CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Potent and broadly neutralizing antibodies against sarbecoviruses induced by sequential COVID-19 vaccination

The current SARS-CoV-2 variants strikingly evade all authorized monoclonal antibodies and threaten the efficacy of serum-neutralizing activity elicited by vaccination or prior infection, urging the need to develop antivirals against SARS-CoV-2 and related sarbecoviruses. Here, we identified both potent and broadly neutralizing antibodies from a five-dose vaccinated donor who exhibited cross-reactive serum-neutralizing activity against diverse coronaviruses. Through single B-cell sorting and sequencing followed by a tailor-made computational pipeline, we successfully selected 86 antibodies with potential cross-neutralizing ability from 684 antibody sequences. Among them, PW5-570 potently neutralized all SARS-CoV-2 variants that arose prior to Omicron BA.5, and the other three could broadly neutralize all current SARS-CoV-2 variants of concern, SARS-CoV and their related sarbecoviruses (Pangolin-GD, RaTG13, WIV-1, and SHC014). Cryo-EM analysis demonstrates that these antibodies have diverse neutralization mechanisms, such as disassembling spike trimers, or binding to RBM or SD1 to affect ACE2 binding. In addition, prophylactic administration of these antibodies significantly protects nasal turbinate and lung infections against BA.1, XBB.1, and SARS-CoV viral challenge in golden Syrian hamsters, respectively. Importantly, post-exposure treatment with PW5-5 and PW5-535 also markedly protects against XBB.1 challenge in these models. This study reveals the potential utility of computational process to assist screening cross-reactive antibodies, as well as the potency of vaccine-induced broadly neutralizing antibodies against current SARS-CoV-2 variants and related sarbecoviruses, offering promising avenues for the development of broad therapeutic antibody drugs.

Cross-reactive MHC class I T cell epitopes may dictate heterologous immune responses between respiratory viruses and food allergens

Respiratory virus infections play a major role in asthma, while there is a close correlation between asthma and food allergy. We hypothesized that T cell-mediated heterologous immunity may induce asthma symptoms among sensitized individuals and used two independent in silico pipelines for the identification of cross-reactive virus- and food allergen- derived T cell epitopes, considering individual peptide sequence similarity, MHC binding affinity and immunogenicity. We assessed the proteomes of human rhinovirus (RV1b), respiratory syncytial virus (RSVA2) and influenza-strains contained in the seasonal quadrivalent influenza vaccine 2019/2020 (QIV 2019/2020), as well as SARS-CoV-2 for human HLA alleles, in addition to more than 200 most common food allergen protein sequences. All resulting allergen-derived peptide candidates were subjected to an elaborate scoring system considering multiple criteria, including clinical relevance. In both bioinformatics approaches, we found that shortlisted peptide pairs that are potentially binding to MHC class II molecules scored up to 10 × lower compared to MHC class I candidate epitopes. For MHC class I food allergen epitopes, several potentially cross-reactive peptides from shrimp, kiwi, apple, soybean and chicken were identified. The shortlisted set of peptide pairs may be implicated in heterologous immune responses and translated to peptide immunization strategies with immunomodulatory properties.

In silico epitope prediction and evolutionary analysis reveals capsid mutation patterns for enterovirus B

Enterovirus B (EVB) is a common species of enterovirus, mainly consisting of Echovirus (Echo) and Coxsackievirus B (CVB). The population is generally susceptible to EVB, especially among children. Since the 21st century, EVB has been widely prevalent worldwide, and can cause serious diseases, such as viral meningitis, myocarditis, and neonatal sepsis. By using cryo-electron microscopy, the three-dimensional (3D) structures of EVB and their uncoating receptors (FcRn and CAR) have been determined, laying the foundation for the study of viral pathogenesis and therapeutic antibodies. A limited number of epitopes bound to neutralizing antibodies have also been determined. It is unclear whether additional epitopes are present or whether epitope mutations play a key role in molecular evolutionary history and epidemics, as in influenza and SARS-CoV-2. In the current study, the conformational epitopes of six representative EVB serotypes (E6, E11, E30, CVB1, CVB3 and CVB5) were systematically predicted by bioinformatics-based epitope prediction algorithm. We found that their epitopes were distributed into three clusters, where the VP1 BC loop, C-terminus and VP2 EF loop were the main regions of EVB epitopes. Among them, the VP1 BC loop and VP2 EF loop may be the key epitope regions that determined the use of the uncoating receptors. Further molecular evolution analysis based on the VP1 and genome sequences showed that the VP1 C-terminus and VP2 EF loop, as well as a potential "breathing epitope" VP1 N-terminus, were common mutation hotspot regions, suggesting that the emergence of evolutionary clades was driven by epitope mutations. Finally, footprints showed mutations were located on or near epitopes, while mutations on the receptor binding sites were rare. This suggested that EVB promotes viral epidemics by breaking the immune barrier through epitope mutations, but the mutations avoided the receptor binding sites. The bioinformatics study of EVB epitopes may provide important information for the monitoring and early warning of EVB epidemics and developing therapeutic antibodies.

Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding

Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains.

Prediction of B cell epitopes in proteins using a novel sequence similarity-based method

Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast.

Antigenic characterization of influenza and SARS-CoV-2 viruses

Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.

Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical–chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Emerging Concepts in Immune Thrombotic Thrombocytopenic Purpura

Immune thrombotic thrombocytopenic purpura (iTTP) is an autoimmune disorder of which the etiology is not fully understood. Autoantibodies targeting ADAMTS13 in iTTP patients have extensively been studied, the immunological mechanisms leading to the breach of tolerance remain to be uncovered. This review addresses the current knowledge on genetic factors associated with the development of iTTP and the interplay between the patient's immune system and environmental factors in the induction of autoimmunity against ADAMTS13. HLA-DRB1*11 has been identified as a risk factor for iTTP in the Caucasian population. Interestingly, HLA-DRB1*08:03 was recently identified as a risk factor in the Japanese population. Combined in vitro and in silico MHC class II peptide presentation approaches suggest that an ADAMTS13-derived peptide may bind to both HLA-DRB1*11 and HLA-DRB1*08:03 through different anchor-residues. It is apparent that iTTP is associated with the presence of infectious microorganisms, viruses being the most widely associated with development of iTTP. Infections may potentially lead to loss of tolerance resulting in the shift from immune homeostasis to autoimmunity. In the model we propose in this review, infections disrupt the epithelial barriers in the gut or lung, promoting exposure of antigen presenting cells in the mucosa-associated lymphoid tissue to the microorganisms. This may result in breach of tolerance through the presentation of microorganism-derived peptides that are homologous to ADAMTS13 on risk alleles for iTTP.

SAS: A Platform of Spike Antigenicity for SARS-CoV-2

Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.

Reverse vaccinology approach for the identifications of potential vaccine candidates against Salmonella

Salmonella is a leading cause of foodborne pathogen which causes intestinal and systemic diseases across the world. Vaccination is the most effective protection against Salmonella, but the identification and design of an effective broad-spectrum vaccine is still a great challenge, because of the multi-serotypes of Salmonella. Reverse vaccinology is a new tool to discovery and design vaccine antigens combining human immunology, structural biology and computational biology with microbial genomics. In this study, reverse vaccinology, an in-silico approach was established to screen appropriate immunogen targets by calculating the immunogenicity score of 583 non-redundant outer membrane and secreted proteins of Salmonella. Herein among 100 proteins identified with top-ranked scores, 15 representative antigens were selected randomly. Applying the sequence conservation test, four proteins (FliK, BcsZ, FhuA and FepA) remained as potential vaccine candidates for in vivo evaluation of immunogenicity and immunoprotection. All four candidates were capable to trigger the immune response and stimulate the production of antiserum in mice. Furthermore, top-ranked proteins including FliK and BcsZ provided wide antigenic coverage among the multi-serotype of Salmonella. The S. Typhimurium LT2 challenge model used in mice immunized with FliK and BcsZ showed a high relative percentage survival (RPS) of 52.74 % and 64.71 % respectively. In conclusion, this study constructed an in-silico pipeline able to successfully pre-screen the vaccine targets characterized by high immunogenicity and protective immunity. We show that reverse vaccinology allowed screening of appropriate broad-spectrum vaccines for Salmonella.

Predicting the Antigenic Relationship of Foot-and-Mouth Disease Virus for Vaccine Selection Through a Computational Model

Foot-and-mouth disease virus (FMDV) is an antigenic-variable RNA virus that is responsible for the recurrence of foot-and-mouth disease in livestock and can be prevented and controlled using a vaccine with broad-spectrum protection. Current anti-genicity evaluation methods, which involve animal immunity experiments and serum preparation, are unable to fulfill the needs of high-throughput antigenicity measurements. This study designed an antigenicity scoring model to rapidly predict the antigenicity of FMDV. Antigenic-dominant sites were initially determined on the VP1 protein, a position-specific scoring matrix and physical chemical indexes were integrated to generate antigenicity descriptors. Independent tests showed a high accuracy of 0.848 and an AUC value of 0.889, indicating the good performance of the model in antigenicity measurement. When applying this model to historical data, annual antigenicity coverage of widely used vaccine strains was successfully evaluated, this was also supported by previous experiments. Furthermore, the utility of this model was extended to select potential broad-spectrum vaccines among 1,201 historical non-redundant strains to recommend potential univalent, bivalent and trivalent vaccine candidates. The results suggested that the computational model designed in this study could be used for the high-throughput antigenicity measurement of FMDV and could aid in vaccine development for preventing FMDV epidemics.

Application of AI and IoT in Clinical Medicine: Summary and Challenges

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2

Background

An outbreak of infection caused by SARS-CoV-2 recently has brought a great challenge to public health. Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of pandemic. This study aimed to predict the protective epitopes with bioinformatics methods and resources for vaccine development.

Methods

The genome sequence and protein sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database. ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DiscoTope 2.0 program. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forecasted epitopes were characterized for immunogen potential evaluation.

Results

A total of 63 sequential B-cell epitopes on spike protein were predicted and 4 peptides (Spike_315–324, Spike_333–338, Spike_648–663, Spike_1064–1079) exhibited high antigenicity score and good surface accessibility. Ten residues within spike protein (Gly⁴⁹⁶, Glu⁴⁹⁸, Pro⁴⁹⁹, Thr⁵⁰⁰, Leu¹¹⁴¹, Gln¹¹⁴², Pro¹¹⁴³, Glu¹¹⁴⁴, Leu¹¹⁴⁵, Asp¹¹⁴⁶) are forecasted as components of discontinuous B-cell epitopes. The bioinformatics analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind MHC class I and MHC class II molecules respectively. The peptides (Nucleocapsid_66–75, Nucleocapsid_104–112) were predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules.

Conclusions

B-cell epitopes on spike protein and T-cell epitopes within nucleocapsid protein were identified and recommended for developing a protective vaccine against SARS-CoV-2.

A benchmark dataset of protein antigens for antigenicity measurement

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.

SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens

B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.

In silico analysis as a strategy to identify candidate epitopes with human IgG reactivity to study Porphyromonas gingivalis virulence factors

Porphyromonas gingivalis (Pg) is one of the main pathogens in chronic periodontitis (CP). Studies on the immunogenicity of its virulence factors may contribute to understanding the host response to infection. The present study aimed to use in silico analysis as a tool to identify epitopes from Lys-gingipain (Kgp) and neuraminidase virulence factors of the Pg ATCC 33277 strain. Protein sequences were obtained from the NCBI Protein Database and they were scanned for amino acid patterns indicative of MHC II binding using the MHC-II Binding Predictions tool from the Immune Epitope Database (IEDB). Peptides from different regions of the proteins were chemically synthesized and tested by the indirect ELISA method to verify IgG immunoreactivity in serum of subjects with CP and without periodontitis (WP). T cell epitope prediction resulted in 16 peptide sequences from Kgp and 18 peptide sequences from neuraminidase. All tested Kgp peptides exhibited IgG immunoreactivity whereas tested neuraminidase peptides presented low IgG immunoreactivity. Thus, the IgG reactivity to Kgp protein could be reaffirmed and the low IgG reactivity to Pg neuraminidase could be suggested. The novel peptide epitopes from Pg were useful to evaluate its immunoreactivity based on the IgG-mediated host response. In silico analysis was useful for preselecting epitopes for immune response studies in CP.

In-silico Antigenicity Determination and Clustering of Dengue Virus Serotypes

Emerging or re-emerging dengue virus (DENV) causes dengue fever epidemics globally. Current DENV serotypes are defined based on genetic clustering, while discrepancies are frequently observed between the genetic clustering and the antigenicity experiments. Rapid antigenicity determination of DENV mutants in high-throughput way is critical for vaccine selection and epidemic prevention during early outbreaks, where accurate prediction methods are seldom reported for DENV. Here, a highly accurate and efficient in-silico model was set up for DENV based on possible antigenicity-dominant positions (ADPs) of envelope (E) protein. Independent testing showed a high performance of our model with AUC-value of 0.937 and accuracy of 0.896 through quantitative Linear Regression (LR) model. More importantly, our model can successfully detect those cross-reactions between inter-serotype strains, while current genetic clustering failed. Prediction cluster of 1,143 historical strains showed new DENV clusters, and we proposed DENV2 should be further classified into two subgroups. Thus, the DENV serotyping may be re-considered antigenetically rather than genetically. As the first algorithm tailor-made for DENV antigenicity measurement based on mutated sequences, our model may provide fast-responding opportunity for the antigenicity surveillance on DENV variants and potential vaccine study.