B-cell Epitope Prediction Research Articles

In silico B-cell epitope prediction and molecular docking of Aspergillus allergens targeting improved ABPA diagnosis

Objective The objective of this study is to in silico predict Aspergillus fumigatus specific B-cell epitopes with a focus on enhancing Allergic Bronchopulmonary Aspergillosis (ABPA) diagnostic precision by using and to validate using molecular docking of Aspergillus fumigatus specific B-cell epitopes, aiming to overcome current serological and clinical method limitations and to support specific therapies and preventive strategies for better ABPA management. Methods The sequences of Asp f1, Asp f2, Asp f3, and Asp f4 from NCBI were analyzed using IEDB-AR for B-cell epitope prediction. Structural modeling and molecular docking analysis were conducted using MODELLER and HADDOCK, respectively, with visualization via PyMOL and PDBe PISA. Results For Asp f1, two IgE-specific (40-47) and four IgG-specific (33-76, 125-148) B-cell epitopes were predicted. Asp f3 had one IgG-specific epitope (47-73), and Asp f4 had two IgG-specific epitopes (52-133) with no IgE epitopes. Asp f2 had eight IgE-specific epitopes (56-63, 93-99, 136-146, 153-160, 185-194, 200-206, 229-239) with IgPred scores above 0.931 and no IgG-specific epitopes. Molecular docking with HADDOCK Z-scores showed strong interactions between IgE and Asp f1 and Asp f2 epitopes. PyMOL and PISA-EBI identified key residues: LYS43 in Asp f1 forms a salt bridge with the IgE heavy chain. In Asp f2, out of nineteen identified residues, six residues (LYS 94, ARG 153, ASP 200, ASP 204, ASP 207 and GLU 233) were confirmed as part of the predicted IgE epitopes, exhibiting significant interactions with IgE, in agreement with both PyMOL and PISA analysis. Conclusion This study aimed to enhance ABPA diagnostics by identifying key B-cell epitopes of Aspergillus fumigatus through in silico prediction and molecular docking, a way to support personalized therapies and preventive strategies in future.

Designing a multi-epitope construct using immuno-informatic tools to prepare a messenger RNA vaccine against Rhipicephalus microplus ticks

Background and Aim: Ticks are blood-feeding ectoparasites that transmit pathogens to animals and humans. One of the most important hard ticks in animals is Rhipicephalus microplus, which transmits Babesia and Anaplasma spp. Although many potential tick vaccine candidates have been identified, no effective vaccine that can provide sterile immunity against R. microplus tick infestations has been developed. This study aimed to design a construct using different computational tools to identify and predict immunogenic epitopes within protein sequences and to prepare a messenger RNA (mRNA) vaccine against R. microplus based on lipid nanoparticles (LNPs). Materials and Methods: The R. microplus proteins (Bm86, Subolesin, and ATAQ) were selected and their consensus sequence was obtained from the National Center for Biotechnology Information in FASTA format. The Immune Epitope Database and Analysis Resource (IEBD) server was used for the prediction of helper T-cell epitopes, the NetCTL 1.2 server was used to predict cytotoxic T-cell epitopes, and the ABCpred server was used for B-cell epitope prediction. Antigenicity testing, allergenicity assessment, and toxicity screening were immuno-informatic techniques used to identify potent epitopes within protein sequences. The multi-epitope construct was prepared and cloned into the pVAX1 plasmid. Plasmids were transformed in compatible competent cells, and restriction analysis was performed. After restriction analysis of the transformed plasmid, in vitro transcription was performed to prepare mRNA. The mRNA was purified, quantified, and converted into complementary DNA, and gene-specific primers were used to confirm the in vitro transcription of mRNA. A mixture of four lipids containing 1,2-dioleoyl-3-dimethylammonium-propane (DODAP), Distearoylphosphatidylcholine (DSPC, cholesterol, and 1,2-Dimyristoyl-sn-glycero-3-methoxypolyethylene glycol-2000 (DMG PEG-2000 was used to prepare LNPs. LNPs were characterized using a scanning electron microscope, Zeta potential, and Zeta Sizer tests. Results: More than 1000 epitopes were predicted, from which only nine helper T-lymphocytes, 18 cytotoxic T-lymphocytes, and nine B-cell epitopes of all three proteins were selected with high antigenic scores of 0.958 for Bm86, 0.752 for Subolesin, and 0.964 for ATAQ, respectively. An adjuvant was used to enhance immune responses, all of which were linked to one another using GPGPG, AAY, and KK linkers, respectively. The physiochemical properties predicted that the instability index of the construct would be <40%, indicating that the construct is stable. Plasmids were transformed in compatible competent cells, and white-transformed colonies were observed. Restriction analysis was performed, DNA was transcribed into mRNA, and LNPs were prepared and characterized. Conclusion: More than 1000 epitopes were predicted using immune informatic tools, and only high-scoring epitopes were selected. A multi-epitope construct was designed using bio-informatic tools, and its physicochemical properties were predicted. The design construct was inserted into the pVAX1 plasmid, and in vitro transcription was performed to prepare the mRNA. LNPs of mRNA were prepared and characterized to be used as vaccines. It was found that LNPs were stable and nanometer-sized. Keywords: immuno-informatic tools, lipid nanoparticles, multiepitope construct, Rhipicephalus microplus.

Using reverse vaccinology techniques combined with B-cell epitope prediction to screen potential antigenic proteins of the bovine pathogen Clostridium perfringens type A

Clostridium perfringens type A frequently causes necrohaemorrhagic enteritis in cattle, a rapidly progressing disease with a high mortality rate, thus inflicting substantial economic losses in the cattle industry. Effective prevention and control of this disease rely on rapid detection and vaccination strategies, making the screening of antigenic proteins with diagnostic and vaccine potential particularly crucial. In this study, we conducted a pangenomic analysis of 15 bacterial strains, grounded in traditional reverse vaccinology and supplemented with B-cell linear and conformational epitope analysis tools. This approach led to the identification of 2304 core genes and 3606 accessory genes, among which 58 surface-exposed proteins, encoded by core genes, were identified Proteins lacking tertiary structure information were predicted via AlphaFold2, ultimately identifying four target proteins and 14 candidate proteins enriched with linear and conformational epitopes, including virulence proteins such as alpha-toxin, theta-toxin, and alpha-clostripain, and extracellular solute-binding proteins, rhodanese-like proteins, and the accessory gene-encoded lysozyme inhibitor LprI family protein. Our findings demonstrate that the combined use of multiple B-cell epitope analysis tools can help overcome the limitations of any single tool. The proteins selected in this study offer valuable references for rapid diagnostics and the development of genetically engineered vaccines.

B-cell epitope prediction of MPB83 protein as a candidate for serodiagnostic antigen of bovine tuberculosis in human: In silico study

Background: Bovine tuberculosis (bTB) can be transmitted to humans by inhalation or consumption of incomplete pasteurized milk and dairy products derived from infected cows. Most cases of Mycobacterium tuberculosis (M. bovis) infection are resistant to tuberculosis (TB) drugs. The risk of death during treatment for bTB has been reported to be 2.55 times higher than for TB. However, the quality of diagnostic methods for bTB remains relatively low.Objective: We aim to evaluate the potential of the B-cell epitope of the MPB83 protein as a candidate bTB serodiagnostic antigen using an in silico approach. Methods: This study was a computer-based descriptive study using secondary data from the National Center for Biotechnology Information (NCBI) protein database. The MPB83 protein sequence was obtained from M. tuberculosis variant bovis AF2122/97 from the United Kingdom. We described the characteristics of the linear epitope of the M. bovis B-cell protein MPB83 by measuring antigenicity, molecular weight, instability index, and Grand Average of Hydropathy (GRAVY) score. The tools used in this study were IBIVU PRALINE, VaxiJen v2.0, IEDB, ExPASy ProtParam, Cluspro, and the PyMOL application.Results: We found an epitope that could be used for bTB serodiagnostic antigen with low conservation, the 106KLNPDVNLVDTLN118 epitope. It has the molecular weight, instability index, and GRAVY score of 1638.76 Da, -28.44, and -0.300, respectively. Epitopes with the best criteria were simulated by docking to human major histocompatibility complex (MHC) class II. Docking results showed that the lowest binding energy was -644.8 kcal/mol. Further analysis using the PyMOL application obtained 14 hydrogen bonds with bond distances ranging from 1.7 Å to 2.2 Å, all of which showed strong hydrogen bonds.Conclusion: The B-cell epitope of MPB83 protein sequence 106KLNPDVNLVDTLN118 has a potential serodiagnostic antigen candidate for human bTB.

Computational Methods to Predict Conformational B-Cell Epitopes.

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.

Molecular characterization of canine circovirus based on the Capsid gene in Thailand

BackgroundCanine circovirus (CanineCV) is a single-stranded circular DNA virus that infects domestic and wild canids in many countries. CanineCV is associated with gastroenteritis and diarrhea, respiratory disease, and generalized vasculitis leading to a fatal event. The Capsid protein (Cap) is a structural protein of the virus which has high genetic variability and plays a role in the canine immune response. In this study, we cloned the full-length CanineCV Capsid gene (Cap). In-silico analyses were used to explore the genomic and amino acid variability and natural selection acting on the Cap gene. The immune relevance for T-cell and B-cell epitopes was predicted by the immunoinformatic approach.ResultsAccording to the Cap gene, our results showed that CanineCV was separated into five phylogenetic groups. The obtained CanineCV strain from this study was grouped with the previously discovered Thai strain (MG737385), as supported by a haplotype network. Entropy analyses revealed high nucleotide and amino acid variability of the Capsid region. Selection pressure analysis revealed four codons at positions 24, 50, 103, and 111 in the Cap protein evolved under diversifying selection. Prediction of B-cell epitopes exhibited four consensus sequences based on physiochemical properties, and eleven peptide sequences were predicted as T-cell epitopes. In addition, the positive selection sites were located within T-cell and B-cell epitopes, suggesting the role of the host immune system as a driving force in virus evolution.ConclusionsOur study provides knowledge of CanineCV genetic diversity, virus evolution, and potential epitopes for host cell immune response.

A novel vaccine construct against Zika virus fever: insights from epitope-based vaccine discovery through molecular modeling and immunoinformatics approaches.

The Zika virus (ZIKV) is an emerging virus associated with the Flaviviridae family that mainly causes infection in pregnant women and leads to several abnormalities during pregnancy. This virus has unique properties that may lead to pathological diseases. As the virus has the ability to evade immune response, a crucial effort is required to deal with ZIKV. Vaccines are a safe means to control different pathogenic infectious diseases. In the current research, a multi-epitope-based vaccination against ZIKV is being designed using in silico methods. For the epitope prediction and prioritization phase, ZIKV polyprotein (YP_002790881.1) and flavivirus polyprotein (>YP_009428568.1) were targeted. The predicted B-cell epitopes were used for MHC-I and MHC-II epitope prediction. Afterward, several immunoinformatics filters were applied and nine (REDLWCGSL, MQDLWLLRR, YKKSGITEV, TYTDRRWCF, RDAFPDSNS, KPSLGLINR, ELIGRARVS, AITQGKREE, and EARRSRRAV) epitopes were found to be probably antigenic in nature, non-allergenic, non-toxic, and water soluble without any toxins. Selected epitopes were joined using a particular GPGPG linker to create the base vaccination for epitopes, and an extra EAAAK linker was used to link the adjuvant. A total of 312 amino acids with a molecular weight (MW) of 31.62762 and an instability value of 34.06 were computed in the physicochemical characteristic analysis, indicating that the vaccine design is stable. The molecular docking analysis predicted a binding energy of -329.46 (kcal/mol) for TLR-3 and -358.54 (kcal/mol) for TLR-2. Moreover, the molecular dynamics simulation analysis predicted that the vaccine and receptor molecules have stable binding interactions in a dynamic environment. The C-immune simulation analysis predicted that the vaccine has the ability to generate both humoral and cellular immune responses. Based on the design, the vaccine construct has the best efficacy to evoke immune response in theory, but experimental analysis is required to validate the in silico base approach and ensure its safety.

Identification and serological responses to a novel Plasmodium vivax merozoite surface protein 1 (PvMSP-1) derived synthetic peptide: a putative biomarker for malaria exposure.

The integration of diagnostic methods holds promise for advancing the surveillance of malaria transmission in both endemic and non-endemic regions. Serological assays emerge as valuable tools to identify and delimit malaria transmission, serving as a complementary method to rapid diagnostic tests (RDT) and thick smear microscopy. Here, we evaluate the potential of antibodies directed against peptides encompassing the entire amino acid sequence of the PvMSP-1 Sal-I strain as viable serological biomarkers for P. vivax exposure. We screened peptides encompassing the complete amino acid sequence of the Plasmodium vivax Merozoite Surface Protein 1 (PvMSP-1) Sal-I strain as potential biomarkers for P. vivax exposure. Here, immunodominant peptides specifically recognized by antibodies from individuals infected with P. vivax were identified using the SPOT-synthesis technique followed by immunoblotting. Two 15-mer peptides were selected based on their higher and specific reactivity in immunoblotting assays. Subsequently, peptides p70 and p314 were synthesized in soluble form using SPPS (Solid Phase Peptide Synthesis) and tested by ELISA (IgG, and subclasses). This study unveils the presence of IgG antibodies against the peptide p314 in most P. vivax-infected individuals from the Brazilian Amazon region. In silico B-cell epitope prediction further supports the utilization of p314 as a potential biomarker for evaluating malaria transmission, strengthened by its amino acid sequence being part of a conserved block of PvMSP-1. Indeed, compared to patients infected with P. falciparum and uninfected individuals never exposed to malaria, P. vivax-infected patients have a notably higher recognition of p314 by IgG1 and IgG3.

WUREN: Whole-modal union representation for epitope prediction

B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.

SEMA 2.0: web-platform for B-cell conformational epitopes prediction using artificial intelligence.

Prediction of conformational B-cell epitopes is a crucial task in vaccine design and development. In this work, we have developed SEMA 2.0, a user-friendly web platform that enables the research community to tackle the B-cell epitopes prediction problem using state-of-the-art protein language models. SEMA 2.0 offers comprehensive research tools for sequence- and structure-based conformational B-cell epitopes prediction, accurate identification of N-glycosylation sites, and a distinctive module for comparing the structures of antigen B-cell epitopes enhancing our ability to analyze and understand its immunogenic properties. SEMA 2.0 website https://sema.airi.net is free and open to all users and there is no login requirement. Source code is available at https://github.com/AIRI-Institute/SEMAi.

Design of a multi-epitope-based vaccine candidate against Bovine Genital Campylobacteriosis using a reverse vaccinology approach

BackgroundBovine Genital Campylobacteriosis (BGC), a worldwide distributed venereal disease caused by Campylobacter fetus subsp. venerealis (Cfv), has a relevant negative economic impact in cattle herds. The control of BGC is hampered by the inexistence of globally available effective vaccines. The present in silico study aimed to develop a multi-epitope vaccine candidate against Cfv through reverse vaccinology.ResultsThe analysis of Cfv strain NCTC 10354 proteome allowed the identification of 9 proteins suitable for vaccine development. From these, an outer membrane protein, OmpA, and a flagellar protein, FliK, were selected for prediction of B-cell and T-cell epitopes. The top-ranked epitopes conservancy was assessed in 31 Cfv strains. The selected epitopes were integrated to form a multi-epitope fragment of 241 amino acids, which included 2 epitopes from OmpA and 13 epitopes from FliK linked by GPGPG linkers and connected to the cholera toxin subunit B by an EAAAK linker. The vaccine candidate was predicted to be antigenic, non-toxic, non-allergenic, and soluble upon overexpression. The protein structure was predicted and optimized, and the sequence was successfully cloned in silico into a plasmid vector. Additionally, immunological simulations demonstrated the vaccine candidate’s ability to stimulate an immune response.ConclusionsThis study developed a novel vaccine candidate suitable for further in vitro and in vivo experimental validation, which may become a useful tool for the control of BGC.

Introduction of protein vaccine candidate based on AP65, AP33, and α-actinin proteins against Trichomonas vaginalis parasite: an immunoinformatics design

Background Trichomonas vaginalis is the most common nonviral sexually transmitted disease (STI) worldwide. Vaccination is generally considered to be one of the most effective methods of preventing infectious diseases. Using AP65, AP33 and α-actinin proteins, this research aims to develop a protein vaccine against Trichomonas vaginalis. MethodsBased on the B-cell and T-cell epitope prediction servers, the most antigenic epitopes were selected, and with the necessary evaluations, epitope-rich domains of three proteins, AP65, AP33, and α-actinin, were selected and linked. Subsequently, the ability of the vaccine to interact with toll-like receptors 2 and 4 (TLR2 and TLR4) was assessed. The stability of the interactions was also studied by molecular dynamics for a duration of 100 nanoseconds. ResultsThe designed protein consists of 780 amino acids with a molecular weight of 85247.31 daltons. The results of the interaction of the vaccine candidate with TLR2 and TLR4 of the immune system also showed that there are strong interactions between the vaccine candidate protein with TLR2 (-890.7 kcal mol-1) and TLR4 (-967.3 kcal mol-1). All parameters studied to evaluate the stability of the protein structure and the protein-TLR2 and protein-TLR4 complexes showed that the structure of the vaccine candidate protein is stable alone and in complex with the immune system receptors. Investigation of the ability of the designed protein to induce an immune response using the C-ImmSim web server also showed that the designed protein is capable of stimulating B- and T-cell lymphocytes to produce the necessary cytokines and antibodies against Trichomonas vaginalis. ConclusionsOverall, our vaccine may have potential protection against Trichomonas vaginalis. However, for experimental in vivo and in vitro studies, it may be a good vaccine candidate.Graphical

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches.

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.

Construction by artificial intelligence and immunovalidation of hypoallergenic mite allergen Der f 36 vaccine.

The house dust mite (HDM) is widely recognized as the most prevalent allergen in allergic diseases. Allergen-specific immunotherapy (AIT) has been successfully implemented in clinical treatment for HDM. Hypoallergenic B-cell epitope-based vaccine designed by artificial intelligence (AI) represents a significant progression of recombinant hypoallergenic allergen derivatives. The three-dimensional protein structure of Der f 36 was constructed using Alphafold2. AI-based tools were employed to predict B-cell epitopes, which were subsequently verified through IgE-reaction testing. Hypoallergenic Der f 36 was then synthesized, expressed, and purified. The reduced allergenicity was assessed by enzyme-linked immunosorbent assay (ELISA), immunoblotting, and basophil activation test. T-cell response to hypoallergenic Der f 36 and Der f 36 was evaluated based on cytokine expression in the peripheral blood mononuclear cells (PBMCs) of patients. The immunogenicity was evaluated and compared through rabbit immunization with hypoallergenic Der f 36 and Der f 36, respectively. The inhibitory effect of the blocking IgG antibody on the specific IgE-binding activity and basophil activation of Der f 36 allergen was also examined. The final selected non-allergic B-cell epitopes were 25-48, 57-67, 107-112, 142-151, and 176-184. Hypoallergenic Der f 36 showed significant reduction in IgE-binding activity. The competitive inhibition of IgE-binding to Der f 36 was investigated using the hypoallergenic Der f 36, and only 20% inhibition could be achieved, which is greatly reduced when compared with inhibition by Der f 36 (98%). The hypoallergenic Der f 36 exhibited a low basophil-stimulating ratio similar to that of the negative control, and it could induce an increasing level of IFN-γ but not Th2 cytokines IL-5 and IL-13 in PBMCs. The vaccine-specific rabbit blocking IgG antibodies could inhibit the patients' IgE binding and basophil stimulation activity of Derf 36. This study represents the first application of an AI strategy to facilitate the development of a B-cell epitope-based hypoallergenic Der f 36 vaccine, which may become a promising immunotherapy for HDM-allergic patients due to its reduced allergenicity and its high immunogenicity in inducing blocking of IgG.

Autoantibodies against acetylcholine receptors are increased in archived serum samples from patients with schizophrenia

Previous studies have demonstrated that the levels of IgG against neurotransmitter receptors are increased in patients with schizophrenia. Genome-wide association (GWA) studies of schizophrenia confirmed that 108 loci harbouring over 300 genes were associated with schizophrenia. Although the functional implications of genetic variants are unclear, theoretical functional alterations of these genes could be replicated by the presence of autoantibodies. This study examined the levels of plasma IgG antibodies against four neurotransmitter receptors, CHRM4, GRM3, CHRNA4 and CHRNA5, using an in-house ELISA in 247 patients with schizophrenia and 344 non-psychiatric controls. Four peptides were designed based on in silico analysis with computational prediction of HLA-DRB1 restricted and B-cell epitopes. The relationship between plasma IgG levels and psychiatric symptoms, as defined by the Operational Criteria Checklist for Psychotic Illness and Affective Illness (OPCRIT), were examined. The results showed that the levels of plasma IgG against peptides derived from CHRM4 and CHRNA4 were significantly increased in patients with schizophrenia compared with control subjects, but there was no significant association of plasma IgG levels with any symptom domain or any specific symptoms. These preliminary results suggest that CHRM4 and CHRNA4 may be novel targets for autoantibody responses in schizophrenia, although the pathogenic relationship between increased serum autoantibody levels and schizophrenia symptoms remains unclear.

Active immunotherapy for C5a-mediated inflammation using adjuvant-free self-assembled peptide nanofibers

The terminal protein in the complement cascade C5a is a potent inflammatory molecule and chemoattractant that is involved in the pathology of multiple inflammatory diseases including sepsis and arthritis, making it a promising protein to target with immunotherapies. Active immunotherapies, in which patients are immunized against problematic self-molecules and generate therapeutic antibodies as a result, have received increasing interest as an alternative to traditional monoclonal antibody treatments. In previous work, we have designed supramolecular self-assembling peptide nanofibers as active immunotherapies with defined combinations of B- and T-cell epitopes. Herein, the self-assembling peptide Q11 platform was employed to generate a C5a-targeting active immunotherapy. Two of three predicted B-cell epitope peptides from C5a were found to be immunogenic when displayed within Q11 nanofibers, and the nanofibers were capable of reducing C5a serum concentrations following immunization. Contrastingly, C5a's precursor protein C5 maintained its original concentration, promising to minimize side effects heretofore associated with C5-targeted therapies. Immunization protected mice against an LPS-challenge model of sepsis, and it reduced clinical severity in a model of collagen-antibody induced arthritis. Together, this work indicates the potential for targeting terminal complement proteins with active immunotherapies by leveraging the immunogenicity of self-assembled peptide nanomaterials. Statement of significanceChronic inflammatory diseases such as rheumatoid arthritis, psoriasis, and inflammatory bowel disease are currently treated primarily with monoclonal antibodies against key inflammatory mediators. While helpful for many patients, they have high non-response rates, are costly, and commonly fail as anti-drug antibodies are raised by the patient. The approach we describe here explores a fundamentally different treatment paradigm: raising therapeutic antibody responses with an active immunotherapy.We employ innovative supramolecular peptide nanomaterials to elicit neutralizing antibody responses against complement component C5a and demonstrate therapeutic efficacy in preclinical mouse models of sepsis and rheumatoid arthritis. The strategy reported may represent a potential alternative to monoclonal antibody therapies.

DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations.

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings

Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT.

Immunoinformatics Approach to Design a Chimeric CD70-Peptide Vaccine against Renal Cell Carcinoma.

Renal cell carcinoma (RCC) accounts for the majority of cancer-related deaths worldwide. Overexpression of CD70 has been linked to advanced stages of RCC. Therefore, this study aims to develop a multiepitope vaccine targeting the overexpressed CD70 using immunoinformatics techniques. In this investigation, in silico multiepitope vaccines were constructed by linking specific CD70 protein epitopes for helper T lymphocytes and CD8+ T lymphocytes. To enhance immunogenicity, sequences of cell-penetrating peptide (CPP), penetratin (pAntp), along with the entire sequence of tumor necrosis factor-α (TNF-α), were attached to the N-terminal and C-terminal of the CD70 epitopes. Computational assessments were performed on these chimeric vaccines for antigenicity, allergenicity, peptide toxicity, population coverage, and physicochemical properties. Furthermore, refined 3D constructs were subjected to a range of analyses, encompassing structural B-cell epitope prediction and molecular docking. The chosen vaccine construct underwent diverse assessments such as molecular dynamics simulation, immune response simulation, and in silico cloning. All vaccines comprised antigenic, nontoxic, and nonallergenic epitopes, ensuring extensive global population coverage. The vaccine constructs demonstrated favorable physicochemical characteristics. The binding affinity of chimeric vaccines to the TNF receptor remained relatively stable, influenced by the alignment of vaccine components. Molecular docking and dynamics analyses predicted stable interactions between CD70-CPP-TNF and the TNF receptor, indicating potential efficacy. In silico codon optimization and cloning of the vaccine nucleic acid sequence were accomplished using the pET28a plasmid. Furthermore, this vaccine displayed the capacity to modulate humoral and cellular immune responses. Overall, the results suggest therapeutic potential for the chimeric CD70-CPP-TNF vaccine against RCC. However, validation through in vitro and in vivo experiments is necessary. This trial is registered with NCT04696731 and NCT04046445.

The rise of taxon-specific epitope predictors.

Computational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment, we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.