The chemistry and mechanism of antibody binding to protein antigens

Deep learning of antibody epitopes using positional permutation vectors

Background

The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.

Results

Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training.

Conclusions

With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Evolutionary dynamics of canine kobuvirus in Vietnam and Thailand reveal the evidence of viral ability to evade host immunity

Canine kobuvirus (CaKoV) is a pathogen associated with canine gastrointestinal disease (GID). This study examined 327 rectal swabs (RS), including 113 from Vietnam (46 healthy, 67 with GID) and 214 from Thailand (107 healthy and 107 with GID). CaKoV was detected in both countries, with prevalences of 28.3% (33/113) in Vietnam and 7.9% (17/214) in Thailand. Additionally, CaKoV was found in both dogs with diarrhea and healthy dogs. CaKoV was mainly found in puppies under six months of age (30.8%). Co-detection with other canine viruses were also observed. The complete coding sequence (CDS) of nine Vietnamese and four Thai CaKoV strains were characterized. Phylogenetic analysis revealed a close genetic relationship between Vietnamese and Thai CaKoV strains, which were related to the Chinese strains. CDS analysis indicated a distinct lineage for two Vietnamese CaKoV strains. Selective pressure analysis on the viral capsid (VP1) region showed negative selection, with potential positive selection sites on B-cell epitopes. This study, the first of its kind in Vietnam, provides insights into CaKoV prevalence in dogs of different ages and healthy statuses, updates CaKoV occurrence in Thailand, and sheds light on its molecular characteristics and immune evasion strategies.

Unraveling the crystal structure of the HpaA adhesin: insights into cell adhesion function and epitope localization of a Helicobacter pylori vaccine candidate

Helicobacter pylori is a bacterium that exhibits strict host restriction to humans and non-human primates, and the bacterium is widely acknowledged as a significant etiological factor in the development of chronic gastritis, peptic ulcers, and gastric cancers. The pathogenic potential of this organism lies in its adeptness at colonizing the gastric mucosa, which is facilitated by a diverse repertoire of virulence factors, including adhesins that promote the attachment of the bacteria to the gastric epithelium. Among these adhesins, HpaA stands out due to its conserved nature and pivotal role in establishing H. pylori colonization. Moreover, this lipoprotein holds promise as an antigen for the development of effective H. pylori vaccines, thus attracting considerable attention for in-depth investigations into its molecular function and identification of binding determinants. Here, we present the elucidation of the crystallographic structure of HpaA at 2.9 Å resolution. The folding adopts an elongated protein shape, which is distinctive to the Helicobacteraceae family, and features an apical domain extension that plays a critical role in the cell-adhesion activity on gastric epithelial cells. Our study also demonstrates the ability of HpaA to induce TNF-α expression in macrophages, highlighting a novel role as an immunoregulatory effector promoting the pro-inflammatory response in vitro. These findings not only contribute to a deeper comprehension of the multifaceted role of HpaA in H. pylori pathogenesis but also establish a fundamental basis for the design and development of structure-based derivatives, aimed at enhancing the efficacy of H. pylori vaccines.

IMPORTANCE

Helicobacter pylori is a bacterium that can cause chronic gastritis, peptic ulcers, and gastric cancers. The bacterium adheres to the lining of the stomach using proteins called adhesins. One of these proteins, HpaA, is particularly important for H. pylori colonization and is considered a promising vaccine candidate against H. pylori infections. In this work, we determined the atomic structure of HpaA, identifying a characteristic protein fold to the Helicobacter family and delineating specific amino acids that are crucial to support the attachment to the gastric cells. Additionally, we discovered that HpaA can trigger the production of TNF-α, a proinflammatory molecule, in macrophages. These findings provide valuable insights into how H. pylori causes disease and suggest that HpaA has a dual role in both attachment and immune activation. This knowledge could contribute to the development of improved vaccine strategies for preventing H. pylori infections.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

B-Cell Epitope Predictions Using Computational Methods

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine Against Clostridium difficile

Clostridium difficile (C.difficile) is an exclusively anaerobic, spore-forming, and Gram-positive pathogen that is the most common cause of nosocomial diarrhea and is becoming increasingly prevalent in the community. Because C. difficile is strictly anaerobic, spores that can survive for months in the external environment contribute to the persistence and diffusion of C. difficile within the healthcare environment and community. Antimicrobial therapy disrupts the natural intestinal flora, allowing spores to develop into propagules that colonize the colon and produce toxins, thus leading to antibiotic-associated diarrhea and pseudomembranous enteritis. However, there is no licensed vaccine to prevent Clostridium difficile infection (CDI). In this study, a multi-epitope vaccine was designed using modern computer methods. Two target proteins, CdeC, affecting spore germination, and fliD, affecting propagule colonization, were chosen to construct the vaccine so that it could simultaneously induce the immune response against two different forms (spore and propagule) of C. difficile. We obtained the protein sequences from the National Center for Biotechnology Information (NCBI) database. After the layers of filtration, 5 cytotoxic T-cell lymphocyte (CTL) epitopes, 5 helper T lymphocyte (HTL) epitopes, and 7 B-cell linear epitopes were finally selected for vaccine construction. Then, to enhance the immunogenicity of the designed vaccine, an adjuvant was added to construct the vaccine. The Prabi and RaptorX servers were used to predict the vaccine's two- and three-dimensional (3D) structures, respectively. Additionally, we refined and validated the structures of the vaccine construct. Molecular docking and molecular dynamics (MD) simulation were performed to check the interaction model of the vaccine-Toll-like receptor (TLR) complexes, vaccine-major histocompatibility complex (MHC) complexes, and vaccine-B-cell receptor (BCR) complex. Furthermore, immune stimulation, population coverage, and in silico molecular cloning were also conducted. The foregoing findings suggest that the final formulated vaccine is promising against the pathogen, but more researchers are needed to verify it.

Novel Secreted Peptides From Rhizopus arrhizus var. delemar With Immunomodulatory Effects That Enhance Fungal Pathogenesis

Secreted fungal peptides are known to influence the interactions between the pathogen and host innate immunity. The aim of this study is to screen and evaluate secreted peptides from the fungus Rhizopus arrhizus var. delemar for their immunomodulatory activity. By using mass spectrometry and immuno-informatics analysis, we identified three secreted peptides CesT (S16), Colicin (S17), and Ca2+/calmodulin-dependent protein kinase/ligand (CAMK/CAMKL; S27). Culturing peripheral blood-derived monocytic macrophages (PBMMs) in the presence of S16 or S17 caused cell clumping, while culturing them with S27 resulted in the formation of spindle-shaped cells. S27-treated PBMMs showed cell cycle arrest at G0 phase and exhibited alternatively activated macrophage phenotype with pronounced reduction in scavenger receptors CD163 and CD206. Homology prediction indicated that IL-4/IL-13 is the immunomodulatory target of S27. Confirming this prediction, S27 initiated macrophage activation through phosphorylation of STAT-6; STAT-6 inhibition reversed the activity of S27 and reduced the formation of spindle-shaped PBMMs. Lastly, S27 treatment of PBMMs was associated with altered expression of key iron regulatory genes including hepcidin, ferroportin, transferrin receptor 1, and ferritin in a pattern consistent with increased cellular iron release; a condition known to enhance Rhizopus infection. Collectively, R. arrhizus var. delemar secretes peptides with immunomodulatory activities that support fungal pathogenesis. Targeting the IL-4/IL-13R/STAT-6 axis is a potential therapeutic approach to enhance the PBMM-mediated fungal phagocytosis. This represents a potential new approach to overcome lethal mucormycosis.

Immunoinformatics-Based Designing of a Multi-Epitope Chimeric Vaccine From Multi-Domain Outer Surface Antigens of Leptospira

Accurate information on antigenic epitopes within a multi-domain antigen would provide insights into vaccine design and immunotherapy. The multi-domain outer surface Leptospira immunoglobulin-like (Lig) proteins LigA and LigB, consisting of 12–13 homologous bacterial Ig (Big)-like domains, are potential antigens of Leptospira interrogans. Currently, no effective vaccine is available against pathogenic Leptospira. Both the humoral immunity and cell-mediated immunity of the host play critical roles in defending against Leptospira infection. Here, we used immunoinformatics approaches to evaluate antigenic B-cell lymphocyte (BCL) and cytotoxic T-lymphocyte (CTL) epitopes from Lig proteins. Based on certain crucial parameters, potential epitopes that can stimulate both types of adaptive immune responses were selected to design a chimeric vaccine construct. Additionally, an adjuvant, the mycobacterial heparin-binding hemagglutinin adhesin (HBHA), was incorporated into the final multi-epitope vaccine construct with a suitable linker. The final construct was further scored for its antigenicity, allergenicity, and physicochemical parameters. A three-dimensional (3D) modeled construct of the vaccine was implied to interact with Toll-like receptor 4 (TLR4) using molecular docking. The stability of the vaccine construct with TLR4 was predicted with molecular dynamics simulation. Our results demonstrate the application of immunoinformatics and structure biology strategies to develop an epitope-specific chimeric vaccine from multi-domain proteins. The current findings will be useful for future experimental validation to ratify the immunogenicity of the chimera.

In silico designing of vaccine candidate against Clostridium difficile

Clostridium difficile is a spore-forming gram-positive bacterium, recognized as the primary cause of antibiotic-associated nosocomial diarrhoea. Clostridium difficile infection (CDI) has emerged as a major health-associated infection with increased incidence and hospitalization over the years with high mortality rates. Contamination and infection occur after ingestion of vegetative spores, which germinate in the gastro-intestinal tract. The surface layer protein and flagellar proteins are responsible for the bacterial colonization while the spore coat protein, is associated with spore colonization. Both these factors are the main concern of the recurrence of CDI in hospitalized patients. In this study, the CotE, SlpA and FliC proteins are chosen to form a multivalent, multi-epitopic, chimeric vaccine candidate using the immunoinformatics approach. The overall reliability of the candidate vaccine was validated in silico and the molecular dynamics simulation verified the stability of the vaccine designed. Docking studies showed stable vaccine interactions with Toll‐Like Receptors of innate immune cells and MHC receptors. In silico codon optimization of the vaccine and its insertion in the cloning vector indicates a competent expression of the modelled vaccine in E. coli expression system. An in silico immune simulation system evaluated the effectiveness of the candidate vaccine to trigger a protective immune response.

Bioinformatic prediction of immunodominant regions in spike protein for early diagnosis of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Background

To contain the pandemics caused by SARS-CoV-2, early detection approaches with high accuracy and accessibility are critical. Generating an antigen-capture based detection system would be an ideal strategy complementing the current methods based on nucleic acids and antibody detection. The spike protein is found on the outside of virus particles and appropriate for antigen detection.

Methods

In this study, we utilized bioinformatics approaches to explore the immunodominant fragments on spike protein of SARS-CoV-2.

Results

The S1 subunit of spike protein was identified with higher sequence specificity. Three immunodominant fragments, Spike_56-94, Spike_199-264, and Spike_577-612, located at the S1 subunit were finally selected via bioinformatics analysis. The glycosylation sites and high-frequency mutation sites on spike protein were circumvented in the antigen design. All the identified fragments present qualified antigenicity, hydrophilicity, and surface accessibility. A recombinant antigen with a length of 194 amino acids (aa) consisting of the selected immunodominant fragments as well as a universal Th epitope was finally constructed.

Conclusion

The recombinant peptide encoded by the construct contains multiple immunodominant epitopes, which is expected to stimulate a strong immune response in mice and generate qualified antibodies for SARS-CoV-2 detection.

Designing a vaccine for fascioliasis using immunogenic 24 kDa mu-class glutathione s-transferase

Fascioliasis, caused by the liver fluke Fasciola gigantica, is a significant zoonotic disease of the livestock and human, causing substantial economic loss worldwide. Triclabendazole (TCBZ) is the only drug available for the management of the disease against which there is an alarming increase in drug resistance. No vaccine is available commercially for the protection against this disease. Increasing resistance to TCBZ and the lack of a successful vaccine against fascioliasis demands the development of vaccines. In the present study, a structural immunoinformatics approach was used to design a multi-epitope subunit vaccine using the glutathione S-transferase (GST) protein of Fasciola gigantica. The GST antigen is a safe, non-allergic, highly antigenic, and effective vaccine candidate against various parasitic flukes and worms. The cytotoxic T lymphocytes, helper T lymphocytes, and B-cell epitopes were selected for constructing the vaccine based on their immunogenic behavior and binding affinity. The physicochemical properties, allergenicity, and antigenicity of the designed vaccine were analyzed. To elucidate the tertiary structure of the vaccine, homology modeling was performed, followed by structure refinement and docking against the TLR2 immune receptor. Molecular dynamics simulations showed a stable interaction between the vaccine and the receptor complex. Finally, in silico cloning was performed to evaluate the expression and translation of the vaccine construct in the E. coli expression system. Further studies require experimental validation for the safety and immunogenic behavior of the designed vaccine.

An array of 60,000 antibodies for proteome-scale antibody generation and target discovery

Antibodies are essential for elucidating gene function. However, affordable technology for proteome-scale antibody generation does not exist. To address this, we developed Proteome Epitope Tag Antibody Library (PETAL) and its array. PETAL consists of 62,208 monoclonal antibodies (mAbs) against 15,199 peptides from diverse proteomes. PETAL harbors binders for a great multitude of proteins in nature due to antibody multispecificity, an intrinsic antibody feature. Distinctive combinations of 10,000 to 20,000 mAbs were found to target specific proteomes by array screening. Phenotype-specific mAb-protein pairs were found for maize and zebrafish samples. Immunofluorescence and flow cytometry mAbs for membrane proteins and chromatin immunoprecipitation-sequencing mAbs for transcription factors were identified from respective proteome-binding PETAL mAbs. Differential screening of cell surface proteomes of tumor and normal tissues identified internalizing tumor antigens for antibody-drug conjugates. By finding high-affinity mAbs at a fraction of current time and cost, PETAL enables proteome-scale antibody generation and target discovery.

SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes

Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP . This chapter describes the webserver of SVMTriP.

A multi-method and structure-based in silico vaccine designing against Echinococcus granulosus through investigating enolase protein

Introduction: Hydatid disease is a ubiquitous parasitic zoonotic disease, which causes different medical, economic and serious public health problems in some parts of the world. The causal organism is a multi-stage parasite named Echinococcus granulosus whose life cycle is dependent on two types of mammalian hosts viz definitive and intermediate hosts.

Methods: In this study, enolase, as a key functional enzyme in the metabolism of E. granulosus (EgEnolase), was targeted through a comprehensive in silico modeling analysis and designing a host-specific multi-epitope vaccine. Three-dimensional (3D) structure of enolase was modeled using MODELLER v9.18 software. The B-cell epitopes (BEs) were predicted based on the multi-method approach and via some authentic online predictors. ClusPro v2.0 server was used for docking-based T-helper epitope prediction. The 3D structure of the vaccine was modeled using the RaptorX server. The designed vaccine was evaluated for its immunogenicity, physicochemical properties, and allergenicity. The codon optimization of the vaccine sequence was performed based on the codon usage table of E. coli K12. Finally, the energy minimization and molecular docking were implemented for simulating the vaccine binding affinity to the TLR-2 and TLR-4 and the complex stability.

Results: The designed multi-epitope vaccine was found to induce anti-EgEnolase immunity which may have the potential to prevent the survival and proliferation of E. granulosus into the definitive host.

Conclusion: Based on the results, this step-by-step immunoinformatics approach could be considered as a rational platform for designing vaccines against such multi-stage parasites. Furthermore, it is proposed that this multi-epitope vaccine is served as a promising preventive anti-echinococcosis agent.

Requirements for Empirical Immunogenicity Trials, Rather than Structure-Based Design, for Developing an Effective HIV Vaccine

The claim that it is possible to rationally design a structure-based HIV-1 vaccine is based on misconceptions regarding the nature of protein epitopes and of immunological specificity. Attempts to use reverse vaccinology to generate an HIV-1 vaccine on the basis of the structure of viral epitopes bound to monoclonal neutralizing antibodies have failed so far because it was not possible to extrapolate from an observed antigenic structure to the immunogenic structure required in a vaccine. Vaccine immunogenicity depends on numerous extrinsic factors such as the host immunoglobulin gene repertoire, the presence of various cellular and regulatory mechanisms in the immunized host and the process of antibody affinity maturation. All these factors played a role in the appearance of the neutralizing antibody used to select the epitope to be investigated as potential vaccine immunogen, but they cannot be expected to be present in identical form in the host to be vaccinated. It is possible to rationally design and optimize an epitope to fit one particular antibody molecule or to improve the paratope binding efficacy of a monoclonal antibody intended for passive immunotherapy. What is not possible is to rationally design an HIV-1 vaccine immunogen that will elicit a protective polyclonal antibody response of predetermined efficacy. An effective vaccine immunogen can only be discovered by investigating experimentally the immunogenicity of a candidate molecule and demonstrating its ability to induce a protective immune response. It cannot be discovered by determining which epitopes of an engineered antigen molecule are recognized by a neutralizing monoclonal antibody. This means that empirical immunogenicity trials rather than structural analyses of antigens offer the best hope of discovering an HIV-1 vaccine.

Longitudinal evaluation of humoral immune response and merozoite surface antigen diversity in calves naturally infected with Babesia bovis, in São Paulo, Brazil

Babesiosis is an economically important infectious disease affecting cattle worldwide. In order to longitudinally evaluate the humoral immune response against Babesia bovis and the merozoite surface antigen diversity of B. bovis among naturally infected calves in Taiaçu, Brazil, serum and DNA samples from 15 calves were obtained quarterly, from their birth to 12 months of age. Anti-B. bovis IgG antibodies were detected by means of the indirect fluorescent antibody test (IFAT) and enzyme-linked immunosorbent assay (ELISA). The polymerase chain reaction (PCR) was used to investigate the genetic diversity of B. bovis, based on the genes that encode merozoite surface antigens (MSA-1, MSA-2b and MSA-2c). The serological results demonstrated that up to six months of age, all the calves developed active immunity against B. bovis. Among the 75 DNA samples evaluated, 2, 4 and 5 sequences of the genes msa-1, msa-2b and msa-2c were obtained. The present study demonstrated that the msa-1 and msa-2b genes sequences amplified from blood DNA of calves positive to B. bovis from Taiaçu were genetically distinct, and that msa-2c was conserved. All animals were serologically positive to ELISA and IFAT, which used full repertoire of parasite antigens in despite of the genetic diversity of MSAs.

iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction

Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.

The path to VICTORy - a beginner's guide to success using commercial research antibodies

Commercial research antibodies are crucial tools in modern cell biology and biochemistry. In the USA some $2 billion a year are spent on them, but many are apparently not fit-for-purpose, and this may contribute to the 'reproducibility crisis' in biological sciences. Inadequate antibody validation and characterization, lack of user awareness, and occasional incompetence amongst suppliers have had immense scientific and personal costs. In this Opinion, I suggest some paths to make the use of these vital tools more successful. I have attempted to summarize and extend expert views from the literature to suggest that sustained routine efforts should made in: (1) the validation of antibodies, (2) their identification, (3) communication and controls, (4) the training of potential users, (5) the transparency of original equipment manufacturer (OEM) marketing agreements, and (5) in a more widespread use of recombinant antibodies (together denoted the 'VICTOR' approach).

Designing and overproducing a tandem epitope of gp350/220 that shows a potential to become an EBV vaccine

Background

Epstein-Barr virus (EBV) can cause cancer in people from around the world. There is no EBV vaccine available for use on a global scale. However, emerging evidence suggests that the epitope on the gp350/220 capsid protein may be developed into an EBV vaccine. Nevertheless, the production of small, single epitope is challenging of stability issues and possible alteration of peptide structure. In this study, a tandem epitope was developed consisting of three single epitopes, aimed to improve stability, antigenicity and preserve epitope structure.

Materials and methods

A tandem epitope was designed using bioinformatics based on the epitope structure of the gp350/220 protein. The tandem epitope structure was analyzed using a protein folding method with Abalone software, which was further refined via YASARA force field and molecular repairing using a FoldX method. Immunogenicity was examined with Epitopia software, whereas allergen properties were tested using AlgPred. The pattern of the tandem epitope binding with anti-gp350/220 antibodies was performed using Z-dock and snugDock. The tandem epitope was then overproduced in E. coli strain BL21 as a host cell.

Result

Our model demonstrated a successfully designed and overproduced tandem epitope. The tandem epitope demonstrated a similar structure compared with the epitope of whole protein gp350/220. Our epitope also demonstrated non-allergen and antigenicity properties, and possessed antibody binding patterns consistent with whole protein gp350/220.

Conclusion and recommendation

These data suggest a novel tandem epitope composed of three similar epitopes demonstrates antigenicity, structure, and binding properties consistent with whole protein gp350/220. We also demonstrate successful production of the tandem epitope using E. coli strain BL21 as a host. Future in vivo experimental animal research is necessary to test the ability of this tandem epitope to stimulate antibody production.

An Introduction to Epitope Mapping

Antibodies are protein molecules used routinely for therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and for proteinaceous antigens the epitope can be of complex nature. Information about the binding epitope of an antibody can provide important mechanistic insights and indicate for what applications an antibody might be useful. Therefore, a variety of epitope mapping techniques have been developed to localize such regions. Although the real picture is even more complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping of the two different classes of epitopes, using high-throughput or high-resolution methods, have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Early approaches utilizing sets of truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high-throughput screening in conjunction with bioinformatic analyses of large datasets.

Conserved Binding Regions Provide the Clue for Peptide-Based Vaccine Development: A Chemical Perspective

Synthetic peptides have become invaluable biomedical research and medicinal chemistry tools for studying functional roles, i.e., binding or proteolytic activity, naturally-occurring regions’ immunogenicity in proteins and developing therapeutic agents and vaccines. Synthetic peptides can mimic protein sites; their structure and function can be easily modulated by specific amino acid replacement. They have major advantages, i.e., they are cheap, easily-produced and chemically stable, lack infectious and secondary adverse reactions and can induce immune responses via T- and B-cell epitopes. Our group has previously shown that using synthetic peptides and adopting a functional approach has led to identifying Plasmodium falciparumconserved regions binding to host cells. Conserved high activity binding peptides’ (cHABPs) physicochemical, structural and immunological characteristics have been taken into account for properly modifying and converting them into highly immunogenic, protection-inducing peptides (mHABPs) in the experimental Aotus monkey model. This article describes stereo–electron and topochemical characteristics regarding major histocompatibility complex (MHC)-mHABP-T-cell receptor (TCR) complex formation. Some mHABPs in this complex inducing long-lasting, protective immunity have been named immune protection-inducing protein structures (IMPIPS), forming the subunit components in chemically synthesized vaccines. This manuscript summarizes this particular field and adds our recent findings concerning intramolecular interactions (H-bonds or π-interactions) enabling proper IMPIPS structure as well as the peripheral flanking residues (PFR) to stabilize the MHCII-IMPIPS-TCR interaction, aimed at inducing long-lasting, protective immunological memory.

Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications

Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research.

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

Structure-Based Reverse Vaccinology Failed in the Case of HIV Because it Disregarded Accepted Immunological Theory

Two types of reverse vaccinology (RV) should be distinguished: genome-based RV for bacterial vaccines and structure-based RV for viral vaccines. Structure-based RV consists in trying to generate a vaccine by first determining the crystallographic structure of a complex between a viral epitope and a neutralizing monoclonal antibody (nMab) and then reconstructing the epitope by reverse molecular engineering outside the context of the native viral protein. It is based on the unwarranted assumption that the epitope designed to fit the nMab will have acquired the immunogenic capacity to elicit a polyclonal antibody response with the same protective capacity as the nMab. After more than a decade of intensive research using this type of RV, this approach has failed to deliver an effective, preventive HIV-1 vaccine. The structure and dynamics of different types of HIV-1 epitopes and of paratopes are described. The rational design of an anti-HIV-1 vaccine is shown to be a misnomer since investigators who claim that they design a vaccine are actually only improving the antigenic binding capacity of one epitope with respect to only one paratope and not the immunogenic capacity of an epitope to elicit neutralizing antibodies. Because of the degeneracy of the immune system and the polyspecificity of antibodies, each epitope studied by the structure-based RV procedure is only one of the many epitopes that the particular nMab is able to recognize and there is no reason to assume that this nMab must have been elicited by this one epitope of known structure. Recent evidence is presented that the trimeric Env spikes of the virus possess such an enormous plasticity and intrinsic structural flexibility that it is it extremely difficult to determine which Env regions are the best candidate vaccine immunogens most likely to elicit protective antibodies.

TLR Agonist Augments Prophylactic Potential of Acid Inducible Antigen Rv3203 against Mycobacterium tuberculosis H37Rv in Experimental Animals

In general, the members of Lip gene family of Mycobacterium tuberculosis evoke strong immune response in the host. Keeping this fact into consideration, we investigated role of Rv3203, a cell wall associated protein with lipolytic activity, in imparting protection against experimental murine tuberculosis. The data of the present study suggested that archaeosome encapsulated Rv3203 induce strong lymphocyte proliferation, up-regulated Th-1 biased cytokines profile, increased expression of co-stimulatory markers on both antigen presenting cells and T lymphocytes. The immuno-prophylactic response was further modulated by exposure of the animals to zymosan, a TLR2/6 agonist, prior to immunization with archaeosome encapsulated Rv3203. Interestingly, pre-treatment of experimental animals with zymosan boosted strong immunological memory as compared to archaeosome encapsulated Rv3203 as well as BCG vaccine. We conclude that priming of immunized animal with TLR agonist followed by immunization with archaeosomes encapsulated Rv3203 offer substantial protection against tuberculosis infection and could be a potential subunit vaccine based prophylactic strategy.

Structural, Functional, and Immunogenic Insights on Cu,Zn Superoxide Dismutase Pathogenic Virulence Factors from Neisseria meningitidis and Brucella abortus

Bacterial pathogens Neisseria meningitidis and Brucella abortus pose threats to human and animal health worldwide, causing meningococcal disease and brucellosis, respectively. Mortality from acute N. meningitidis infections remains high despite antibiotics, and brucellosis presents alimentary and health consequences. Superoxide dismutases are master regulators of reactive oxygen and general pathogenicity factors and are therefore therapeutic targets. Cu,Zn superoxide dismutases (SODs) localized to the periplasm promote survival by detoxifying superoxide radicals generated by major host antimicrobial immune responses. We discovered that passive immunization with an antibody directed at N. meningitidis SOD (NmSOD) was protective in a mouse infection model. To define the relevant atomic details and solution assembly states of this important virulence factor, we report high-resolution and X-ray scattering analyses of NmSOD and of SOD from B. abortus (BaSOD). The NmSOD structures revealed an auxiliary tetrahedral Cu-binding site bridging the dimer interface; mutational analyses suggested that this metal site contributes to protein stability, with implications for bacterial defense mechanisms. Biochemical and structural analyses informed us about electrostatic substrate guidance, dimer assembly, and an exposed C-terminal epitope in the NmSOD dimer. In contrast, the monomeric BaSOD structure provided insights for extending immunogenic peptide epitopes derived from the protein. These collective results reveal unique contributions of SOD to pathogenic virulence, refine predictive motifs for distinguishing SOD classes, and suggest general targets for antibacterial immune responses. The identified functional contributions, motifs, and targets distinguishing bacterial and eukaryotic SOD assemblies presented here provide a foundation for efforts to develop SOD-specific inhibitors of or vaccines against these harmful pathogens.

IMPORTANCE

By protecting microbes against reactive oxygen insults, SODs aid survival of many bacteria within their hosts. Despite the ubiquity and conservation of these key enzymes, notable species-specific differences relevant to pathogenesis remain undefined. To probe mechanisms that govern the functioning of Neisseria meningitidis and Brucella abortus SODs, we used X-ray structures, enzymology, modeling, and murine infection experiments. We identified virulence determinants common to the two homologs, assembly differences, and a unique metal reservoir within meningococcal SOD that stabilizes the enzyme and may provide a safeguard against copper toxicity. The insights reported here provide a rationale and a basis for SOD-specific drug design and an extension of immunogen design to target two important pathogens that continue to pose global health threats.

Specificity, polyspecificity, and heterospecificity of antibody-antigen recognition

The concept of antibody specificity is analyzed and shown to reside in the ability of an antibody to discriminate between two antigens. Initially, antibody specificity was attributed to sequence differences in complementarity determining regions (CDRs), but as increasing numbers of crystallographic antibody-antigen complexes were elucidated, specificity was analyzed in terms of six antigen-binding regions (ABRs) that only roughly correspond to CDRs. It was found that each ABR differs significantly in its amino acid composition and tends to bind different types of amino acids at the surface of proteins. In spite of these differences, the combined preference of the six ABRs does not allow epitopes to be distinguished from the rest of the protein surface. These findings explain the poor success of past and newly proposed methods for predicting protein epitopes. Antibody polyspecificity refers to the ability of one antibody to bind a large variety of epitopes in different antigens, and this property explains how the immune system develops an antibody repertoire that is able to recognize every antigen the system is likely to encounter. Antibody heterospecificity arises when an antibody reacts better with another antigen than with the one used to raise the antibody. As a result, an antibody may sometimes appear to have been elicited by an antigen with which it is unable to react. The implications of antibody polyspecificity and heterospecificity in vaccine development are pointed out.

Usefulness of the Nonself-Self Algorithm of HLA Epitope Immunogenicity in the Specificity Analysis of Monospecific Antibodies Induced during Pregnancy

BACKGROUND

HLAMatchmaker is a program to analyze the epitope specificities of HLA antibodies. It considers each HLA allele as a string of eplets. Intralocus and interlocus comparisons between donor and recipient alleles offer a structural assessment of compatibility and an analysis of allele panel reactivity patterns can generate information about epitope specificities of HLA antibodies. However, HLAMatchmaker cannot always generate conclusive interpretations of reactivity patterns of all monospecific antibodies, which by definition recognize single epitopes.

HYPOTHESIS

We have therefore developed a new antibody analysis approach that utilizes the nonself-self algorithm of HLA epitope immunogenicity. It is based on the concept that HLA antibodies originate from B-cells with immunoglobulin receptors to self-HLA epitopes on one given allele and which can be activated by epitopes defined by a few nonself residue differences whereas the remainder of the structural epitope of the immunizing allele consists of self residues.

METHODS

Three human monoclonal class I antibodies from HLA typed women sensitized during pregnancy were tested in Ig-binding assays with single alleles on a Luminex platform.

FINDINGS

Three new HLA epitopes were identified; they are defined by combinations of nonself- and self-residues for one allele of the antibody producer.

CONCLUSION

The nonself-self paradigm of HLA epitope immunogenicity offers a second approach to analyze HLA antibody specificities.

Predicting linear B-cell epitopes using amino acid anchoring pair composition

BACKGROUND

Accurate identification of linear B-cell epitopes plays an important role in peptide vaccine designs, immunodiagnosis, and antibody productions. Although several prediction methods have been reported, unsatisfied accuracy has limited the broad usages in linear B-cell epitope prediction. Therefore, developing a reliable model with significant improvement on prediction accuracy is highly desirable.

RESULTS

In this study, we developed a novel model for prediction of linear B-cell epitopes, APCpred, which was derived from the combination of amino acid anchoring pair composition (APC) and Support Vector Machine (SVM) methods. Systematic comparisons with the existing prediction models demonstrated that APCpred method significantly improved the prediction accuracy both in fivefold cross-validation of training datasets and in independent blind datasets. In the fivefold cross-validation test with Chen872 dataset at window size of 20, APCpred achieved AUC of 0.809 and accuracy of 72.94%, which was much more accurate than the existing models, e.g., Bayesb, Chen's AAP methods and the enhanced combination method of AAP with five AP scales. For the fivefold cross-validation test with ABC16 dataset, APCpred achieved an improved AUC of 0.794 and ACC of 73.00% at window size of 16, and attained an AUC of 0.748 and ACC of 67.96% on Blind387 dataset after being trained with ABC16 dataset. Trained with Lbtope_Confirm dataset, APCpred achieved an increased Acc of 55.09% on FBC934 dataset. Within sequence window sizes from 12 to 20, APCpred final model on homology-reduced dataset achieved an optimal AUC of 0.748 and ACC of 68.43% in fivefold cross-validation at the window size of 20.

CONCLUSION

APCpred model demonstrated a significant improvement in predicting linear B-cell epitopes using the features of amino acid anchoring pair composition (APC). Based on our study, a webserver has been developed for on-line prediction of linear B-cell epitopes, which is a free access at: http:/ccb.bmi.ac.cn/APCpred/.

Diagnostic potential of zinc finger protein-specific autoantibodies and associated linear B-cell epitopes in colorectal cancer

Colorectal cancer is one of the most common cancers worldwide with almost 700,000 deaths every year. Detection of colorectal cancer at an early stage significantly improves patient survival. Cancer-specific autoantibodies found in sera of cancer patients can be used for pre-symptomatic detection of the disease. In this study we assess the zinc finger proteins ZNF346, ZNF638, ZNF700 and ZNF768 as capture antigens for the detection of autoantibodies in colorectal cancer. Sera from 96 patients with colorectal cancer and 35 control patients with no evidence of cancer on colonoscopy were analysed for the presence of ZNF-specific autoantibodies using an indirect ELISA. Autoantibodies to individual ZNF proteins were detected in 10-20% of colorectal cancer patients and in 0-5.7% of controls. A panel of all four ZNF proteins resulted in an assay specificity of 91.4% and sensitivity of 41.7% for the detection of cancer patients in a cohort of non-cancer controls and colorectal cancer patients. Clinicopathological and survival analysis revealed that ZNF autoantibodies were independent of disease stage and did not correlate with disease outcome. Since ZNF autoantibodies were shared between patients and corresponding ZNF proteins showed similarities in their zinc finger motifs, we performed an in silico epitope sequence analysis. Zinc finger proteins ZNF700 and ZNF768 showed the highest sequence similarity with a bl2seq score of 262 (E-value 1E-81) and their classical C2H2 ZNF motifs were identified as potential epitopes contributing to their elevated immunogenic potential. Our findings show an enhanced and specific immunogenicity to zinc finger proteins, thereby providing a multiplexed autoantibody assay for minimally invasive detection of colorectal cancer.

FlaF Is a β-Sandwich Protein that Anchors the Archaellum in the Archaeal Cell Envelope by Binding the S-Layer Protein

Archaea employ the archaellum, a type IV pilus-like nanomachine, for swimming motility. In the crenarchaeon Sulfolobus acidocaldarius, the archaellum consists of seven proteins: FlaB/X/G/F/H/I/J. FlaF is conserved and essential for archaellum assembly but no FlaF structures exist. Here, we truncated the FlaF N terminus and solved 1.5-Å and 1.65-Å resolution crystal structures of this monotopic membrane protein. Structures revealed an N-terminal α-helix and an eight-strand β-sandwich, immunoglobulin-like fold with striking similarity to S-layer proteins. Crystal structures, X-ray scattering, and mutational analyses suggest dimer assembly is needed for in vivo function. The sole cell envelope component of S. acidocaldarius is a paracrystalline S-layer, and FlaF specifically bound to S-layer protein, suggesting that its interaction domain is located in the pseudoperiplasm with its N-terminal helix in the membrane. From these data, FlaF may act as the previously unknown archaellum stator protein that anchors the rotating archaellum to the archaeal cell envelope.

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This is the first structural and functional study of an archaellum stator component

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sFlaF is a β-sandwich, immunoglobulin-like dimeric protein

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FlaF resembles and binds to the S-layer protein

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FlaF exerts its function in the pseudoperiplasm

Identification of human papillomavirus-16 E6 variation in cervical cancer and their impact on T and B cell epitopes

The infection with high-risk human papillomavirus (HR-HPV) is the most important risk factor for development of cervical cancer. The intra-type variations of HPV have different biological and pathological consequences with respect to disease progression. In the present study, six major Indian variants were experimentally identified in E6 gene of HPV-16 and showed their impact on immunogenicity by in silico methods. Four different phylogenetic lineages were observed in sequences including European (E) prototype, European variant, Asian and American Asian variant classes and complete absence of African phylogenetic lineages. On the prediction of B- and T-cell epitopes, 18 and 23 potent epitopes for MHC-II alleles, 10 potent MHC-I and 15 B-cell epitopes in each reference and variant sequence were identified. Interestingly, the presence of variation H78Y and L83V result in creation of four new epitopes for the HLA-DQA1*0101/DQB1*0501. Out of 15 B-cell predicted epitopes, three most potent epitopes were identified in both reference and variant sequence. Notably the amino acid stretch from amino acid 16-60 and 76-94 are very important for the immunological properties of E6 protein because these regions contain majority of the predicted epitopes. In future, this could control the cervical cancer by targeting these amino acid stretches for the development of HPV-16 vaccine.

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.

Ribosome display enhanced by next generation sequencing: a tool to identify antibody-specific peptide ligands

Detection of antibodies in serum has many important applications. Our goal was to develop a facile general experimental approach for identifying antibody-specific peptide ligands that could be used as the reagents for antibody detection. Our emphasis was on an approach that would allow identification of peptide ligands for antibodies in serum without the need to isolate the target antibody or to know the identity of its antigen. We combined ribosome display (RD) with the analysis of peptide libraries by next generation sequencing (NGS) of their coding RNA to facilitate identification of antibody-specific peptide ligands from random sequence peptide library. We first demonstrated, using purified antibodies, that with our approach-specific peptide ligands for antibodies with simple linear epitopes, as well as peptide mimotopes for antibodies recognizing complex epitopes, were readily identified. Inclusion of NGS analysis reduced the number of RD selection rounds that were required to identify specific ligands and facilitated discrimination between specific and spurious nonspecific sequences. We then used a model of human serum spiked with a known target antibody to develop NGS-based analysis that allowed identification of specific ligands for a target antibody in the context of an overwhelming amount of unrelated immunoglobins present in serum.

HLA epitope based matching for transplantation

As important risk factors for transplant rejection and failure, HLA antibodies are now recognized as being specific for epitopes which can be defined structurally with amino acid differences between HLA alleles. Donor-recipient compatibility should therefore be assessed at the epitope rather than the antigen level. HLAMatchmaker is a computer algorithm that considers each HLA antigen as a series of small configurations of polymorphic residues referred to as eplets as essential components of HLA epitopes. It includes epitopes on antigens encoded by all HLA-A, B, C, DR, DQ and DP loci as well as MICA. HLA epitopes have two characteristics namely antigenicity, i.e. the reactivity with antibody and immunogenicity, i.e. the ability of eliciting an antibody response. This article addresses the relevance of determining epitope-specificities of HLA antibodies, the effect of epitope structure on technique-dependent antibody reactivity and the identification of acceptable mismatches for sensitized patients considered for transplantation. Permissible mismatching for non-sensitized patients aimed to prevent or reduce HLA antibody responses could consider epitope loads of mismatched antigens and the recently developed nonself-self paradigm of epitope immunogenicity.

High-throughput SAXS for the characterization of biomolecules in solution: a practical approach

The recent innovation of collecting X-ray scattering from solutions containing purified macromolecules in high-throughput has yet to be truly exploited by the biological community. Yet, this capability is becoming critical given that the growth of sequence and genomics data is significantly outpacing structural biology results. Given the huge mismatch in information growth rates between sequence and structural methods, their combined high-throughput and high success rate make high-throughput small angle X-ray scattering (HT-SAXS) analyses increasingly valuable. HT-SAXS connects sequence as well as NMR and crystallographic results to biological outcomes by defining the flexible and dynamic complexes controlling cell biology. Commonly falling under the umbrella of bio-SAXS, HT-SAXS data collection pipelines have or are being developed at most synchrotrons. How investigators practically get their biomolecules of interest into these pipelines, balance sample requirements and manage HT-SAXS data output format varies from facility to facility. While these features are unlikely to be standardized across synchrotron beamlines, a detailed description of HT-SAXS issues for one pipeline provides investigators with a practical guide to the general procedures they will encounter. One of the longest running and generally accessible HT-SAXS endstations is the SIBYLS beamline at the Advanced Light Source in Berkeley CA. Here we describe the current state of the SIBYLS HT-SAXS pipeline, what is necessary for investigators to integrate into it, the output format and a summary of results from 2 years of operation. Assessment of accumulated data informs issues of concentration, background, buffers, sample handling, sample shipping, homogeneity requirements, error sources, aggregation, radiation sensitivity, interpretation, and flags for concern. By quantitatively examining success and failures as a function of sample and data characteristics, we define practical concerns, considerations, and concepts for optimally applying HT-SAXS techniques to biological samples.

Anti-HLA-A, -B, -DR, -DQB1 and -DQA1 antibodies reactive epitope determination with HLAMatchmaker in multipare awaiting list for heart transplant

Human leukocyte antigen (HLA) antibodies represent a significant risk factor for transplant failure. It is very important to characterize anti-HLA antibodies as epitopes rather than antigens so that this knowledge can be applied clinically. The aim of the study was to investigate the extra reactivity patterns in sensitized multipare. Here, we have used the HLAMatchmaker program, a theoretical algorithm, to explain these unexpected antibody reactivity patterns in multipare awaiting for heart transplant. The patient was sensitized during pregnancy by alleles HLA-A(*)24:02, HLA-DRB1(*)07:01, HLA-DRB4(*)01:01, DQB1(*)02:02 and DQA1(*)02:01 mismatches with development of respective antibodies. However, the patient' sera were shown an unexpected reactivity not directed toward HLA mismatches of daughters: A(∗)23:01, A(*)24:03 and B(*)15:12 for class I and DRB4(*)01:03, DRB1(*)09:02, DRB1(*)09:01, DQB1(*)03:01, DQB1(*)03:03, DQB1(*)03:02, DQB1(*)04:02, DQB1(*)04:01 and DQB1(*)02:01 for class II. By HLAMatchmaker analysis we found that these antibodies reacted with eplet shared by antigens in single allele Luminex panels. These eplets were: 62EE, 66GKH, 70KAH, 71HS, 127K, 113YH, 144KR, 150AAH, 151AHV, 163TG and 167DG for class I and 4Q, 74RRAE, 71RRA, 98KN, 120N, and 135G, 25FT, 34HE, 41ER, 47EK2, 48LF for class II. Thus, HLAMatchmaker software together with to solid phase techniques could open new horizons for a more precise characterization of the HLA-antibodies.

SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

Basic research in HIV vaccinology is hampered by reductionist thinking

This review describes the structure-based reverse vaccinology approach aimed at developing vaccine immunogens capable of inducing antibodies that broadly neutralize HIV-1. Some basic principles of protein immunochemistry are reviewed and the implications of the extensive polyspecificity of antibodies for vaccine development are underlined. Although it is natural for investigators to want to know the cause of an effective immunological intervention, the classic notion of causality is shown to have little explanatory value for a system as complex as the immune system, where any observed effect always results from many interactions between a large number of components. Causal explanations are reductive because a single factor is singled out for attention and given undue explanatory weight on its own. Other examples of the negative impact of reductionist thinking on HIV vaccine development are discussed. These include (1) the failure to distinguish between the chemical nature of antigenicity and the biological nature of immunogenicity, (2) the belief that when an HIV-1 epitope is reconstructed by rational design to better fit a neutralizing monoclonal antibody (nMab), this will produce an immunogen able to elicit Abs with the same neutralizing capacity as the Ab used as template for designing the antigen, and (3) the belief that protection against infection can be analyzed at the level of individual molecular interactions although it has meaning only at the level of an entire organism. The numerous unsuccessful strategies that have been used to design HIV-1 vaccine immunogens are described and it is suggested that the convergence of so many negative experimental results justifies the conclusion that reverse vaccinology is unlikely to lead to the development of a preventive HIV-1 vaccine. Immune correlates of protection in vaccines have not yet been identified because this will become feasible only retrospectively once an effective vaccine exists. The finding that extensive antibody affinity maturation is needed to obtain mature anti-HIV-1 Abs endowed with a broad neutralizing capacity explains why antigens designed to fit matured Mabs are not effective vaccine immunogens since these are administered to naive recipients who possess only B-cell receptors corresponding to the germline version of the matured Abs.

Limitations to the structure-based design of HIV-1 vaccine immunogens

In spite of 25 years of intensive research, no effective human immunodeficiency virus type 1 (HIV-1) vaccine has yet been developed. One reason for this is that investigators have concentrated mainly on the structural analysis of HIV-1 antigens because they assumed that it should be possible to deduce vaccine-relevant immunogens from the structure of viral antigens bound to neutralizing monoclonal antibodies. This unwarranted assumption arises from misconceptions regarding the nature of protein epitopes and from the belief that it is justified to extrapolate from the antigenicity to the immunogenicity of proteins. Although the structure of the major HIV-1 antigenic sites has been elucidated, this knowledge has been of little use for designing an HIV-1 vaccine. Little attention has been given to the fact that protective immune responses tend to be polyclonal and involve antibodies directed to several different epitopes. It is concluded that only trial and error, empirical investigations using numerous immunization protocols may eventually allow us to identify which mixtures of immunogens are likely to be the best candidates for an HIV-1 vaccine.

Structural aspects of human leukocyte antigen class I epitopes detected by human monoclonal antibodies

This study addresses the concept that human leukocyte antigen (HLA) class I-specific alloantibodies are specific for epitopes that correspond to HLAMatchmaker-defined eplets. Eplets are essential parts of so-called structural epitopes that make contact with the 6 complementarity determining regions of an antibody. From published molecular models of crystallized protein antigen-antibody complexes, we have calculated that contact residues on structural HLA epitopes should reside within a 15-Å radius of a mismatched eplet. This study addresses the structural basis of high-frequency HLA class I epitopes reacting with human monoclonal antibodies (mAbs) derived from women sensitized during pregnancy. All mAbs were tested in Luminex assays with single HLA allele panels. The HLAMatchmaker algorithm was used to determine their specificity in context with eplet sharing between the immunizing allele and antibody-reactive alleles. To assess the autoreactive B cell origin of these antibodies, we have applied the recently developed nonself-self paradigm of epitope immunogenicity to analyze residue differences between the immunizer and the alleles of the antibody producer. A total of 9 mAbs were specific for epitopes associated with the 41T, 80NRG, 163LW, 69AA, or 80ERILR eplets. In each case, the immunizing allele had within 15 Å of the mismatched eplet, no residue differences with 1 of the alleles of the antibody producer. This observation is consistent with the concept that these mAbs originated from B cells with self HLA immunoglobulin receptors. Eplet-carrying alleles exhibited different levels of reactivity, which, when compared with the immunizing allele, ranged from high to intermediate to very low. In many cases, lower reactivities were associated with differences from self to nonself residues in surface locations within 15 Å of the specific eplet. Apparently, such locations may serve as critical contact sites for the antibody. In other cases, other residue differences did not appear to affect binding with the antibody, suggesting that these locations do not play a major role in antibody binding. For these mAbs we did not obtain convincing evidence that residue differences in hidden positions below the molecular surface had significant effects on antibody binding. These findings have increased our understanding of the structural basis of the immunogenicity and antigenicity of HLA class I epitopes and provide a basis for interpreting HLA antibody reactivity patterns in Luminex assays with single alleles.

Humoral alloimmunity in transplantation: relevance of HLA epitope antigenicity and immunogenicity

HLA mismatching is an important risk factor for antibody-mediated rejection and transplant failure. With the realization HLA antibodies recognize epitopes rather than antigens, it has become apparent that donor-recipient compatibility should be assessed at the epitope level. Recent developments have increased our understanding of the structural basis of HLA antigenicity, i.e., the reactivity with specific antibody and, immunogenicity, i.e., the ability to induce an antibody response. HLAMatchmaker is a computer algorithm that considers each HLA antigen as a series of small configurations of polymorphic residues referred to as eplets as essential components of HLA epitopes. This article addresses the relevance of determining epitope-specificities of HLA antibodies in the identification of acceptable mismatches for sensitized patients considered for transplantation. Permissible mismatching for non-sensitized patients aimed to prevent or reduce HLA antibody responses could consider epitope loads of mismatched antigens and the recently developed non-self–self paradigm of epitope immunogenicity.

Requirements for empirical immunogenicity trials, rather than structure-based design, for developing an effective HIV vaccine

The claim that it is possible to rationally design a structure-based HIV-1 vaccine is based on misconceptions regarding the nature of protein epitopes and of immunological specificity. Attempts to use reverse vaccinology to generate an HIV-1 vaccine on the basis of the structure of viral epitopes bound to monoclonal neutralizing antibodies have failed so far because it was not possible to extrapolate from an observed antigenic structure to the immunogenic structure required in a vaccine. Vaccine immunogenicity depends on numerous extrinsic factors such as the host immunoglobulin gene repertoire, the presence of various cellular and regulatory mechanisms in the immunized host and the process of antibody affinity maturation. All these factors played a role in the appearance of the neutralizing antibody used to select the epitope to be investigated as potential vaccine immunogen, but they cannot be expected to be present in identical form in the host to be vaccinated. It is possible to rationally design and optimize an epitope to fit one particular antibody molecule or to improve the paratope binding efficacy of a monoclonal antibody intended for passive immunotherapy. What is not possible is to rationally design an HIV-1 vaccine immunogen that will elicit a protective polyclonal antibody response of predetermined efficacy. An effective vaccine immunogen can only be discovered by investigating experimentally the immunogenicity of a candidate molecule and demonstrating its ability to induce a protective immune response. It cannot be discovered by determining which epitopes of an engineered antigen molecule are recognized by a neutralizing monoclonal antibody. This means that empirical immunogenicity trials rather than structural analyses of antigens offer the best hope of discovering an HIV-1 vaccine.

The antibody response to an HLA mismatch: a model for nonself-self discrimination in relation to HLA epitope immunogenicity

Antibodies to HLA mismatches are specific for epitopes rather than antigens. HLAMatchmaker considers each HLA antigen as a string of eplets that represent key elements of epitopes. Certain antibodies are specific for single eplets, but recent studies have demonstrated that epitopes defined by eplet pairs always involve one nonself-eplet and a self-eplet shared between the immunizing antigen and the antibody producer. This suggests an autoreactive component of the alloantibody response to an HLA mismatch and this report expands this concept. During B-cell development, V(H) and V(L) gene rearrangements produce a diversity of Ig receptors that can recognize epitopes on autologous proteins. It is hypothesized that B cells carry low-affinity receptors for self-HLA antigens. Their interactions with self-HLA proteins will not lead to B-cell activation or antibody production. In contrast, exposure to HLA mismatches induces often strong alloantibody responses. The activation of self-HLA-specific B cell by a nonself-eplet may require that the remainder of the structural epitope of the immunizing antigen has considerable structural similarity with one of the antibody producer's alleles. This hypothesis has been tested in molecular modelling studies with six epitopes defined by human monoclonal antibodies. In each case, one allele of the antibody producer had no or few differences with the immunizing allele in antibody-accessible positions defined by a 15 Ångstrom radius of the mismatched eplet. The other alleles of the antibody producer showed significantly greater numbers of residue differences with the immunizer (5.8 ± 2.0 versus 1.0 ± 0.6, P < 0.0001). These data support the concept that HLA antibodies originate from B cells with self-HLA immunoglobulin receptors that recognize mismatched eplets as nonself entities on immunizing antigens. The nonself-self paradigm provides a new insight of HLA epitope immunogenicity and may explain why sensitized patients have antibodies to a restricted number of mismatched epitopes.

Structural basis of neutralization of the major toxic component from the scorpion Centruroides noxius Hoffmann by a human-derived single-chain antibody fragment

It has previously been reported that several single-chain antibody fragments of human origin (scFv) neutralize the effects of two different scorpion venoms through interactions with the primary toxins of Centruroides noxius Hoffmann (Cn2) and Centruroides suffusus suffusus (Css2). Here we present the crystal structure of the complex formed between one scFv (9004G) and the Cn2 toxin, determined in two crystal forms at 2.5 and 1.9 Å resolution. A 15-residue span of the toxin is recognized by the antibody through a cleft formed by residues from five of the complementarity-determining regions of the scFv. Analysis of the interface of the complex reveals three features. First, the epitope of toxin Cn2 overlaps with essential residues for the binding of β-toxins to its Na(+) channel receptor site. Second, the putative recognition of Css2 involves mainly residues that are present in both Cn2 and Css2 toxins. Finally, the effect on the increase of affinity of previously reported key residues during the maturation process of different scFvs can be inferred from the structure. Taken together, these results provide the structural basis that explain the mechanism of the 9004G neutralizing activity and give insight into the process of directed evolution that gave rise to this family of neutralizing scFvs.

Antibody-reactive epitope determination with HLAMatchmaker and its clinical applications

Antibodies against allogeneic human leukocyte antigen (HLA) molecules are important impediments to the success of different clinical procedures including transplantation and platelet transfusion. In these settings, characterization of the repertoire of immunogenic epitopes is important for permissible mismatch determination and the identification of acceptable mismatches for sensitized patients. HLAMatchmaker is a computer algorithm that considers small configurations of polymorphic residues referred to as eplets as essential components of HLA epitopes. This review critically elaborates the concepts underlying the HLAMatchmaker and describes the usefulness of HLAMatchmaker in the clinical setting. Recent developments have increased our understanding of structural basis of HLA antigenicity (i.e. reactivity with specific antibody) and immunogenicity (i.e. its ability to induce an antibody response).

Identification of major antigenic peptide of filarial glutathione-S-transferase

In our earlier report, a 26kDa Setaria cervi glutathione-S-transferase showed significant protection (82%) in jirds infected with L3 larvae of Brugia malayi. In the present study we have identified the major antigenic epitopes in ScGST. Carboxypeptidase B has been used to digest the ScGST in to smaller fragments. The digested products were separated as four protein bands on SDS-PAGE. The smallest fragment of 6kDa (P4) from ScGST was identified as major antigenic epitope because of its significant reactivity with jird anti ScGST sera and human filarial sera in immunoblotting. The MALDI-LC/MS sequencing of ScGST P4 peptide ((5)KLTYFSIRGRGLAEPIRL(20), (22)KVPDDQQFLDDLISR(36) and (47)VFHFGQGPHHGPPR(62)) suggested that this protein band has a fragment of 5-62 residues long that matched with the N-terminal end of filarial GST. The antigenicity plot of ScGST was compared with BmGST model and both exhibited three immunogenic peaks within the first 60 residues towards N-terminal. In BmGST the N-terminal region was also detected with N-glycosylation signal peptide NAS adding to its high immunogenic property. Further, P4 showed strong reactivity with IgG1 and IL-4 response in endemic normal sera suggested its role in Th2 response which in turn is correlated with antibody dependent cell mediated cytotoxicity. Thus taking these results into account we propose 5-62 residues long N-terminal peptide of GST as a potential target for further vaccination studies against filarial infection.

What is a B-cell epitope?

The antigenicity of proteins resides in different types of antigenic determinants known as continuous and discontinuous epitopes, cryptotopes, neotopes, and mimotopes. All epitopes have fuzzy boundaries and can be identified only by their ability to bind to certain antibodies. Antigenic cross-reactivity is a common phenomenon because antibodies are always able to recognize a considerable number of related epitopes. This places severe limits to the specificity of antibodies. Antigenicity, which is the ability of an epitope to react with an antibody, must be distinguished from its immunogenicity or ability to induce antibodies in a competent vertebrate host. Failure to make this distinction partly explains why no successful peptide-based vaccines have yet been developed. Methods for predicting the epitopes of proteins are discussed and the reasons for the low success rate of epitope prediction are analyzed.