VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Overview of computational vaccinology: vaccine development through information technology

Pathogenic organisms, causes of various infectious diseases, possess a rich repository of antigenic proteins that engender an immune response in a host. These types of diseases are usually treated with the use of pharmaceuticals; unfortunately, many of these also have a potential to induce fatal side effects, especially allergic responses in the diseased host. In addition, many pathogens evolve (by selective survival) single or multi-drug resistance (MDR). Therefore, a means to prevent the host from becoming susceptible to the pathogen from the onset, rather than trying to devise pharmacologic protocols to treat an ongoing infection, are increasingly seen as desirable to reduce the incidence of infectious diseases altogether. To this end, cost-effective development and use of "safe" vaccines is key. This paper provides an overview on the new and expanding area of computational vaccinology and a brief background on pathogen antigenicity, identification of pathogen-specific antigens, and screening of candidate antigens using various tools and databases developed in the recent past.

In silico analysis of Acinetobacter baumannii phospholipase D as a subunit vaccine candidate

The rate of human health care-associated infections caused by Acinetobacter baumannii has increased significantly in recent years for its remarkable resistance to desiccation and most antibiotics. Phospholipases, capable of destroying a phospholipid substrate, are heterologous group of enzymes which are believed to be the bacterial virulence determinants. There is a need for in silico studies to identify potential vaccine candidates. A. baumannii phospholipase D (PLD) role has been proved in increasing organism's resistance to human serum, destruction of host epithelial cell and pathogenesis in murine model. In this in silico study high potentials of A. baumannii PLD in elicitation of humoral and cellular immunities were elucidated. Thermal stability, long half-life, non-similarity to human and gut flora proteome and non-allergenicity were in a list of A. baumannii PLD positive properties. Potential epitopic sequences were also identified that could be used as peptide vaccines against A. baumannii and various other human bacterial pathogens.

Protectome analysis: a new selective bioinformatics tool for bacterial vaccine candidate discovery

New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called "Protectome space," and using such "protective signatures" for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.

Computational vaccinology and epitope vaccine design by immunoinformatics

Human immune system includes variety of different cells and molecules correlating with other body systems. These instances complicate the analysis of the system; particularly in postgenomic era by introducing more amount of data, the complexity is increased and necessity of using computational approaches to process and interpret them is more tangible.Immunoinformatics as a subset of bioinformatics is a new approach with variety of tools and databases that facilitate analysis of enormous amount of immunologic data obtained from experimental researches. In addition to directing the insight regarding experiment selections, it helps new thesis design which was not feasible with conventional methods due to the complexity of data. Considering this features immunoinformatics appears to be one of the fields that accelerate the immunological research progression.In this study we discuss advances in genomics and vaccine design and their relevance to the development of effective vaccines furthermore several division of this field and available tools in each item are introduced.

Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach

Human coronavirus (HCoV), a member of Coronaviridae family, is the causative agent of upper respiratory tract infections and “atypical pneumonia”. Despite severe epidemic outbreaks on several occasions and lack of antiviral drug, not much progress has been made with regard to an epitope-based vaccine designed for HCoV. In this study, a computational approach was adopted to identify a multiepitope vaccine candidate against this virus that could be suitable to trigger a significant immune response. Sequences of the spike proteins were collected from a protein database and analyzed with an in silico tool, to identify the most immunogenic protein. Both T cell immunity and B cell immunity were checked for the peptides to ensure that they had the capacity to induce both humoral and cell-mediated immunity. The peptide sequence from 88–94 amino acids and the sequence KSSTGFVYF were found as the most potential B cell and T cell epitopes, respectively. Furthermore, conservancy analysis was also done using in silico tools and showed a conservancy of 64.29% for all epitopes. The peptide sequence could interact with as many as 16 human leukocyte antigens (HLAs) and showed high cumulative population coverage, ranging from 75.68% to 90.73%. The epitope was further tested for binding against the HLA molecules, using in silico docking techniques, to verify the binding cleft epitope interaction. The allergenicity of the epitopes was also evaluated. This computational study of design of an epitope-based peptide vaccine against HCoVs allows us to determine novel peptide antigen targets in spike proteins on intuitive grounds, albeit the preliminary results thereof require validation by in vitro and in vivo experiments.

In silico prediction of Gallibacterium anatis pan-immunogens

The Gram-negative bacterium Gallibacterium anatis is a major cause of salpingitis and peritonitis in commercial egg-layers, leading to reduced egg production and increased mortality. Unfortunately, widespread multidrug resistance and antigenic diversity makes it difficult to control infections and novel prevention strategies are urgently needed. In this study, a pan-genomic reverse vaccinology (RV) approach was used to identify potential vaccine candidates. Firstly, the genomes of 10 selected Gallibacterium strains were analyzed and proteins selected on the following criteria; predicted surface-exposure or secretion, none or one transmembrane helix (TMH), and presence in six or more of the 10 genomes. In total, 42 proteins were selected. The genes encoding 27 of these proteins were successfully cloned in Escherichia coli and the proteins expressed and purified. To reduce the number of vaccine candidates for in vivo testing, each of the purified recombinant proteins was screened by ELISA for their ability to elicit a significant serological response with serum from chickens that had been infected with G. anatis. Additionally, an in silico prediction of the protective potential was carried out based on a protein property prediction method. Of the 27 proteins, two novel putative immunogens were identified; Gab_1309 and Gab_2312. Moreover, three previously characterized virulence factors; GtxA, FlfA and Gab_2156, were identified. Thus, by combining the pan-genomic RV approach with subsequent in vitro and in silico screening, we have narrowed down the pan-proteome of G. anatis to five vaccine candidates. Importantly, preliminary immunization trials indicated an in vivo protective potential of GtxA-N, FlfA and Gab_1309.

Prediction of epitope-based peptides for the utility of vaccine development from fusion and glycoprotein of nipah virus using in silico approach

This study aims to design epitope-based peptides for the utility of vaccine development by targeting glycoprotein G and envelope protein F of Nipah virus (NiV) that, respectively, facilitate attachment and fusion of NiV with host cells. Using various databases and tools, immune parameters of conserved sequence(s) from G and F proteins of different isolates of NiV were tested to predict probable epitope(s). Binding analyses of the peptides with MHC class-I and class-II molecules, epitope conservancy, population coverage, and linear B cell epitope prediction were analyzed. Predicted peptides interacted with seven or more MHC alleles and illustrated population coverage of more than 99% and 95%, for G and F proteins, respectively. The predicted class-I nonamers, SLIDTSSTI and EWISIVPNF, superimposed on the putative decameric B cell epitopes, were also identified as core sequences of the most probable class-II 15-mer peptides GPKVSLIDTSSTITI and EWISIVPNFILVRNT. These peptides were further validated for their binding to specific HLA alleles using in silico docking technique. Our in silico analysis suggested that the predicted epitopes, either GPKVSLIDTSSTITI or EWISIVPNFILVRNT, could be a better choice as universal vaccine component against NiV irrespective of different isolates which may elicit both humoral and cell-mediated immunity.

De Novo modeling of Envelope 2 protein of HCV isolated from Pakistani patient and epitopes prediction for vaccine development

Hepatitis C virus (HCV) is a universal health issue and a significant risk factor leading to hepatocellular carcinoma. HCV has infected approximately 170 million individuals worldwide. It is a member of Flaviviridae with positive sense RNA genome. In the absence of any effective vaccine against HCV, pegylated interferon with ribavirin is the standard of treatment against HCV infection. In this study, sequence and structural analysis of envelope 2 (E2) protein was performed which was isolated from patients of HCV genotype 3a in Pakistan. Then, epitopes were predicted which were specific for both B-cells and T-cells. Later, conservancy of epitopes was checked with the HCV 3a and 1a sequences from different countries. A total of 6 conserved epitopes were found from extra-membranous regions of E2 protein. Presence of conserved epitopes in E2 protein generates the possibility that these epitopes can be used to elicit the immune response against HCV.

Comparative genomics and immunoinformatics approach for the identification of vaccine candidates for enterohemorrhagic Escherichia coli O157:H7

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 strains are major human food-borne pathogens, responsible for bloody diarrhea and hemolytic-uremic syndrome worldwide. Thus far, there is no vaccine for humans against EHEC infections. In this study, a comparative genomics analysis was performed to identify EHEC-specific antigens useful as potential vaccines. The genes present in both EHEC EDL933 and Sakai strains but absent in nonpathogenic E. coli K-12 and HS strains were subjected to an in silico analysis to identify secreted or surface-expressed proteins. We obtained a total of 65 gene-encoding protein candidates, which were subjected to immunoinformatics analysis. Our criteria of selection aided in categorizing the candidates as high, medium, and low priority. Three members of each group were randomly selected and cloned into pVAX-1. Candidates were pooled accordingly to their priority group and tested for immunogenicity against EHEC O157:H7 using a murine model of gastrointestinal infection. The high-priority (HP) pool, containing genes encoding a Lom-like protein (pVAX-31), a putative pilin subunit (pVAX-12), and a fragment of the type III secretion structural protein EscC (pVAX-56.2), was able to induce the production of EHEC IgG and sIgA in sera and feces. HP candidate-immunized mice displayed elevated levels of Th2 cytokines and diminished cecum colonization after wild-type challenge. Individually tested HP vaccine candidates showed that pVAX-12 and pVAX-56.2 significantly induced Th2 cytokines and production of fecal EHEC sIgA, with pVAX-56.2 reducing EHEC cecum colonization. We describe here a bioinformatics approach able to identify novel vaccine candidates potentially useful for preventing EHEC O157:H7 infections.

In silico analysis of chimeric TF, Omp31 and BP26 fragments of Brucella melitensis for development of a multi subunit vaccine candidate

OBJECTIVE(S)

Brucellosis, especially caused by Brucella melitensis, remains one of the most common zoonotic diseases worldwide with more than 500,000 human cases reported annually. The commonly used live attenuated vaccine in ovine brucellosis prophylaxis is B. melitensis Rev1. But due to different problems caused by the administration of this vaccine, a protective subunit vaccine against B. melitensis is strongly demanded. Brucella BP26, Omp31 and TF proteins have shown a considerable potential as protective antigens for brucellosis. Chimeric proteins carrying epitopes or adjuvant sequences increase the possibility of eliciting a broad cellular or humoral immune response. In silico tools are highly suited to study, design and evaluate vaccine strategies.

MATERIALS AND METHODS

In this study, a synthetic chimeric gene, encoding TF, BP26 (93-111) and Omp31(48-74) was designed. In order to predict the 3D structure of protein, modeling was carried out.

RESULTS

Validation results showed that 91.1% of residues lie in favored or additional allowed region of Ramachandran plot. The epitopes in the chimeric protein are likely to induce both the B-cell and T-cell mediated immune responses. Conclusion : The chimeric protein may be used as multi subunit for development of Brucella vaccine candidates.

Understanding the evolutionary structural variability and target specificity of tick salivary Kunitz peptides using next generation transcriptome data

BACKGROUND

Ticks are blood-sucking arthropods and a primary function of tick salivary proteins is to counteract the host's immune response. Tick salivary Kunitz-domain proteins perform multiple functions within the feeding lesion and have been classified as venoms; thereby, constituting them as one of the important elements in the arms race with the host. The two main mechanisms advocated to explain the functional heterogeneity of tick salivary Kunitz-domain proteins are gene sharing and gene duplication. Both do not, however, elucidate the evolution of the Kunitz family in ticks from a structural dynamic point of view. The Red Queen hypothesis offers a fruitful theoretical framework to give a dynamic explanation for host-parasite interactions. Using the recent salivary gland Ixodes ricinus transcriptome we analyze, for the first time, single Kunitz-domain encoding transcripts by means of computational, structural bioinformatics and phylogenetic approaches to improve our understanding of the structural evolution of this important multigenic protein family.

RESULTS

Organizing the I. ricinus single Kunitz-domain peptides based on their cysteine motif allowed us to specify a putative target and to relate this target specificity to Illumina transcript reads during tick feeding. We observe that several of these Kunitz peptide groups vary in their translated amino acid sequence, secondary structure, antigenicity, and intrinsic disorder, and that the majority of these groups are subject to a purifying (negative) selection. We finalize by describing the evolution and emergence of these Kunitz peptides. The overall interpretation of our analyses discloses a rapidly emerging Kunitz group with a distinct disulfide bond pattern from the I. ricinus salivary gland transcriptome.

CONCLUSIONS

We propose a model to explain the structural and functional evolution of tick salivary Kunitz peptides that we call target-oriented evolution. Our study reveals that combining analytical approaches (transcriptomes, computational, bioinformatics and phylogenetics) improves our understanding of the biological functions of important salivary gland mediators during tick feeding.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

Common antigens prediction in bacterial bioweapons: a perspective for vaccine design

Bioweapons (BWs) are a serious threat to mankind and the lack of efficient vaccines against bacterial bioweapons (BBWs) further worsens the situation in face of BW attack. Experts believe that difficulties in detection and ease in dissemination of deadly pathogens make BW a better option for attack compared to nuclear weapons. Molecular biology techniques facilitate the use of genetically modified BBWs thus creating uncertainty on which bacteria will be used for BW attack. In the present work, available resources such as proteomic sequences of BBWs, protective antigenic proteins (PAPs) reported in Protegen database and VaxiJen dataset, and immunogenic epitopes in immune epitope database (IEDB) were used to predict potential broad-specific vaccine candidates against BBWs. Comparison of proteomes sequences of BBWs and their analyses using in-house PERL scripts identified 44 conserved proteins and many of them were known to be immunogenic. Comparison of conserved proteins against PAPs identified six either as PAPs or their homologues with a potential of providing protection against multiple pathogens. Similarly, mapping of conserved proteins against experimentally known IEDB epitopes identified six epitopes which had exact epitope match in four proteins including three from earlier predicted six PAPs. These epitopes were also reported to provide protection against several pathogens. In the backdrop of conserved heat shock GroEL protein from Salmonella enterica providing protection against five diverse bacterial pathogens involved in different diseases, and synthetic proteins produced by combination of epitopes from Mycobacterium tuberculosis and 4 viruses providing protection against both bacterium and viruses, the identified putative immunogenic conserved proteins and immune-protective epitopes can further be explored for their potential as broad-specific vaccine candidates against BBWs.

A computational assay to design an epitope-based Peptide vaccine against saint louis encephalitis virus

Saint Louis encephalitis virus, a member of the flaviviridae subgroup, is a culex mosquito-borne pathogen. Despite severe epidemic outbreaks on several occasions, not much progress has been made with regard to an epitope-based vaccine designed for Saint Louis encephalitis virus. The envelope proteins were collected from a protein database and analyzed with an in silico tool to identify the most immunogenic protein. The protein was then verified through several parameters to predict the T-cell and B-cell epitopes. Both T-cell and B-cell immunity were assessed to determine that the protein can induce humoral as well as cell-mediated immunity. The peptide sequence from 330-336 amino acids and the sequence REYCYEATL from the position 57 were found as the most potential B-cell and T-cell epitopes, respectively. Furthermore, as an RNA virus, one important thing was to establish the epitope as a conserved one; this was also done by in silico tools, showing 63.51% conservancy. The epitope was further tested for binding against the HLA molecule by computational docking techniques to verify the binding cleft epitope interaction. However, this is a preliminary study of designing an epitope-based peptide vaccine against Saint Louis encephalitis virus; the results awaits validation by in vitro and in vivo experiments.

AllergenFP: allergenicity prediction by descriptor fingerprints

MOTIVATION

Allergenicity, like antigenicity and immunogenicity, is a property encoded linearly and non-linearly, and therefore the alignment-based approaches are not able to identify this property unambiguously. A novel alignment-free descriptor-based fingerprint approach is presented here and applied to identify allergens and non-allergens. The approach was implemented into a four step algorithm. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and β-strand forming propensities. Then, the generated strings of different length are converted into vectors with equal length by auto- and cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of Tanimoto coefficient.

RESULTS

The approach was applied to a set of 2427 known allergens and 2427 non-allergens and identified correctly 88% of them with Matthews correlation coefficient of 0.759. The descriptor fingerprint approach presented here is universal. It could be applied for any classification problem in computational biology. The set of E-descriptors is able to capture the main structural and physicochemical properties of amino acids building the proteins. The ACC transformation overcomes the main problem in the alignment-based comparative studies arising from the different length of the aligned protein sequences. The conversion of protein ACC values into binary descriptor fingerprints allows similarity search and classification.

AVAILABILITY AND IMPLEMENTATION

The algorithm described in the present study was implemented in a specially designed Web site, named AllergenFP (FP stands for FingerPrint). AllergenFP is written in Python, with GIU in HTML. It is freely accessible at http://ddg-pharmfac.net/Allergen FP.

CONTACT

idoytchinova@pharmfac.net or ivanbangov@shu-bg.net.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

Background

Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.

Results

A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset.

Conclusions

Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html

Immune response variations to Salmonella enterica serovar Typhi recombinant porin proteins in mice

OBJECTIVES

Typhoid fever is caused by Salmonella enterica serovar Typhi. OmpC, OmpF and OmpA, the three major outer membrane proteins (OMPs), could serve as vaccine candidates.

METHODS

The porins antigenicity was predicted in silico. The OMP genes were amplified, cloned and expressed. Sero-reactivities of the recombinant proteins purified by denaturing method were assayed by ELISA. BALB/c mice were immunized with the recombinant porins followed by bacterial challenge.

RESULTS

Bacterial challenge of the animal model brought about antibody triggering efficacy of the antigen in OmpF > OmpC > OmpA order. Experimental findings validated the in silico results. None of the antigens had synergic or antagonistic effects on each other from immune system induction points of view. Despite their high immunogenicity, none of the antigens was protective. However, administration of two or three antigens simultaneously resulted in retardation of lethal effect. Porins, in addition to their specific functions, share common functions. Hence, they can compensate for each other's functions.

CONCLUSIONS

The produced antibodies could not eliminate the pathogenicity by blockade of one or some of the antigens. Porin antigens are not suitable vaccine candidates alone or in denatured forms. Native forms of the antigens maybe studied for protective immunogenicity.

Functional and immunological relevance of Anaplasma marginale major surface protein 1a sequence and structural analysis

Bovine anaplasmosis is caused by cattle infection with the tick-borne bacterium, Anaplasma marginale. The major surface protein 1a (MSP1a) has been used as a genetic marker for identifying A. marginale strains based on N-terminal tandem repeats and a 5′-UTR microsatellite located in the msp1a gene. The MSP1a tandem repeats contain immune relevant elements and functional domains that bind to bovine erythrocytes and tick cells, thus providing information about the evolution of host-pathogen and vector-pathogen interactions. Here we propose one nomenclature for A. marginale strain classification based on MSP1a. All tandem repeats among A. marginale strains were classified and the amino acid variability/frequency in each position was determined. The sequence variation at immunodominant B cell epitopes was determined and the secondary (2D) structure of the tandem repeats was modeled. A total of 224 different strains of A. marginale were classified, showing 11 genotypes based on the 5′-UTR microsatellite and 193 different tandem repeats with high amino acid variability per position. Our results showed phylogenetic correlation between MSP1a sequence, secondary structure, B-cell epitope composition and tick transmissibility of A. marginale strains. The analysis of MSP1a sequences provides relevant information about the biology of A. marginale to design vaccines with a cross-protective capacity based on MSP1a B-cell epitopes.

HCV Envelope protein 2 sequence comparison of Pakistani isolate and In-silico prediction of conserved epitopes for vaccine development

BACKGROUND

HCV is causing hundreds of cases yearly in Pakistan and has become a threat for Pakistani population. HCV E2 protein is a transmembrane protein involved in viral attachment and thus can serve as an important target for vaccine development but because of its variability, vaccine development against it has become a challenge. Therefore, this study was designed to isolate the HCV E2 gene from Pakistani HCV infected patients of 3a genotype, to perform In-silico analysis of HCV E2 isolated in Pakistan and to analyze HCV E2 protein sequence in comparison with other E2 proteins belonging to 3a and 1a genotypes to find potential conserved B-cells and T-cell epitopes that can be important in designing novel inhibitory compounds and peptide vaccine against genotype 3a and 1a.

PATIENTS AND METHODS

Patients were selected on the basis of elevated serum ALT and AST levels at least for six months, histological examination, and detection of serum HCV RNA anti-HCV antibodies (3rd generation ELISA). RNA isolation, cDNA synthesis, amplification, cloning and sequencing was performed from 4 patient's serum samples in order to get the HCV E2 sequence. HCV E2 protein of Pakistani origin was analyzed using various bioinformatics tools including sequence and structure tools.

RESULTS

HCV E1 protein modeling was performed with I-TASSER online server and quality of the model was assessed with ramchandran plot and Z-score. A total of 3 B-cell and 3 T-cell epitopes were found to be highly conserved among HCV 3a and 1a genotype.

CONCLUSION

The present study revealed potential conserved B-cell and T-cell epitopes of the HCV E2 protein along with 3D protein modeling. These conserved B-cell and T-cell epitopes can be helpful in developing effective vaccines against HCV and thus limiting threats of HCV infection in Pakistan.

AllerTOP--a server for in silico prediction of allergens

Background

Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.

Results

A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z₁, z₂ and z₃) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.

Conclusions

AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.

Structural analysis and epitope prediction of HCV E1 protein isolated in Pakistan: an in-silico approach

Background

HCV infection is a major health problem causing acute and chronic hepatitis. HCV E1 protein is a transmembrane protein that is involved in viral attachment and therefore, can serve as an important target for vaccine development. Consequently, this study was designed to analyze the HCV E1 protein sequence isolated in Pakistan to find potential conserved epitopes/antigenic determinants.

Results

HCV E1 protein isolated in Pakistan was analyzed using various bio-informatics and immuno-informatics tools including sequence and structure tools. A total of four antigenic B cell epitopes, 5 MHC class I binding peptides and 5 MHC class II binding peptides were predicted. Best designed epitopes were subjected to conservation analyses with other countries.

Conclusion

The study was conducted to predict antigenic determinants/epitopes of HCV E1 protein of genotype 3a along with the 3D protein modeling. The study revealed potential B-cell and T-cell epitopes that can raise the desired immune response against HCV E1 protein isolated in Pakistan. Conservation analysis can be helpful in developing effective vaccines against HCV and thus limiting threats of HCV infection in Pakistan.

An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Background

DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence.

Results

In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively.

Conclusions

The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

Exoproteome and secretome derived broad spectrum novel drug and vaccine candidates in Vibrio cholerae targeted by Piper betel derived compounds

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species.

T-cell epitope vaccine design by immunoinformatics

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

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

Massive gene acquisitions in Mycobacterium indicus pranii provide a perspective on mycobacterial evolution

Understanding the evolutionary and genomic mechanisms responsible for turning the soil-derived saprophytic mycobacteria into lethal intracellular pathogens is a critical step towards the development of strategies for the control of mycobacterial diseases. In this context, Mycobacterium indicus pranii (MIP) is of specific interest because of its unique immunological and evolutionary significance. Evolutionarily, it is the progenitor of opportunistic pathogens belonging to M. avium complex and is endowed with features that place it between saprophytic and pathogenic species. Herein, we have sequenced the complete MIP genome to understand its unique life style, basis of immunomodulation and habitat diversification in mycobacteria. As a case of massive gene acquisitions, 50.5% of MIP open reading frames (ORFs) are laterally acquired. We show, for the first time for Mycobacterium, that MIP genome has mosaic architecture. These gene acquisitions have led to the enrichment of selected gene families critical to MIP physiology. Comparative genomic analysis indicates a higher antigenic potential of MIP imparting it a unique ability for immunomodulation. Besides, it also suggests an important role of genomic fluidity in habitat diversification within mycobacteria and provides a unique view of evolutionary divergence and putative bottlenecks that might have eventually led to intracellular survival and pathogenic attributes in mycobacteria.

Edwardsiella tarda flagellar protein FlgD: a protective immunogen against edwardsiellosis

Edwardsiella tarda is a gram-negative bacterium and a causative agent of edwardsiellosis, resulting to severe loss of the aquaculture industry. In this study, based on the reverse vaccinology, sixteen flagellar proteins were selected from highly pathogenic E. tarda EIB202 genome information and in silico analyzed as potential vaccine candidates. Among them, ten recombinant proteins were highly expressed in Escherichia coli and successfully purified. The immunoprotective potentials of these purified recombinant proteins were evaluated in zebrafish model. And recombinant FlgD and FliD were found to lead to a high relative percent survival (RPS, about 70%) against E. tarda EIB202. Furthermore, FlgD required in flagellum hook assembly brought about the similar immune protection in turbot. The immune responses of zebrafish and turbot to recombinant FlgD were also investigated, and the results indicated that its high protection was mainly involved in cellular mediated immune response, corresponding to the intracellular pathogenicity of E. tarda.

A systematic, functional genomics, and reverse vaccinology approach to the identification of vaccine candidates in the cattle tick, Rhipicephalus microplus

In the post-genomic era, reverse vaccinology is proving promising in the development of vaccines against bacterial and viral diseases, with limited application in ectoparasite vaccine design. In this study, we present a systematic approach using a combination of functional genomics (DNA microarrays) techniques and a pipeline incorporating in silico prediction of subcellular localization and protective antigenicity using VaxiJen for the identification of novel anti-tick vaccine candidates. A total of 791 candidates were identified using this approach, of which 176 are membrane-associated and 86 secreted soluble proteins. A preliminary analysis on the antigenicity of selected membrane proteins using anti-gut antisera yielded candidates with an IgG binding capacity greater than previously identified epitopes of Bm86. Subsequent vaccination trials using recombinant proteins will not only validate this approach, but will also improve subsequent reverse vaccinology approaches for the identification of novel anti-tick vaccine candidates.

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates: E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 < 50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

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

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

Improving reverse vaccinology with a machine learning approach

Reverse vaccinology aims to accelerate subunit vaccine design by rapidly predicting which proteins in a pathogenic bacterial proteome are putative protective antigens. Support vector machine classification is a machine learning approach that has been applied to solve numerous classification problems in biological sciences but has not previously been incorporated into a reverse vaccinology approach. A training data set of 136 bacterial protective antigens paired with 136 non-antigens was constructed and bioinformatic tools were used to annotate this data for predicted protein features, many of which are associated with antigenicity (i.e. extracellular localization, signal peptides and B-cell epitopes). Annotation was used to train support vector machine classifiers that exhibited a maximum accuracy of 92% for discriminating protective antigens from non-antigens as assessed by a leave-tenth-out cross-validation approach. These accuracies were superior to those achieved when annotating training data with auto and cross covariance transformations of z-descriptors for hydrophobicity, molecular size and polarity, or when classification was performed using regression methods. To further validate support vector machine classifiers, they were used to rank all the proteins in six bacterial proteomes for their antigenicity. Protective antigens from the training data were significantly recalled (enriched) in the top 75 ranked proteins for all six proteomes as assessed by a Fisher's exact test (p<0.05). This paper describes a superior workflow for performing reverse vaccinology studies and provides a benchmark training data set that can be used to evaluate future methodological improvements.

Toward an improved discrimination of outer membrane proteins using a sequence-based approach

This article offers a novel sequence-based approach to discriminate outer membrane proteins (OMPs). The first step is to use a new representation approach, factor analysis scales of generalized amino acid information (FASGAI) representing hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, etc., to characterize sequences of OMPs and non-OMPs. The subsequent data is then transformed into a uniform matrix by the auto cross covariance (ACC). The second step is to develop discrimination predictors of OMPs from non-OMPs using a support vector machine (SVM). The SVM predictors thus successfully produce a high Matthews correlation coefficient (MCC) of 0.916 on 208 OMPs from non-OMPs including 206 α-helical membrane proteins and 673 globular proteins by a fivefold cross validation test. Meanwhile, overall MCC values of 0.923 and 0.930 are obtained for the discrimination OMPs from the α-helical membrane proteins and the globular proteins, respectively. The results demonstrate that the FASGAI-ACC-SVM combination approach shows great prospect of application in the field of bioinformatics or proteomics studies.

High-throughput prediction of protein antigenicity using protein microarray data

MOTIVATION

Discovery of novel protective antigens is fundamental to the development of vaccines for existing and emerging pathogens. Most computational methods for predicting protein antigenicity rely directly on homology with previously characterized protective antigens; however, homology-based methods will fail to discover truly novel protective antigens. Thus, there is a significant need for homology-free methods capable of screening entire proteomes for the antigens most likely to generate a protective humoral immune response.

RESULTS

Here we begin by curating two types of positive data: (i) antigens that elicit a strong antibody response in protected individuals but not in unprotected individuals, using human immunoglobulin reactivity data obtained from protein microarray analyses; and (ii) known protective antigens from the literature. The resulting datasets are used to train a sequence-based prediction model, ANTIGENpro, to predict the likelihood that a protein is a protective antigen. ANTIGENpro correctly classifies 82% of the known protective antigens when trained using only the protein microarray datasets. The accuracy on the combined dataset is estimated at 76% by cross-validation experiments. Finally, ANTIGENpro performs well when evaluated on an external pathogen proteome for which protein microarray data were obtained after the initial development of ANTIGENpro.

AVAILABILITY

ANTIGENpro is integrated in the SCRATCH suite of predictors available at http://scratch.proteomics.ics.uci.edu.

CONTACT

pfbaldi@ics.uci.edu

Identification of immunogenic consensus T-cell epitopes in globally distributed influenza-A H1N1 neuraminidase

Antigenic drift is the ability of the swine influenza virus to undergo continuous and progressive changes in response to the host immune system. These changes dictate influenza vaccine updates annually to ensure inclusion of antigens of the most current strains. The identification of those peptides that stimulate T-cell responses, termed T-cell epitopes, is essential for the development of successful vaccines. In this study, the highly conserved and specific epitopes from neuraminidase of globally distributed H1N1 strains were predicted so that these potential vaccine candidates may escape with antigenic drift. A total of nine novel CD8(+) T-cell epitopes for MHC class-I and eight novel CD4(+) T-cell epitopes for MHC class-II alleles were proposed as novel epitope based vaccine candidates. Additionally, the epitope FSYKYGNGV was identified as a highly conserved, immunogenic and potential vaccine candidate, capable for generating both CD8(+) and CD4(+) responses.

Concept and application of a computational vaccinology workflow

BACKGROUND

The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.

RESULTS

We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.

CONCLUSION

Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Immunoinformatics: an integrated scenario

Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost.

Prediction and characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitidis serogroup B

Neisseria meningitidis serogroup B (MC58) is a leading cause of meningitis and septicaemia, principally infects the infants and adolescents. No vaccine is available for the prevention of these infections because the serogroup B capsular polysaccharide is unable to stimulate an immune response, due to its similarity with polysialic acid. To overcome these obstacles, we proposed to develop a peptide based epitope vaccine from outer membrane protein contained in outer membrane vesicles (OMV) based on our computational analysis. In OMV a total of 236 proteins were identified, only 15 (6.4%) of which were predicted to be located in outer membrane. The major requirement is the identification and selection of T-cell epitopes that act as a vaccine target. We have selected 13 out of 15 outer membrane proteins from OMV proteins. Due to similarity of the fkpA and omp85 with the human FKBP2 and SAMM50 protein, we removed these two sequences from the analysis as their presence in the vaccine is likely to elicit an autoimmune response. ProPred and ProPred1 were used to predict promiscuous helper T Lymphocytes (HTL) and cytotoxic T Lymphocytes (CTL) epitopes and MHCPred for their binding affinity in N. meningitidis serogroup B (MC58), respectively. Binding peptides (epitopes) are distinguished from nonbinding peptides in properties such as amino acid preference on the basis of amino acid composition. By using this dataset, we compared physico-chemical and structural properties at amino acid level through amino acid composition, computed from ProtParam server. Results indicate that porA, porB, opc, rmpM, mtrE and nspA are more suitable vaccine candidates. The predicted peptides are expected to be useful in the design of multi-epitope vaccines without compromising the human population coverage.

Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance

Machine learning methods play the very important role in protein secondary structure prediction and other related works. On condition of a certain approach, the prediction qualities mostly depend on the ways of representing protein sequences into numeric features. In this paper, two Support Vector Machine (SVM) multi-classification strategies, "one-against-one" (1-a-1) and "one-against-all" (1-a-a), were used in protein structural classes identification. Auto covariance (AC), which transforms the physicochemical properties of the amino acids of the proteins into a data matrix, focuses on the neighboring effects and the interactions between residues in protein sequences. "1-a-1" approach was used on SVM to predict protein structural classes and obtained very promising overall accuracy 90.69% by Jackknife test. It was more than 10% higher than the accuracy obtained by using "1-a-a". Experimental results led to the finding that the SVM predictor constructed by "1-a-1" can avoid the appearance of biased prediction accuracy. This current method, using the protein primary sequence information described by auto covariance (AC) and "1-a-1" approach on SVM, should play an important complementary role in other related applications.

A novel strategy of epitope design in Neisseria gonorrhoeae

In spite of genome sequences of both human and N. gonorrhoeae in hand, vaccine for gonorrhea is yet not available. Due to availability of several host and pathogen genomes and numerous tools for in silico prediction of effective B-cell and T-cell epitopes; recent trend of vaccine designing has been shifted to peptide or epitope based vaccines that are more specific, safe, and easy to produce. In order to design and develop such a peptide vaccine against the pathogen, we adopted a novel computational approache based on sequence, structure, QSAR, and simulation methods along with fold level analysis to predict potential antigenic B-cell epitope derived T-cell epitopes from four vaccine targets of N. gonorrhoeae previously identified by us [Barh and Kumar (2009) In Silico Biology 9, 1-7]. Four epitopes, one from each protein, have been designed in such a way that each epitope is highly likely to bind maximum number of HLA molecules (comprising of both the MHC-I and II) and interacts with most frequent HLA alleles (A*0201, A*0204, B*2705, DRB1*0101, and DRB1*0401) in human population. Therefore our selected epitopes are highly potential to induce both the B-cell and T-cell mediated immune responses. Of course, these selected epitopes require further experimental validation.

Discriminating antigen and non-antigen using proteome dissimilarity II: viral and fungal antigens

Immunogenicity arises via many synergistic mechanisms, yet the overall dissimilarity of pathogenic proteins versus the host proteome has been proposed as a key arbiter. We have previously explored this concept in relation to Bacterial antigens; here we extend our analysis to antigens of viral and fungal origin. Sets of known viral and fungal antigenic and non-antigenic protein sequences were compared to human and mouse proteomes. Both antigenic and non-antigenic sequences lacked human or mouse homologues. Observed distributions were compared using the non-parametric Mann-Whitney test. The statistical null hypothesis was accepted, indicating that antigen and non-antigens did not differ significantly. Likewise, we could not determine a threshold able meaningfully to separate non-antigen from antigen. We conclude that viral and fungal antigens cannot be predicted from pathogen genomes based solely on their dissimilarity to mammalian genomes.