用免疫信息学技术设计新型H1N1流感神经氨酸酶抗原表位肽

神经氨酸酶(NA)是一种具有唾液酸酶活性的流感毒粒表面糖蛋白,切断病毒HA与细胞膜上神经氨酸残基之间的连接,使病毒能够从宿主细胞表面释放.检测了新型H1N1流感NA基因核苷酸序列,用免疫信息学方法预测、筛选和鉴定B细胞表位;人工合成NA抗原多肽SE8和RE6,并免疫新西兰兔制备抗血清.两种抗血清具有体外中和新型H1N1流感病毒能力(微量中和实验),而且还具有拮抗血凝释放作用.根据2009年全球毒株NA基因序列检测和比对结果,发现多肽SE8和RE6序列未变异.实验揭示,多肽SE8和RE6可以作为新型H1N1流感候选多肽疫苗.     

Proteome-wide screening for designing a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis using immunoinformatic approaches

Abstract

Elizabethkingia anophelis is an emerging human pathogen causing neonatal meningitis, catheter-associated infections and nosocomial outbreaks with high mortality rates. Besides, they are resistant to most antibiotics used in empirical therapy. In this study, therefore, we used immunoinformatic approaches to design a prophylactic peptide vaccine against E. anophelis as an alternative preventive measure. Initially, cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes were predicted from the highest antigenic protein. The CTL and HTL epitopes together had a population coverage of 99.97% around the world. Eventually, six CTL, seven HTL, and two LBL epitopes were selected and used to construct a multi-epitope vaccine. The vaccine protein was found to be highly immunogenic, non-allergenic, and non-toxic. Codon adaptation and in silico cloning were performed to ensure better expression within E. coli K12 host system. The stability of the vaccine structure was also improved by disulphide bridging. In addition, molecular docking and dynamics simulation revealed strong and stable binding affinity between the vaccine and toll-like receptor 4 (TLR4) molecule. The immune simulation showed higher levels of T-cell and B-cell activities which was in coherence with actual immune response. Repeated exposure simulation resulted in higher clonal selection and faster antigen clearance. Nevertheless, experimental validation is required to ensure the immunogenic potency and safety of this vaccine to control E. anophelis infection in the future.

Communicated by Ramaswamy H. Sarma     

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

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Highlights

• •NNAlign_MA enables full deconvolution of single MHC specificities from MS assays.
• •NNAlign_MA expands MHC allelic coverage, improving identification of T-cell epitopes.
• •NNAlign_MA was benchmarked on MHC classes I and II, outperforming current methods.
• •NNAlign_MA offers a universal solution to analyze and exploit MHC peptidomics data.

     

Cation–π, amino–π, π–π, and H‐bond interactions stabilize antigen–antibody interfaces

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions—in particular B‐cell epitopes—but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen–antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B‐cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen–antibody interfaces were shown to differ from other protein–protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H‐bond, cation–π, amino–π, and π–π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino–π and π–π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen–antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes—albeit to a lesser extent—have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B‐cell epitope prediction. Proteins 2014; 82:1734–1746. © 2014 Wiley Periodicals, Inc.     

BepiPred‐3.0: Improved B‐cell epitope prediction using protein language models

B‐cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred‐3.0, a sequence‐based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user‐friendly interface to navigate the results.     

Metadherin peptides containing CD4+ and CD8+ T cell epitopes as a therapeutic vaccine candidate against cancer

The concept of peptide‐based vaccines against cancer has made noteworthy progress. Metadherin (MTDH) overexpression and its role in the development of diverse cancers make it an attractive target for cancer immunotherapy. In the current study, six different T cell epitope prediction tools were run to identify MTDH peptides with multiple immunogenic regions. Further, molecular docking was performed to assess HLA‐peptide binding interactions. Nine and eleven peptides fragments containing multiple CD8 ⁺ and CD4 ⁺ T‐cell epitopes, ranging from 9 to 20 amino acids, respectively, were obtained using a consensus immunoinformatics approach. The three peptides that were finally identified as having overlapping CD4 ⁺ and CD8 ⁺ T‐ cell epitopes are ARLREMLSVGLGFLRTELG, FLLGYGWAAACAGAR, YIDDEWSGLNGLSSADP. These peptides were found to not only have multiple T cell epitopes but also to have binding affinity with wide HLA molecules. A molecular docking study revealed that the predicted immunogenic peptides (with single or multiple T cell epitopes) of MTDH have comparable binding energies with naturally bound peptides for both HLA classes I and II. Thus, these peptides have the potential to induce immune responses that could be considered for developing synthetic peptide vaccines against multiple cancers.     

Combining proteomics and bioinformatics to explore novel tegumental antigens as vaccine candidates against Echinococcus granulosus infection

Echinococcus granulosus is the parasite responsible for cystic echinococcosis (CE), an important worldwide-distributed zoonosis. New effective vaccines against CE could potentially have great economic and health benefits. Here, we describe an innovative vaccine design scheme starting from an antigenic fraction enriched in tegumental antigens from the protoscolex stage (termed PSEx) already known to induce protection against CE. We first used mass spectrometry to characterize the protein composition of PSEx followed by Gene Ontology analysis to study the potential Biological Processes, Molecular Functions, and Cellular Localizations of the identified proteins. Following, antigenicity predictions and determination of conservancy degree against other organisms were determined. Thus, nine novel proteins were identified as potential vaccine candidates. Furthermore, linear B cell epitopes free of posttranslational modifications were predicted in the whole PSEx proteome through colocalization of in silico predicted epitopes within peptide fragments identified by matrix-assisted laser desorption/ionization-TOF/TOF. Resulting peptides were termed “clean linear B cell epitopes,” and through BLASTp scanning against all nonhelminth proteins, those with 100% identity against any other protein were discarded. Then, the secondary structure was predicted for peptides and their corresponding proteins. Peptides with highly similar secondary structure respect to their parental protein were selected, and those potentially toxic and/or allergenic were discarded. Finally, the selected clean linear B cell epitopes were mapped within their corresponding 3D-modeled protein to analyze their possible antibody accessibilities, resulting in 14 putative peptide vaccine candidates. We propose nine novel proteins and 14 peptides to be further tested as vaccine candidates against CE.     

Identification of B-cell Epitope of Leishmania donovani and its application in diagnosis of visceral leishmaniasis

Diagnosis of visceral leishmaniasis (VL) is often hindered by cross-reactions with antigens from other related parasite infections. This study aimed to develop an immunochromatographic test (ICT) which can detect the antigen present in circulating immune complexes (CICs) of VL patients using B-cell epitope-specific antibodies. MS analysis of six immunoreactive 2DE spots revealed two epitopes i.e. RFFVQGDGIGQHSLQEALERR (P₁) and RRVAVLVLLDRL (P₂) (From a hypothetical protein [Acc No: XP_003861458.1]). The epitope conservancy analysis suggested that the linear epitope (P₁P₂) is 97–100% conserved among Leishmania species and diverged from Homo sapiens (61% query coverage and 80% identity). Further, immunoinformatics analysis of hydrophilicity and flexibility confirmed the antigenicity of the peptide fragment. The linear epitope (P₁P₂) was synthesized (98% purity) and the purity was confirmed by high-performance liquid chromatography and MS. The indirect Enzyme linked immunosorbent assay results confirmed the presence of the corresponding antibody in VL patient’s sera but not in those of healthy and other diseases. The result demonstrated a sensitivity 90%; Se Cl95% (82.16–96.27)% and specificity 100%; Sp Cl95% (84.56–100)% which indicated the possibility to be used as a diagnostic tool. Sensitivity, specificity, and diagnostic efficiency of colloidal gold conjugated anti-P₁P₂ antibody ICT strip was 100, 95.2, and 96.7%, respectively, which is slightly better as compared to other ICT for VL. Though, our result indicated the utility of anti-P₁P₂ antibody to detect CICs epitopes, a large-scale inspection in endemic and non-endemic area and in different ethnic population is needed for its validation and authentication.     

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated (‘attenuated’) forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.     

Excavating SARS‐coronavirus 2 genome for epitope‐based subunit vaccine synthesis using immunoinformatics approach

Since the outbreak of severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) in December 2019 in China, there has been an upsurge in the number of deaths and infected individuals throughout the world, thereby leading to the World Health Organization declaration of a pandemic. Since no specific therapy is currently available for the same, the present study was aimed to explore the SARS‐CoV‐2 genome for the identification of immunogenic regions using immunoinformatics approach. A series of computational tools were applied in a systematic way to identify the epitopes that could be utilized in vaccine development. The screened‐out epitopes were passed through several immune filters, such as promiscuousity, conservancy, antigenicity, nonallergenicity, population coverage, nonhomologous to human proteins, and affinity with human leukocyte antigen alleles, to screen out the best possible ones. Further, a construct comprising 11 CD4, 12 CD8, 3 B cell, and 3 interferon‐γ epitopes, along with an adjuvant β‐defensin, was designed in silico, resulting in the formation of a multiepitope vaccine. The in silico immune simulation and population coverage analysis of the vaccine sequence showed its capacity to elicit cellular, humoral, and innate immune cells and to cover up a worldwide population of more than 97%. Further, the interaction analysis of the vaccine construct with Toll‐like receptor 3 (immune receptor) was carried out by docking and dynamics simulations, revealing high affinity, constancy, and pliability between the two. The overall findings suggest that the vaccine may be highly effective, and is therefore required to be tested in the lab settings to evaluate its efficacy.