA4 and B7-2:CTLA-4 complex structures, support our view that the observed B7-2 1D zipper array is physiologically important. This observed 1D zipper-like array also provides an explanation for its clustering, and upright orientation on the cell surface, and avoidance of spurious signaling.

Logomaker: beautiful sequence logos in Python

Summary

Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures.

Availability and implementation

Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

Local adaptive evolution of two distinct clades of Beijing and T families of Mycobacterium tuberculosis in Chongqing: a Bayesian population structure and phylogenetic study

Background

Beijing sub-pedigree 2 (BSP2) and T sub-lineage 6 (TSL6) are two clades belonging to Beijing and T family of Mycobacterium tuberculosis (MTB), respectively, defined by Bayesian population structure analysis based on 24-loci mycobacterial interspersed repetitive unit-variable number of tandem repeats (MIRU-VNTR). Globally, over 99% of BSP2 and 89% of TSL6 isolates were distributed in Chongqing, suggesting their possible local adaptive evolution. The objective of this paper is to explore whether BSP2 and TSL6 originated by their local adaptive evolution from the specific isolates of Beijing and T families in Chongqing.

Methods

The genotyping data of 16 090 MTB isolates were collected from laboratory collection, published literatures and SITVIT database before subjected to Bayesian population structure analysis based on 24-loci MIRU-VNTR. Spacer Oligonucleotide Forest (Spoligoforest) and 24-loci MIRU-VNTR-based minimum spanning tree (MST) were used to explore their phylogenetic pathways, with Bayesian demographic analysis for exploring the recent demographic change of TSL6.

Results

Phylogenetic analysis suggested that BSP2 and TSL6 in Chongqing may evolve from BSP4 and TSL5, respectively, which were locally predominant in Tibet and Jiangsu, respectively. Spoligoforest showed that Beijing and T families were genetically distant, while the convergence of the MIRU-VNTR pattern of BSP2 and TSL6 was revealed by WebLogo. The demographic analysis concluded that the recent demographic change of TSL6 might take 111.25 years.

Conclusions

BSP2 and TSL6 clades might originate from BSP4 and TSL5, respectively, by their local adaptive evolution in Chongqing. Our study suggests MIRU-VNTR be combined with other robust markers for a more comprehensive genotyping approach, especially for families of clades with the same MIRU-VNTR pattern.

Lipid droplet-associated kinase STK25 regulates peroxisomal activity and metabolic stress response in steatotic liver

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are emerging as leading causes of liver disease worldwide and have been recognized as one of the major unmet medical needs of the 21st century. Our recent translational studies in mouse models, human cell lines, and well-characterized patient cohorts have identified serine/threonine kinase (STK)25 as a protein that coats intrahepatocellular lipid droplets (LDs) and critically regulates liver lipid homeostasis and progression of NAFLD/NASH. Here, we studied the mechanism-of-action of STK25 in steatotic liver by relative quantification of the hepatic LD-associated phosphoproteome from high-fat diet-fed Stk25 knockout mice compared with their wild-type littermates. We observed a total of 131 proteins and 60 phosphoproteins that were differentially represented in STK25-deficient livers. Most notably, a number of proteins involved in peroxisomal function, ubiquitination-mediated proteolysis, and antioxidant defense were coordinately regulated in Stk25 ^-/- versus wild-type livers. We confirmed attenuated peroxisomal biogenesis and protection against oxidative and ER stress in STK25-deficient human liver cells, demonstrating the hepatocyte-autonomous manner of STK25's action. In summary, our results suggest that regulation of peroxisomal function and metabolic stress response may be important molecular mechanisms by which STK25 controls the development and progression of NAFLD/NASH.

ProteomicsBrowser: MS/proteomics data visualization and investigation

SUMMARY

Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data.

AVAILABILITY AND IMPLEMENTATION

ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clonal analysis of Salmonella-specific effector T cells reveals serovar-specific and cross-reactive T cell responses

To tackle the complexity of cross-reactive and pathogen-specific T cell responses against related Salmonella serovars, we used mass cytometry, unbiased single-cell cloning, live fluorescence barcoding, and T cell-receptor sequencing to reconstruct the Salmonella-specific repertoire of circulating effector CD4⁺ T cells, isolated from volunteers challenged with Salmonella enterica serovar Typhi (S. Typhi) or Salmonella Paratyphi A (S. Paratyphi). We describe the expansion of cross-reactive responses against distantly related Salmonella serovars and of clonotypes recognizing immunodominant antigens uniquely expressed by S. Typhi or S. Paratyphi A. In addition, single-amino acid variations in two immunodominant proteins, CdtB and PhoN, lead to the accumulation of T cells that do not cross-react against the different serovars, thus demonstrating how minor sequence variations in a complex microorganism shape the pathogen-specific T cell repertoire. Our results identify immune-dominant, serovar-specific, and cross-reactive T cell antigens, which should aid in the design of T cell-vaccination strategies against Salmonella.

Epitope-Binding Characteristics for Risk versus Protective DRB1 Alleles for Visceral Leishmaniasis

HLA-DRB1 is the major genetic risk factor for visceral leishmaniasis (VL). We used SNP2HLA to impute HLA-DRB1 alleles and SNPTEST to carry out association analyses in 889 human cases and 977 controls from India. NetMHCIIpan 2.1 was used to map epitopes and binding affinities across 49 Leishmania vaccine candidates, as well as across peptide epitopes captured from dendritic cells treated with crude Leishmania Ag and identified using mass spectrometry and alignment to amino acid sequences of a reference Leishmania genome. Cytokines were measured in peptide-stimulated whole blood from 26 cured VL cases and eight endemic healthy controls. HLA-DRB1*1501 and DRB1*1404/DRB1*1301 were the most significant protective and risk alleles, respectively, with specific residues at aa positions 11 and 13 unique to protective alleles. We observed greater peptide promiscuity in sequence motifs for 9-mer core epitopes predicted to bind to risk (*1404/*1301) compared with protective (*1501) DRB1 alleles. There was a higher frequency of basic amino acids in DRB1*1404/*1301-specific epitopes compared with hydrophobic and polar amino acids in DRB1*1501-specific epitopes at anchor residues pocket 4 and pocket 6, which interact with residues at DRB1 positions 11 and 13. Cured VL patients made variable, but robust, IFN-γ, TNF, and IL-10 responses to 20-mer peptides based on captured epitopes, with peptides based on DRB1*1501-captured epitopes resulting in a higher proportion (odds ratio 2.23, 95% confidence interval 1.17-4.25, p = 0.017) of patients with IFN-γ/IL-10 ratios > 2-fold compared with peptides based on DRB1*1301-captured epitopes. Our data provide insight into the molecular mechanisms underpinning the association of HLA-DRB1 alleles with risk versus protection in VL in humans.

New Mycobacterium tuberculosis Beijing clonal complexes in China revealed by phylogenetic and Bayesian population structure analyses of 24-loci MIRU-VNTRs

Beijing lineage of Mycobacterium tuberculosis constitutes the most predominant lineage in East Asia. Beijing epidemiology, evolutionary history, genetics are studied in details for years revealing probable origin from China followed by worldwide expansion, partially linked to higher mutation rate, hypervirulence, drug-resistance, and association with cases of mixed infections. Considering huge amount of data available for 24-loci Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats, we performed detailed phylogenetic and Bayesian population structure analyses of Beijing lineage strains in mainland China and Taiwan using available 24-loci MIRU-VNTR data extracted from publications or the SITVIT2 database (n = 1490). Results on genetic structuration were compared to previously published data. A total of three new Beijing clonal complexes tentatively named BSP1, BPS2 and BSP3 were revealed with surprising phylogeographical specificities to previously unstudied regions in Sichuan, Chongqing and Taiwan, proving the need for continued investigations with extended datasets. Such geographical restriction could correspond to local adaptation of these “ecological specialist” Beijing isolates to local human host populations in contrast with “generalist pathogens” able to adapt to several human populations and to spread worldwide.

Bayesian population structure analysis reveals presence of phylogeographically specific sublineages within previously ill-defined T group of Mycobacterium tuberculosis

Mycobacterium tuberculosis genetic structure, and evolutionary history have been studied for years by several genotyping approaches, but delineation of a few sublineages remains controversial and needs better characterization. This is particularly the case of T group within lineage 4 (L4) which was first described using spoligotyping to pool together a number of strains with ill-defined signatures. Although T strains were not traditionally considered as a real phylogenetic group, they did contain a few phylogenetically meaningful sublineages as shown using SNPs. We therefore decided to investigate if this observation could be corroborated using other robust genetic markers. We consequently made a first assessment of genetic structure using 24-loci MIRU-VNTRs data extracted from the SITVIT2 database (n = 607 clinical isolates collected in Russia, Albania, Turkey, Iraq, Brazil and China). Combining Minimum Spanning Trees and Bayesian population structure analyses (using STRUCTURE and TESS softwares), we distinctly identified eight tentative phylogenetic groups (T1-T8) with a remarkable correlation with geographical origin. We further compared the present structure observed with other L4 sublineages (n = 416 clinical isolates belonging to LAM, Haarlem, X, S sublineages), and showed that 5 out of 8 T groups seemed phylogeographically well-defined as opposed to the remaining 3 groups that partially mixed with other L4 isolates. These results provide with novel evidence about phylogeographically specificity of a proportion of ill-defined T group of M. tuberculosis. The genetic structure observed will now be further validated on an enlarged worldwide dataset using Whole Genome Sequencing (WGS).

A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding

Background

Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets.

Methods

We have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets.

Results

We applied this method to predict CD8⁺ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded.

Conclusions

We developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.

A systematic analysis of a broadly neutralizing antibody AR3C epitopes on Hepatitis C virus E2 envelope glycoprotein and their cross-reactivity

Background

Hepatitis C virus (HCV) belongs to Flaviviridae family of viruses. HCV represents a major challenge to public health since its estimated global prevalence is 2.8% of the world's human population. The design and development of HCV vaccine has been hampered by rapid evolution of viral quasispecies resulting in antibody escape variants. HCV envelope glycoprotein E1 and E2 that mediate fusion and entry of the virus into host cells are primary targets of the host immune responses.

Results

Structural characterization of E2 core protein and a broadly neutralizing antibody AR3C together with E1E2 sequence information enabled the analysis of B-cell epitope variability. The E2 binding site by AR3C and its surrounding area were identified from the crystal structure of E2c-AR3C complex. We clustered HCV strains using the concept of "discontinuous motif/peptide" and classified B-cell epitopes based on their similarity.

Conclusions

The assessment of antibody neutralizing coverage provides insights into potential cross-reactivity of the AR3C neutralizing antibody across a large number of HCV variants.

Shetti, a simple tool to parse, manipulate and search large datasets of sequences

Parsing and manipulating long and/or multiple protein or gene sequences can be a challenging process for experimental biologists and microbiologists lacking prior knowledge of bioinformatics and programming. Here we present a simple, easy, user-friendly and versatile tool to parse, manipulate and search within large datasets of long and multiple protein or gene sequences. The Shetti tool can be used to search for a sequence, species, protein/gene or pattern/motif. Moreover, it can also be used to construct a universal consensus or molecular signatures for proteins based on their physical characteristics. Shetti is an efficient and fast tool that can deal with large sets of long sequences efficiently. Shetti parses UniProt Knowledgebase and NCBI GenBank flat files and visualizes them as a table.

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses

FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. FluKB was built using a modular framework allowing the implementation of analytical workflows and includes standard search tools, such as keyword search and sequence similarity queries, as well as advanced tools for the analysis of sequence variability. The advanced analytical tools for vaccine discovery include visual mapping of T- and B-cell vaccine targets and assessment of neutralizing antibody coverage. FluKB supports the discovery of vaccine targets and the analysis of viral diversity and its implications for vaccine discovery as well as potential T-cell breadth and antibody cross neutralization involving multiple strains. FluKB is representation of a new generation of databases that integrates data, analytical tools, and analytical workflows that enable comprehensive analysis and automatic generation of analysis reports.

Genome-wide association study of IgG1 responses to the choline-binding protein PspC of Streptococcus pneumoniae

Streptococcus pneumoniae causes invasive pneumococcal disease. Delayed development of antibodies to S. pneumoniae in infancy is associated with the development of atopy and asthma. Pneumococcal surface protein C (PspC) is a vaccine candidate and variation in its choline-binding region is associated with invasive disease. This study examined 523 060 single-nucleotide polymorphisms in The Western Australian Pregnancy Cohort (Raine) Study to find loci influencing immunoglobulin G1 (IgG1) responses to PspC measured at age 14 years (n=1152). Genome-wide significance (top SNP rs9275596; P=3.1 × 10(-14)) was only observed at human leucocyte antigen (HLA). Imputed HLA amino-acid polymorphisms showed the strongest associations at positions DRB1 47 (P=3.2 × 10(-11)), 13SRG (P=9.8 × 10(-10)) and 11SP (P=9.8 × 10(-10)), and at DQA1 34 (P=6.4 × 10(-10)), DQB1 167R (P=9.3 × 10(-6)) and HLA-B 95 W (P=1.2 × 10(-9)). Conditional analyses showed independent contributions from DRB1 47 and DQB1 167R to the signal at rs9275596, supported by an omnibus test showing a strong signal for the haplotype DRB1_47_DQB1_167 (P=9.02 × 10(-15)). In silico analysis showed that DRB1 four-digit allele groups defined by DRB1 47F bind to a greater complexity of core 9-mer epitopes compared with DRB1 47Y, especially across repeats in the C-term choline-binding region. Consequent differences in CD4 T-cell help for IgG1 to PspC could have implications for vaccine design. Further analysis in other cohorts is merited.

Intrinsic disorder mediates hepatitis C virus core-host cell protein interactions

Viral proteins bind to numerous cellular and viral proteins throughout the infection cycle. However, the mechanisms by which viral proteins interact with such large numbers of factors remain unknown. Cellular proteins that interact with multiple, distinct partners often do so through short sequences known as molecular recognition features (MoRFs) embedded within intrinsically disordered regions (IDRs). In this study, we report the first evidence that MoRFs in viral proteins play a similar role in targeting the host cell. Using a combination of evolutionary modeling, protein-protein interaction analyses and forward genetic screening, we systematically investigated two computationally predicted MoRFs within the N-terminal IDR of the hepatitis C virus (HCV) Core protein. Sequence analysis of the MoRFs showed their conservation across all HCV genotypes and the canine and equine Hepaciviruses. Phylogenetic modeling indicated that the Core MoRFs are under stronger purifying selection than the surrounding sequence, suggesting that these modules have a biological function. Using the yeast two-hybrid assay, we identified three cellular binding partners for each HCV Core MoRF, including two previously characterized cellular targets of HCV Core (DDX3X and NPM1). Random and site-directed mutagenesis demonstrated that the predicted MoRF regions were required for binding to the cellular proteins, but that different residues within each MoRF were critical for binding to different partners. This study demonstrated that viruses may use intrinsic disorder to target multiple cellular proteins with the same amino acid sequence and provides a framework for characterizing the binding partners of other disordered regions in viral and cellular proteomes.

Bioinformatics for cancer immunotherapy target discovery

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

Big data analytics in immunology: a knowledge-based approach

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

Large-scale analysis of B-cell epitopes on influenza virus hemagglutinin - implications for cross-reactivity of neutralizing antibodies

Influenza viruses continue to cause substantial morbidity and mortality worldwide. Fast gene mutation on surface proteins of influenza virus result in increasing resistance to current vaccines and available antiviral drugs. Broadly neutralizing antibodies (bnAbs) represent targets for prophylactic and therapeutic treatments of influenza. We performed a systematic bioinformatics study of cross-reactivity of neutralizing antibodies (nAbs) against influenza virus surface glycoprotein hemagglutinin (HA). This study utilized the available crystal structures of HA complexed with the antibodies for the analysis of tens of thousands of HA sequences. The detailed description of B-cell epitopes, measurement of epitope area similarity among different strains, and estimation of antibody neutralizing coverage provide insights into cross-reactivity status of existing nAbs against influenza virus. We have developed a method to assess the likely cross-reactivity potential of bnAbs for influenza strains, either newly emerged or existing. Our method catalogs influenza strains by a new concept named discontinuous peptide, and then provide assessment of cross-reactivity. Potentially cross-reactive strains are those that share 100% identity with experimentally verified neutralized strains. By cataloging influenza strains and their B-cell epitopes for known bnAbs, our method provides guidance for selection of representative strains for further experimental design. The knowledge of sequences, their B-cell epitopes, and differences between historical influenza strains, we enhance our preparedness and the ability to respond to the emerging pandemic threats.