Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

Prenatal diagnosis and postnatal management of congenital mesoblastic nephroma: Experience at a single center in China.

OBJECTIVE: To review the prenatal and postnatal clinical characteristics and pathological subtypes, as well as the surgical outcome for congenital mesoblastic nephroma (CMN) cases. METHOD: A retrospective review was performed in 11 cases with CMN prenatally diagnosed at a single center between 2015 and 2019. The clinical characteristics, surgical outcome, histopathology, and follow-up were retrospectively obtained and reviewed. RESULTS: The median gestational age at which the sonographic diagnosis was made was 35 weeks. Polyhydramnios was found in four (36.4%) cases, and all resulted in a preterm birth. Nine infants had hypertension. Ten cases underwent radical nephrectomy, and one underwent radical nephrectomy and partial adrenalectomy. The pathological results showed that six tumors were classical variants, four mixed variants, and one was a cellular variant. Three cases presented as a stage I, eight as stage II, and no stage III or IV cases were diagnosed. All patients are alive so far. At a median follow-up of 14 months, no local recurrence, or remote metastases were found. CONCLUSION: The prognosis of prenatal CMN cases is excellent after early surgery.

Physical detection of influenza A epitopes identifies a stealth subset on human lung epithelium evading natural CD8 immunity.

Vaccines eliciting immunity against influenza A viruses (IAVs) are currently antibody-based with hemagglutinin-directed antibody titer the only universally accepted immune correlate of protection. To investigate the disconnection between observed CD8 T-cell responses and immunity to IAV, we used a Poisson liquid chromatography data-independent acquisition MS method to physically detect PR8/34 (H1N1), X31 (H3N2), and Victoria/75 (H3N2) epitopes bound to HLA-A*02:01 on human epithelial cells following in vitro infection. Among 32 PR8 peptides (8-10mers) with predicted IC50 < 60 nM, 9 were present, whereas 23 were absent. At 18 h postinfection, epitope copies per cell varied from a low of 0.5 for M13-11 to a high of >500 for M1(58-66) with PA, HA, PB1, PB2, and NA epitopes also detected. However, aside from M1(58-66), natural CD8 memory responses against conserved presented epitopes were either absent or only weakly observed by blood Elispot. Moreover, the functional avidities of the immunodominant M1(58-66)/HLA-A*02:01-specific T cells were so poor as to be unable to effectively recognize infected human epithelium. Analysis of T-cell responses to primary PR8 infection in HLA-A*02:01 transgenic B6 mice underscores the poor avidity of T cells recognizing M1(58-66). By maintaining high levels of surface expression of this epitope on epithelial and dendritic cells, the virus exploits the combination of immunodominance and functional inadequacy to evade HLA-A*02:01-restricted T-cell immunity. A rational approach to CD8 vaccines must characterize processing and presentation of pathogen-derived epitopes as well as resultant immune responses. Correspondingly, vaccines may be directed against "stealth" epitopes, overriding viral chicanery.

TANTIGEN: a comprehensive database of tumor T cell antigens.

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Prenatal diagnosis and early postnatal management of pyriform sinus cyst: experience at a single medical center in mainland China.

OBJECTIVE: The aim of this study was to determine whether prenatal diagnosis of pyriform sinus cyst can improve the prognosis of this disorder. METHODS: A retrospective review was performed in 15 neonates with a pyriform sinus cyst seen at a single center between 2010 and 2014. Among the 15 cases, the diagnosis was made prenatally in eight cases (PreD), while the diagnosis was made postnatally in seven cases (PostD). Neonatal outcome was compared in the two subgroups. RESULTS: The mean gestational age at diagnosis of PreD was 27 ± 6.8 weeks, while the mean age at admission of PostD was 10.1 ± 8.8 days. Cervical mass, fever, respiratory distress, and hoarseness were common symptoms. The mean duration of postoperative mechanical ventilation was 11.5 ± 13.9 and 100.71 ± 80.0 h, respectively, in PreD and PostD (p < 0.01). The average postoperative length of stay and the length of hospital stay were 11.3 ± 3.34 and 19.6 ± 4.41 days in PreD, and 15.14 ± 8.28 and 24.14 ± 8.51 days in PostD, respectively. CONCLUSION: Prenatal diagnosis and timely postnatal sequential intervention were associated with less complications and shortened duration of postoperative mechanical ventilation. © 2016 John Wiley & Sons, Ltd.

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells--a benchmarking study.

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.

Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation.

Targets of curative donor-derived graft-versus-myeloma (GVM) responses after allogeneic hematopoietic stem cell transplantation (HSCT) remain poorly defined, partly because immunity against minor histocompatibility Ags (mHAgs) complicates the elucidation of multiple myeloma (MM)-specific targets. We hypothesized that syngeneic HSCT would facilitate the identification of GVM-associated Ags because donor immune responses in this setting should exclusively target unique tumor Ags in the absence of donor-host genetic disparities. Therefore, in the present study, we investigated the development of tumor immunity in an HLA-A0201(+) MM patient who achieved durable remission after myeloablative syngeneic HSCT. Using high-density protein microarrays to screen post-HSCT plasma, we identified 6 Ags that elicited high-titer (1:5000-1:10 000) Abs that correlated with clinical tumor regression. Two Ags (DAPK2 and PIM1) had enriched expression in primary MM tissues. Both elicited Ab responses in other MM patients after chemotherapy or HSCT (11 and 6 of 32 patients for DAPK2 and PIM1, respectively). The index patient also developed specific CD8(+) T-cell responses to HLA-A2-restricted peptides derived from DAPK2 and PIM1. Peptide-specific T cells recognized HLA-A2(+) MM-derived cell lines and primary MM tumor cells. Coordinated T- and B-cell immunity develops against MM-associated Ags after syngeneic HSCT. DAPK1 and PIM1 are promising target Ags for MM-directed immunotherapy.

Transcriptionally abundant major histocompatibility complex class I alleles are fundamental to nonhuman primate simian immunodeficiency virus-specific CD8+ T cell responses.

Simian immunodeficiency virus (SIV)-infected macaques are the preferred animal model for human immunodeficiency virus (HIV) vaccines that elicit CD8(+) T cell responses. Unlike humans, whose CD8(+) T cell responses are restricted by a maximum of six HLA class I alleles, macaques express up to 20 distinct major histocompatibility complex class I (MHC-I) sequences. Interestingly, only a subset of macaque MHC-I sequences are transcriptionally abundant in peripheral blood lymphocytes. We hypothesized that highly transcribed MHC-I sequences are principally responsible for restricting SIV-specific CD8(+) T cell responses. To examine this hypothesis, we measured SIV-specific CD8(+) T cell responses in MHC-I homozygous Mauritian cynomolgus macaques. Each of eight CD8(+) T cell responses defined by full-proteome gamma interferon (IFN-γ) enzyme-linked immunospot (ELISPOT) assay were restricted by four of the five transcripts that are transcriptionally abundant (>1% of total MHC-I transcripts in peripheral blood lymphocytes). The five transcriptionally rare transcripts shared by these animals did not restrict any detectable CD8(+) T cell responses. Further, seven CD8(+) T cell responses were defined by identifying peptide binding motifs of the three most frequent MHC-I transcripts on the M3 haplotype. Combined, these results suggest that transcriptionally abundant MHC-I transcripts are principally responsible for restricting SIV-specific CD8(+) T cell responses. Thus, only a subset of the thousands of known MHC-I alleles in macaques should be prioritized for CD8(+) T cell epitope characterization.

Can perinatal outcomes of fetal omphalocele be improved at a tertiary center in South China?

OBJECTIVE: To assess the efficacy of positive feedback closed-loop management system (PFCMS) protocol in influencing parents' decision about pregnancy continuation in pregnancies diagnosed with omphalocele. METHODS: This was a retrospective cohort study of patients who were diagnosed with fetal omphalocele prior to 20 weeks' gestation by ultrasound and were referred to Fetal Care Center at a mainland Chinese medical center during an 11-year period. Two management strategies were offered during the two stages of the study period: a single consultant with a routine protocol and a multidisciplinary support team with PFCMS, respectively. We analyzed the two protocols influencing parents' decision about pregnancy continuation. RESULTS: Forty-nine patients diagnosed with fetal omphalocele were included in this study. In Group A including 16 patients with routine protocol during the first stage of the study period, the majority opted for termination, and only five continued the pregnancy. In Group B including 33 patients with PFCMS during the second stage of the study period, less than one third chose TOP, and 23 ended in live births. There was a significantly lower TOP rate in patients treated with the PFCMS protocol. CONCLUSION: The PFCMS protocol may be an efficient approach in managing pregnancies complicated by omphalocele, which may help in preventing unnecessary pregnancy terminations.

PB1-F2 Finder: scanning influenza sequences for PB1-F2 encoding RNA segments.

BACKGROUND: PB1-F2 is a major virulence factor of influenza A. This protein is a product of an alternative reading frame in the PB1-encoding RNA segment 2. Its presence of is dictated by the presence or absence of premature stop codons. This virulence factor is present in every influenza pandemic and major epidemic of the 20th century. Absence of PB1-F2 is associated with mild disease, such as the 2009 H1N1 ("swine flu"). RESULTS: The analysis of 8608 segment 2 sequences showed that only 8.5% have been annotated for the presence of PB1-F2. Our analysis indicates that 75% of segment 2 sequences are likely to encode PB1-F2. Two major populations of PB1-F2 are of lengths 90 and 57 while minor populations include lengths 52, 63, 79, 81, 87, and 101. Additional possible populations include the lengths of 59, 69, 81, 95, and 106. Previously described sequences include only lengths 57, 87, and 90. We observed substantial variation in PB1-F2 sequences where certain variants show up to 35% difference to well-defined reference sequences. Therefore this dataset indicates that there are many more variants that need to be functionally characterized. CONCLUSIONS: Our web-accessible tool PB1-F2 Finder enables scanning of influenza sequences for potential PB1-F2 protein products. It provides an initial screen and annotation of PB1-F2 products. It is accessible at http://cvc.dfci.harvard.edu/pb1-f2.

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.

BACKGROUND: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW. RESULTS: Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds. CONCLUSION: The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes.

BACKGROUND: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. RESULTS: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population. CONCLUSION: Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

UNLABELLED: Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AVAILABILITY: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

Prediction of supertype-specific HLA class I binding peptides using support vector machines.

Experimental approaches for identifying T-cell epitopes are time-consuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a prediction system based on a novel data representation of peptide/MHC interaction and support vector machines (SVM) for prediction of peptides that promiscuously bind to multiple Human Leukocyte Antigen (HLA, human MHC) alleles belonging to a HLA supertype. Ten-fold cross-validation results showed that the overall performance of SVM models is improved in comparison to our previously published methods based on hidden Markov models (HMM) and artificial neural networks (ANN), also confirmed by blind testing. At specificity 0.90, sensitivity values of SVM models were 0.90 and 0.92 for HLA-A2 and -A3 dataset respectively. Average area under the receiver operating curve (A(ROC)) of SVM models in blind testing are 0.89 and 0.92 for HLA-A2 and -A3 datasets. A(ROC) of HLA-A2 and -A3 SVM models were 0.94 and 0.95, validated using a full overlapping study of 9-mer peptides from human papillomavirus type 16 E6 and E7 proteins. In addition, a large-scale experimental dataset has been used to validate HLA-A2 and -A3 SVM models. The SVM prediction models were integrated into a web-based computational system MULTIPRED1, accessible at antigen.i2r.a-star.edu.sg/multipred1/.

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse.

PRED(BALB/c) is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I (H2-K(d), H2-L(d) and H2-D(d)) and class II (I-E(d) and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PRED(BALB/c) is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PRED(BALB/c) is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

PREDNOD, a prediction server for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse.

The non-obese diabetic (NOD) mouse is a widely used animal model for study of autoimmune diseases, in particular human type 1 diabetes mellitus (T1DM). Identification of the subset of peptides that bind MHC molecules comprising the H-2g7 haplotype of NOD mouse and thereby representing potential NOD T-cell epitopes is important for research into the pathogenesis and immunotherapy of T1DM. The H-2g7 haplotype comprises the MHC class-I molecules Kd and Db and a single class-II molecule I-Ag7. We have developed a prediction system, PREDNOD, for accurate identification of peptides that bind the MHC molecules constituting the H-2g7 haplotype. PREDNOD is accessible at http://antigen.i2r.a-star.edu.sg/Ag7.

Neural models for predicting viral vaccine targets.

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.

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.