Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification

Localization of G1A1a Allergenic Domain Destroyed by Thermal Processing

Glycinin is an important allergenic protein. A1a is the acidic chain of the G1 subunit in glycinin (G1A1a), and it has strong allergenicity. In this study, we used phage display technology to express the protein of G1A1a and its overlapping fragments and an indirect enzyme-linked immunosorbent assay (iELISA) to determine the antigenicity and allergenicity of the expressed protein. After three rounds of screening, it was determined that fragment A1a-2-B-I (151SLENQLDQMPRRFYLAGNQEQEFLKYQQEQG181) is the allergenic domain of G1A1a destroyed by thermal processing. In addition, three overlapping peptides were synthesized from fragments A1a-2-B-I, and a linear epitope was found in this domain through methods including dot blot and iELISA. Peptide 2 (157DQMPRRFYLANGNQE170) showed allergenicity, and after replacing it with alanine, it was found that amino acids D157, Q158, M159, and Y164 were the key amino acids that affected its antigenicity, while Q158, M159, R162, and N168 affected allergenicity.

Antibody response to mycobacterial Rpf B protein and its immunodominant peptides in HIV-TB co-infected individuals

Diagnosis of TB at early stages of HIV infection may lead to timely intervention for improving patient outcome. Antibodies to Mycobacterium tuberculosis recombinant RpfB protein and two immunodominant peptides of Rpf B protein were evaluated in the sera of HIV +TB+, HIV+ and HIV- pulmonary TB patients by ELISA. Serum antibodies from 90 % and 65 % of HIV+TB+ patients reacted to recombinant RpfB protein and synthetic peptide RpfP1 respectively. Overall, this study shows that resuscitation promoting factor B elicits humoral antibody response in HIV+TB+ co-infected individuals and be proposed as a potential biomarker for diagnosis of HIV+TB+ patients, however further longitudinal follow up studies are warranted.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm

Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.

Evaluation of immunodominant peptides of in vivo expressed mycobacterial antigens in an ELISA-based diagnostic assay for pulmonary tuberculosis

Non-sputum-based biomarker assay is urgently required as per WHO's target product pipeline for diagnosis of tuberculosis. Therefore, the current study was designed to evaluate the utility of previously identified proteins, encoded by in vivo expressed mycobacterial transcripts in pulmonary tuberculosis, as diagnostic targets for a serodiagnostic assay. A total of 300 subjects were recruited including smear+, smear- pulmonary tuberculosis (PTB) patients, sarcoidosis patients, lung cancer patients and healthy controls. Proteins encoded by eight in vivo expressed transcripts selected from previous study including those encoded by two topmost expressed and six RD transcripts (Rv0986, Rv0971, Rv1965, Rv1971, Rv2351c, Rv2657c, Rv2674, Rv3121) were analyzed for B-cell epitopes by peptide arrays/bioinformatics. Enzyme-linked immunosorbent assay was used to evaluate the antibody response against the selected peptides in sera from PTB and controls. Overall 12 peptides were selected for serodiagnosis. All the peptides were initially screened for their antibody response. The peptide with highest sensitivity and specificity was further assessed for its serodiagnostic ability in all the study subjects. The mean absorbance values for antibody response to selected peptide were significantly higher (p<0.001) in PTB patients as compared to healthy controls; however, the sensitivity for diagnosis of PTB was 31% for smear+ and 20% for smear- PTB patients. Thus, the peptides encoded by in vivo expressed transcripts elicited a significant antibody response, but are not suitable candidates for serodiagnosis of PTB.

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning

MOTIVATION

Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.

RESULTS

Our method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix.

Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae

Background: Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs). Methodology: A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs. Principal findings: We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined. Conclusion: Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.

Engineering of Cytolethal Distending Toxin B by Its Reducing Immunogenicity and Maintaining Stability as a New Drug Candidate for Tumor Therapy; an In Silico Study

The cytolethal distending toxin (CDT), Haemophilus ducreyi, is one of the bacterial toxins that have recently been considered for targeted therapies, especially in cancer therapies. CDT is an A-B2 exotoxin. Its catalytic subunit (CdtB) is capable of inducing DNA double strand breaks, cell cycle arrest and apoptosis in host eukaryotic cells. The sequence alignment indicates that the CdtB is structurally homologyr to phosphatases and deoxyribonucleases I (DNase I). Recently, it has been found that CdtB toxicity is mainly related to its nuclease activity. The immunogenicity of CDT can reduce its effectiveness in targeted therapies. However, the toxin can be very useful if its immunogenicity is significantly reduced. Detecting hotspot ectopic residues by computational servers and then mutating them to eliminate B-cell epitopes is a promising approach to reduce the immunogenicity of foreign protein-based therapeutics. By the mentioned method, in this study, we try to reduce the immunogenicity of the CdtB- protein sequence. This study initially screened residue of the CdtB is B-cell epitopes both linearly and conformationally. By overlapping the B-cell epitopes with the excluded conserve residues, and active and enzymatic sites, four residues were allowed to be mutated. There were two mutein options that show reduced antigenicity probability. Option one was N19F, G74I, and S161F with a VaxiJen score of 0.45 and the immune epitope database (IEDB) score of 1.80, and option two was N19F, G74I, and S161W with a VaxiJen score of 0.45 and IEDB score of 1.88. The 3D structure of the proposed sequences was evaluated and refined. The structural stability of native and mutant proteins was accessed through molecular dynamic simulation. The results showed that the mutations in the mutants caused no considerable changes in their structural stability. However, mutant 1 reveals more thermodynamic stability during the simulation. The applied approaches in this study can be used as rough guidelines for finding hot spot immunogen regions in the therapeutic proteins. Our results provide a new version of CdtB that, due to reduced immunogenicity and increased stability, can be used in toxin-based drugs such as immunotoxins.

Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understanding the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. Results indicate that the immune recognition potential of SARS-CoV2 epitopes tend to vary between different ethnic groups. While the South Asians are likely to recognize higher number of CD8-specific epitopes, Europeans are likely to identify higher number of CD4-specific epitopes. We also hypothesize and provide clues that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gaging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ∼40,000 viral genomes.

Conformational epitope matching and prediction based on protein surface spiral features

Background

A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.

Results

We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.

Conclusions

The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Disruption of VirB6 Paralogs in Anaplasma phagocytophilum Attenuates Its Growth

Knowledge of the T4SS is derived from model systems, such as Agrobacterium tumefaciens. The structure of the T4SS in Rickettsiales differs from the classical arrangement. These differences include missing and duplicated components with structural alterations. Particularly, two sequenced virB6-4 genes encode unusual C-terminal structural extensions resulting in proteins of 4,322 (GenBank accession number AGR79286.1) and 9,935 (GenBank accession number ANC34101.1) amino acids. To understand how the T4SS is used in A. phagocytophilum, we describe the expression of the virB6 paralogs and explore their role as the bacteria replicate within its host cell. Conclusions about the importance of these paralogs for colonization of human and tick cells are supported by the deficient phenotype of an A. phagocytophilum mutant isolated from a sequence-defined transposon insertion library.

Many pathogenic bacteria translocate virulence factors into their eukaryotic hosts by means of type IV secretion systems (T4SS) spanning the inner and outer membranes. Genes encoding components of these systems have been identified within the order Rickettsiales based upon their sequence similarities to other prototypical systems. Anaplasma phagocytophilum strains are obligate intracellular, tick-borne bacteria that are members of this order. The organization of these components at the genomic level was determined in several Anaplasma phagocytophilum strains, showing overall conservation, with the exceptions of the virB2 and virB6 genes. The virB6 loci are characterized by the presence of four virB6 copies (virB6-1 through virB6-4) arranged in tandem within a gene cluster known as the sodB-virB operon. Interestingly, the virB6-4 gene varies significantly in length among different strains due to extensive tandem repeats at the 3′ end. To gain an understanding of how these enigmatic virB6 genes function in A. phagocytophilum, we investigated their expression in infected human and tick cells. Our results show that these genes are expressed by A. phagocytophilum replicating in both cell types and that VirB6-3 and VirB6-4 proteins are surface exposed. Analysis of an A. phagocytophilum mutant carrying the Himar1 transposon within the virB6-4 gene demonstrated that the insertion not only disrupted its expression but also exerted a polar effect on the sodB-virB operon. Moreover, the altered expression of genes within this operon was associated with the attenuated in vitro growth of A. phagocytophilum in human and tick cells, indicating the importance of these genes in the physiology of this obligate intracellular bacterium in such different environments.

IMPORTANCE Knowledge of the T4SS is derived from model systems, such as Agrobacterium tumefaciens. The structure of the T4SS in Rickettsiales differs from the classical arrangement. These differences include missing and duplicated components with structural alterations. Particularly, two sequenced virB6-4 genes encode unusual C-terminal structural extensions resulting in proteins of 4,322 (GenBank accession number AGR79286.1) and 9,935 (GenBank accession number ANC34101.1) amino acids. To understand how the T4SS is used in A. phagocytophilum, we describe the expression of the virB6 paralogs and explore their role as the bacteria replicate within its host cell. Conclusions about the importance of these paralogs for colonization of human and tick cells are supported by the deficient phenotype of an A. phagocytophilum mutant isolated from a sequence-defined transposon insertion library.

Deep learning methods improve linear B-cell epitope prediction

Background

B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes.

Results

After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available.

Conclusions

We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Laboratory Diagnosis of Human Brucellosis

The clinical presentation of brucellosis in humans is variable and unspecific, and thus, laboratory corroboration of the diagnosis is essential for the patient's proper treatment. The diagnosis of brucellar infections can be made by culture, serological tests, and nucleic acid amplification assays. Modern automated blood culture systems enable detection of acute cases of brucellosis within the routine 5- to 7-day incubation protocol employed in clinical microbiology laboratories, although a longer incubation and performance of blind subcultures may be needed for protracted cases. Serological tests, though they lack specificity and provide results that may be difficult to interpret in individuals repeatedly exposed to Brucella organisms, nevertheless remain a diagnostic cornerstone in resource-poor countries. Nucleic acid amplification assays combine exquisite sensitivity, specificity, and safety and enable rapid diagnosis of the disease. However, long-term persistence of positive molecular test results in patients that have apparently fully recovered is common and has unclear clinical significance and therapeutic implications. Therefore, as long as there are no sufficiently validated commercial tests or studies that demonstrate an adequate interlaboratory reproducibility of the different homemade PCR assays, cultures and serological methods will remain the primary tools for the diagnosis and posttherapeutic follow-up of human brucellosis.

PEPOP 2.0: new approaches to mimic non-continuous epitopes

BACKGROUND

Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody.

RESULTS

We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.

CONCLUSION

Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family

Background

The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily

Results

The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily

Conclusions

Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.

Targeting Tumor Markers with Antisense Peptides: An Example of Human Prostate Specific Antigen

The purpose of this paper was to outline the development of short peptide targeting of the human prostate specific antigen (hPSA), and to evaluate its effectiveness in staining PSA in human prostate cancer tissue. The targeting of the hPSA antigen by means of antisense peptide AVRDKVG was designed according to a three-step method involving: 1. The selection of the molecular target (hPSA epitope), 2. the modeling of an antisense peptide (paratope) based on the epitope sequence, and 3. the spectroscopic evaluation of sense–antisense peptide binding. We then modified standard hPSA immunohistochemical staining practice by using a biotinylated antisense peptide instead of the standard monoclonal antibody and compared the results of both procedures. Immunochemical testing on human tissue showed the applicability of the antisense peptide technology to human molecular targets. This methodology represents a new approach to deriving peptide ligands and potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines.

Non-destructive detection of matrix stabilization correlates with enhanced mechanical properties of self-assembled articular cartilage

Tissue engineers rely on expensive, time-consuming, and destructive techniques to monitor the composition, microstructure, and function of engineered tissue equivalents. A non-destructive solution to monitor tissue quality and maturation would greatly reduce costs and accelerate the development of tissue-engineered products. The objectives of this study were to (a) determine whether matrix stabilization with exogenous lysyl oxidase-like protein-2 (LOXL2) with recombinant hyaluronan and proteoglycan link protein-1 (LINK) would result in increased compressive and tensile properties in self-assembled articular cartilage constructs, (b) evaluate whether label-free, non-destructive fluorescence lifetime imaging (FLIm) could be used to infer changes in both biochemical composition and biomechanical properties, (c) form quantitative relationships between destructive and non-destructive measurements to determine whether the strength of these correlations is sufficient to replace destructive testing methods, and (d) determine whether support vector machine (SVM) learning can predict LOXL2-induced collagen crosslinking. The combination of exogenous LOXL2 and LINK proteins created a synergistic 4.9-fold increase in collagen crosslinking density and an 8.3-fold increase in tensile strength as compared with control (CTL). Compressive relaxation modulus was increased 5.9-fold with addition of LOXL2 and 3.4-fold with combined treatments over CTL. FLIm parameters had strong and significant correlations with tensile properties (R²  = 0.82; p < 0.001) and compressive properties (R²  = 0.59; p < 0.001). SVM learning based on FLIm-derived parameters was capable of automating tissue maturation assessment with a discriminant ability of 98.4%. These results showed marked improvements in mechanical properties with matrix stabilization and suggest that FLIm-based tools have great potential for the non-destructive assessment of tissue-engineered cartilage.

de la Fuente I. Weighted lambda superstrings applied to vaccine design

We generalize the notion of λ-superstrings, presented in a previous paper, to the notion of weighted λ-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted λ-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic λ-superstrings that at the same time present similar conformations to the Nef virus proteins.

Diversity analysis of MSP1 identifies conserved epitope organization in block 2 amidst high sequence variability in Indian Plasmodium falciparum isolates

Background

Despite its immunogenicity, the polymorphic nature of merozoite surface protein 1, an important vaccine candidate for Plasmodium falciparum malaria, remains a concern. This study analyses the impact of genetic variability and parasite population structure on epitope organization of different MSP1 segments.

Methods

Altogether 98 blood samples collected from P. falciparum infected mild and severe malaria patients of Chhattisgarh and West Bengal were used to sequence regions encoding block 2 and MSP1-19 of msp1. Sequences were analysed using MEGA7, DnaSPv5, Arlequin3.5 and BepiPred.

Results

All three major MSP1 block 2 allele families namely K1, MAD20 and RO33 were detected in the samples and they together resulted in 41 indel variants. Chhattisgarh samples displayed an average MOI of 2.07 ± 1.59 which was higher in mild malaria and in age group < 18 years. Ultra-structure of block 2 alleles revealed that mutation and repeat expansion were two major mechanisms responsible for allelic variability of K1 and MAD20. Regions flanking block 2 were highly variable in Chhattisgarh with average mismatch differences (k) ranging from 1.198 to 5.156 for three families. In contrast, region encompassing MSP1-19 exhibited limited heterogeneity (k_Chhattisgarh = 1.45, k_{West Bengal} = 1.363). Of the 50 possible B cell linear epitopes predicted from block 2 variants, 94.9% (131 of 138) of the parasites could be represented by three conserved antigens.

Conclusions

Present data indicates that natural selection and transmission intensity jointly play a role in controlling allelic diversity of MSP1 in Indian parasite isolates. Despite remarkable genetic variability, a limited number of predominant and conserved epitopes are present in Indian parasite isolates reinstating the importance of MSP1 as a promising malaria vaccine candidate.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2592-y) contains supplementary material, which is available to authorized users.

DRREP: deep ridge regressed epitope predictor

INTRODUCTION

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP).

RESULTS

DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702.

CONCLUSION

DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach

Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNAStar Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23-28, 39-46, 58-64, 91-118, 131-136, 145-154, 159-165, 176-183, 290-299, 309-320 and 338-344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119-127, 194-202, 210-218, 239-250 and 279-290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies.

Molecular evolution of the capsid gene in human norovirus genogroup II

Capsid protein of norovirus genogroup II (GII) plays crucial roles in host infection. Although studies on capsid gene evolution have been conducted for a few genotypes of norovirus, the molecular evolution of norovirus GII is not well understood. Here we report the molecular evolution of all GII genotypes, using various bioinformatics techniques. The time-scaled phylogenetic tree showed that the present GII strains diverged from GIV around 1630CE at a high evolutionary rate (around 10(-3) substitutions/site/year), resulting in three lineages. The GII capsid gene had large pairwise distances (maximum > 0.39). The effective population sizes of the present GII strains were large (>10(2)) for about 400 years. Positive (20) and negative (over 450) selection sites were estimated. Moreover, some linear and conformational B-cell epitopes were found in the deduced GII capsid protein. These results suggested that norovirus GII strains rapidly evolved with high divergence and adaptation to humans.

Design and Evaluation of a Multi-Epitope Peptide of Human Metapneumovirus

OBJECTIVES

No licensed vaccines or therapeutic agents for human metapneumovirus (hMPV) infection exist to date. We aimed to construct a multi-epitope peptide (MEP) of hMPV to show promising results for epitope-based vaccine development.

METHODS

Six independent algorithms were screened to predict B-cell epitopes of hMPV, and three algorithms were used to predict cytotoxic T lymphocyte and T helper (Th) lymphocyte epitopes. Predicted epitopes were assembled in series with the spacers GPGPG and KK introduced, termed MEP. Recombinant mep genes were inserted into pET32a(+) plasmid and expressed in Escherichia coli strain BL21 (DE3). BALB/c mice were immunized with MEP with different adjuvants. Antibody titer, lymphocyte proliferation, cytotoxic T lymphocyte (CTL) activity and splenocyte cytokines were detected 2 weeks later after the last immunization. Microneutralization assay was used to detect neutralizing antibodies.

RESULTS

Six B-cell epitopes, four CTL epitopes and two Th epitopes were screened to construct the mep gene. Expressed MEP induced >104 antibodies in BALB/c mice, and produced anti-MEP antibody reacting with hMPV strains specifically as detected in indirect fluorescent assay (the titer was 160). The lymphocyte proliferation index, CTL activity and splenocyte cytokines of the MEP immunization groups were higher than in the control group (p < 0.05). Both IgG1 and IgG2a antibodies could be detected in the different groups, and balanced Th1/Th2 cytokines were secreted by splenocytes in these groups. The mean neutralizing titers of the MEP+CpG ODN, MEP+Alum and MEP+Alum+ CpG ODN groups were 87 (95% CI 50-126), 93 (95% CI 67-121) and 96 (95% CI 69-147), respectively.

CONCLUSION

MEP of hMPV elicited both strong humoral immunity and cell-mediated immunity in mice. The anti-MEP serum could neutralize hMPV infection in vitro. Joint use of CpG ODN and aluminum hydroxide adjuvants obtained the best immune effects. This study may contribute to hMPV epitope-based vaccine development.

Sleep reverts changes in human gray and white matter caused by wake-dependent training

Learning leads to rapid microstructural changes in gray (GM) and white (WM) matter. Do these changes continue to accumulate if task training continues, and can they be reverted by sleep? We addressed these questions by combining structural and diffusion weighted MRI and high-density EEG in 16 subjects studied during the physiological sleep/wake cycle, after 12 h and 24 h of intense practice in two different tasks, and after post-training sleep. Compared to baseline wake, 12 h of training led to a decline in cortical mean diffusivity. The decrease became even more significant after 24 h of task practice combined with sleep deprivation. Prolonged practice also resulted in decreased ventricular volume and increased GM and WM subcortical volumes. All changes reverted after recovery sleep. Moreover, these structural alterations predicted cognitive performance at the individual level, suggesting that sleep's ability to counteract performance deficits is linked to its effects on the brain microstructure. The cellular mechanisms that account for the structural effects of sleep are unknown, but they may be linked to its role in promoting the production of cerebrospinal fluid and the decrease in synapse size and strength, as well as to its recently discovered ability to enhance the extracellular space and the clearance of brain metabolites.

Classification epitopes in groups based on their protein family

Background

The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.

Results

We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.

Conclusions

We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

Molecular Evolution of the Capsid Gene in Norovirus Genogroup I

We studied the molecular evolution of the capsid gene in all genotypes (genotypes 1–9) of human norovirus (NoV) genogroup I. The evolutionary time scale and rate were estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. We also performed selective pressure analysis and B-cell linear epitope prediction in the deduced NoV GI capsid protein. Furthermore, we analysed the effective population size of the virus using Bayesian skyline plot (BSP) analysis. A phylogenetic tree by MCMC showed that NoV GI diverged from the common ancestor of NoV GII, GIII, and GIV approximately 2,800 years ago with rapid evolution (about 10⁻³ substitutions/site/year). Some positive selection sites and over 400 negative selection sites were estimated in the deduced capsid protein. Many epitopes were estimated in the deduced virus capsid proteins. An epitope of GI.1 may be associated with histo-blood group antigen binding sites (Ser377, Pro378, and Ser380). Moreover, BSP suggested that the adaptation of NoV GI strains to humans was affected by natural selection. The results suggested that NoV GI strains evolved rapidly and date back to many years ago. Additionally, the virus may have undergone locally affected natural selection in the host resulting in its adaptation to humans.

Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull

The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.

A linear-RBF multikernel SVM to classify big text corpora

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

EBV-Associated Cancer and Autoimmunity: Searching for Therapies

Epstein-Barr virus (EBV) infects B-, T-, and NK cells and has been associated not only with a wide range of lymphoid malignancies but also with autoimmune diseases such as lupus erythematosus, rheumatoid arthritis and, in particular, multiple sclerosis. Hence, effective immunotherapeutic approaches to eradicate EBV infection might overthrow cancer and autoimmunity incidence. However, currently no effective anti-EBV immunotherapy is available. Here we use the concept that protein immunogenicity is allocated in rare peptide sequences and search the Epstein-Barr nuclear antigen 1 (EBNA1) sequence for peptides unique to the viral protein and absent in the human host. We report on a set of unique EBV EBNA1 peptides that might be used in designing peptide-based therapies able to specifically hitting the virus or neutralizing pathogenic autoantibodies.

Prediction of Antibody Epitopes

Antibodies recognize their cognate antigens in a precise and effective way. In order to do so, they target regions of the antigenic molecules that have specific features such as large exposed areas, presence of charged or polar atoms, specific secondary structure elements, and lack of similarity to self-proteins. Given the sequence or the structure of a protein of interest, several methods exploit such features to predict the residues that are more likely to be recognized by an immunoglobulin. Here, we present two methods (BepiPred and DiscoTope) to predict linear and discontinuous antibody epitopes from the sequence and/or the three-dimensional structure of a target protein.

In silico prediction of the T-cell and IgE-binding epitopes of Per a 6 and Bla g 6 allergens in cockroaches

Per a 6 and Bla g 6 are cockroach allergens found in Periplaneta americana and Blattella germanica, respectively. The objective of the present study was to predict the B‑ and T‑cell epitopes of the Per a 6 and Bla g 6 allergens. Three immunoinformatics tools, the DNAStar Protean system, the Bioinformatics Predicted Antigenic Peptides system and the BepiPred 1.0 server, were used to predict the potential B‑cell epitopes, while Net‑MHCIIpan‑2.0 and NetMHCII‑2.2 were used to predict the T‑cell epitopes of the two allergens. As a result, seven peptides were predicted in the Per a 6 allergen and seven peptides were predicted in the Bla g 6 allergen in the B‑cell epitope predictions. In the T‑cell prediction, the Per a 6 allergen was predicted to have nine strongly binding nonamer core epitope sequences (IC50<50 nm) and 28 weakly binding sequences (50 nm<IC50<500 nm), while the Bla g 6 allergen was predicted to have nine strong binders and 25 weak binders. These results may be useful for peptide‑based vaccine designs.

The structure and dynamics of BmR1 protein from Brugia malayi: in silico approaches

Brugia malayi is a filarial nematode, which causes lymphatic filariasis in humans. In 1995, the disease has been identified by the World Health Organization (WHO) as one of the second leading causes of permanent and long-term disability and thus it is targeted for elimination by year 2020. Therefore, accurate filariasis diagnosis is important for management and elimination programs. A recombinant antigen (BmR1) from the Bm17DIII gene product was used for antibody-based filariasis diagnosis in "Brugia Rapid". However, the structure and dynamics of BmR1 protein is yet to be elucidated. Here we study the three dimensional structure and dynamics of BmR1 protein using comparative modeling, threading and ab initio protein structure prediction. The best predicted structure obtained via an ab initio method (Rosetta) was further refined and minimized. A total of 5 ns molecular dynamics simulation were performed to investigate the packing of the protein. Here we also identified three epitopes as potential antibody binding sites from the molecular dynamics average structure. The structure and epitopes obtained from this study can be used to design a binder specific against BmR1, thus aiding future development of antigen-based filariasis diagnostics to complement the current diagnostics.

Benchmarking B-cell epitope prediction with quantitative dose-response data on antipeptide antibodies: towards novel pharmaceutical product development

B-cell epitope prediction can enable novel pharmaceutical product development. However, a mechanistically framed consensus has yet to emerge on benchmarking such prediction, thus presenting an opportunity to establish standards of practice that circumvent epistemic inconsistencies of casting the epitope prediction task as a binary-classification problem. As an alternative to conventional dichotomous qualitative benchmark data, quantitative dose-response data on antibody-mediated biological effects are more meaningful from an information-theoretic perspective in the sense that such effects may be expressed as probabilities (e.g., of functional inhibition by antibody) for which the Shannon information entropy (SIE) can be evaluated as a measure of informativeness. Accordingly, half-maximal biological effects (e.g., at median inhibitory concentrations of antibody) correspond to maximally informative data while undetectable and maximal biological effects correspond to minimally informative data. This applies to benchmarking B-cell epitope prediction for the design of peptide-based immunogens that elicit antipeptide antibodies with functionally relevant cross-reactivity. Presently, the Immune Epitope Database (IEDB) contains relatively few quantitative dose-response data on such cross-reactivity. Only a small fraction of these IEDB data is maximally informative, and many more of them are minimally informative (i.e., with zero SIE). Nevertheless, the numerous qualitative data in IEDB suggest how to overcome the paucity of informative benchmark data.

Rapid replacement of human respiratory syncytial virus A with the ON1 genotype having 72 nucleotide duplication in G gene

•

We investigated the prevalence of HRSV during 2008–2013.

•

Novel HRSV-A ON1 genotype was emerged in August 2011.

•

After 1 year of emergence in 2012–2013, 94.6% was replaced with novel ON1 genotype.

•

Evolutionary dynamics also drastically increased in 2011.

•

The result of epitope prediction shows the possibilities of antigenic variation.

Human respiratory syncytial virus (HRSV) is the main cause of severe respiratory illness in young children and elderly people. We investigated the genetic characteristics of the circulating HRSV subgroup A (HRSV-A) to determine the distribution of genotype ON1, which has a 72-nucleotide duplication in attachment G gene. We obtained 456 HRSV-A positive samples between October 2008 and February 2013, which were subjected to sequence analysis. The first ON1 genotype was discovered in August 2011 and 273 samples were identified as ON1 up to February 2013. The prevalence of the ON1 genotype increased rapidly from 17.4% in 2011–2012 to 94.6% in 2012–2013. The mean evolutionary rate of G protein was calculated as 3.275 × 10⁻³ nucleotide substitution/site/year and several positively selected sites for amino acid substitutions were located in the predicted epitope region. This basic and important information may facilitate a better understanding of HRSV epidemiology and evolution.

Identification of B-cell epitopes in an antigen for inducing specific class of antibodies

Background

In the past, numerous methods have been developed for predicting antigenic regions or B-cell epitopes that can induce B-cell response. To the best of authors’ knowledge, no method has been developed for predicting B-cell epitopes that can induce a specific class of antibody (e.g., IgA, IgG) except allergenic epitopes (IgE). In this study, an attempt has been made to understand the relation between primary sequence of epitopes and the class of antibodies generated.

Results

The dataset used in this study has been derived from Immune Epitope Database and consists of 14725 B-cell epitopes that include 11981 IgG, 2341 IgE, 403 IgA specific epitopes and 22835 non-B-cell epitopes. In order to understand the preference of residues or motifs in these epitopes, we computed and compared amino acid and dipeptide composition of IgG, IgE, IgA inducing epitopes and non-B-cell epitopes. Differences in composition profiles of different classes of epitopes were observed, and few residues were found to be preferred. Based on these observations, we developed models for predicting antibody class-specific B-cell epitopes using various features like amino acid composition, dipeptide composition, and binary profiles. Among these, dipeptide composition-based support vector machine model achieved maximum Matthews correlation coefficient of 0.44, 0.70 and 0.45 for IgG, IgE and IgA specific epitopes respectively. All models were developed on experimentally validated non-redundant dataset and evaluated using five-fold cross validation. In addition, the performance of dipeptide-based model was also evaluated on independent dataset.

Conclusion

Present study utilizes the amino acid sequence information for predicting the tendencies of antigens to induce different classes of antibodies. For the first time, in silico models have been developed for predicting B-cell epitopes, which can induce specific class of antibodies. A web service called IgPred has been developed to serve the scientific community. This server will be useful for researchers working in the field of subunit/epitope/peptide-based vaccines and immunotherapy (http://crdd.osdd.net/raghava/igpred/).

Reviewers

This article was reviewed by Dr. M Michael Gromiha, Dr Christopher Langmead (nominated by Dr Robert Murphy) and Dr Lina Ma (nominated by Dr Zhang Zhang).

Mature Epitope Density--a strategy for target selection based on immunoinformatics and exported prokaryotic proteins

Background

Current immunological bioinformatic approaches focus on the prediction of allele-specific epitopes capable of triggering immunogenic activity. The prediction of major histocompatibility complex (MHC) class I epitopes is well studied, and various software solutions exist for this purpose. However, currently available tools do not account for the concentration of epitope products in the mature protein product and its relation to the reliability of target selection.

Results

We developed a computational strategy based on measuring the epitope's concentration in the mature protein, called Mature Epitope Density (MED). Our method, though simple, is capable of identifying promising vaccine targets. Our online software implementation provides a computationally light and reliable analysis of bacterial exoproteins and their potential for vaccines or diagnosis projects against pathogenic organisms. We evaluated our computational approach by using the Mycobacterium tuberculosis (Mtb) H37Rv exoproteome as a gold standard model. A literature search was carried out on 60 out of 553 Mtb's predicted exoproteins, looking for previous experimental evidence concerning their possible antigenicity. Half of the 60 proteins were classified as highest scored by the MED statistic, while the other half were classified as lowest scored. Among the lowest scored proteins, ~13% were confirmed as not related to antigenicity or not contributing to the bacterial pathogenicity, and 70% of the highest scored proteins were confirmed as related. There was no experimental evidence of antigenic or pathogenic contributions for three of the highest MED-scored Mtb proteins. Hence, these three proteins could represent novel putative vaccine and drug targets for Mtb. A web version of MED is publicly available online at http://med.mmci.uni-saarland.de/.

Conclusions

The software presented here offers a practical and accurate method to identify potential vaccine and diagnosis candidates against pathogenic bacteria by "reading" results from well-established reverse vaccinology software in a novel way, considering the epitope's concentration in the mature portion of the protein.

Prediction of B-cell epitopes using evolutionary information and propensity scales

Background

Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.

Methods

We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results.

Results

In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066.

Conclusions

Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.

Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics

Background

A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.

Results

We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen.

Conclusions

Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

In silico identification of IgE-binding epitopes of osmotin protein

The identification of B-cell epitopes is an important step to study the antigen- antibody interactions for diagnosis and therapy. The present study aimed to identify B- cell epitopes of osmotin using bioinformatic tools and further modify these regions to study the allergenic property. B-cell epitopes were predicted based on amino acid physicochemical properties. Three single point mutations M1, M2, and M3 and a multiple point mutant (M123) were selected to disrupt the IgE binding. These mutants were cloned, expressed and proteins purified to homogeneity. The IgE binding of the purified proteins was evaluated by ELISA and ELISA inhibition with patients' sera. Three regions of osmotin M1 (57–70 aa), M2 (72–85 aa) and M3 (147–165 aa) were identified as potential antibody recognition sites using in silico tools. The sequence similarity search of the predicted epitopes of osmotin using Structural Database of Allergenic proteins (SDAP) showed similarity with known allergens from tomato, kiwifruit, bell pepper, apple, mountain cedar and cypress. Mutants M1, M2 and M3 showed up to 72%, 60% and 76% reduction, respectively in IgE binding whereas M123 showed up to 90% reduction with patients' sera. The immunoblot of M123 mutant showed 40% reduction in spot density as compared to osmotin. All mutants showed decreased inhibition potency with M123 exhibiting lowest potency of 32% with osmotin positive pooled patients' sera. The three B- cell epitopes of osmotin predicted by in silico method correlated with the experimental approach. The mutant M123 showed a reduction of 90% in IgE binding. The present method may be employed for prediction of B- cell epitopes of allergenic proteins.