B-cell Epitope Prediction Research Articles

In silico prediction of B-cell epitopes for twenty-five mite allergens: The therapeutic potentials for immunotherapy

Mite allergens are one of the major allergens; however, their structures and epitopes have not been thoroughly studied. In the present study, we predicted the tertiary structures of several mite allergens and also identified the B-cell epitopes, which can be suggested as potential epitopes for allergen immunotherapy. Twenty-five mite allergens, from six mite allergen groups, were investigated; homology modeling and structure refinement were performed for seventeen allergens with unknown structures. Furthermore, various servers were employed to predict linear and conformational B-cell epitopes and consensus B-cell epitopes were identified (172 linear and 64 conformational epitopes). Conservation and epitope identity were also determined among the allergens of the same group and some conserved epitopes were identified. Some regions of the predicted epitopes were identified as novel epitope regions. The predicted consensus epitopes can be applied as suitable candidates to design immunotherapeutic vaccines.

Plasmodium vivax ookinete surface protein (Pvs25) is highly conserved among field isolates from five different regions of the Brazilian Amazon

The Plasmodium vivax Ookinete Surface Protein (Pvs25) is one of the leading malaria Transmission-Blocking Vaccine candidates based on its high immunogenicity in animal models, transmission-blocking activity of antibodies elicited in clinical trials and high conservation among P. vivax isolates from endemic areas. However, the polymorphism in gene encoding Pvs25 in endemic areas from South America has been poorly studied so far. Here, we investigated the genetic polymorphism of pvs25 in P. vivax isolates from five different regions of the Brazilian Amazon (Cruzeiro do Sul, Mâncio Lima, Guajará, Manaus and Oiapoque) and its impact on antigenicity of predicted B-cell epitopes using gene sequencing and epitope prediction tools. Firstly, only a non-synonymous substitution was found in the 657 bp amplified fragment in all sequenced samples, which represented an exchange of Gln by Lys at position 87 (Q87K) of protein amino acid sequence (domain II EGF-like). Q87K substitution was also present in all studied sites with a total frequency of 37.8%. Cruzeiro do Sul presented Q87K substitution in almost half of the isolates (48.4%), and an expressive frequency (40.5%) was also found in Manaus, while in Mâncio Lima, Guajará and Oiapoque, the frequencies were low (23.5%, 25% and 22.2% respectively). We also observed the Q87K mutation in a predicted B-cell epitope of pvs25, with no significant changes on its putative antigenicity. Our data suggest that the pvs25 gene is conserved among isolates from different Brazilian Amazon geographic regions, an important observation considering the antigen potentiality as a vaccine candidate to cover distinct P. vivax endemic areas worldwide.

Preparation of multi-epitope recombinant diagnostic antigen of Mycobacterium tuberculosis

Multi-epitope recombinant diagnostic antigen (designated 'B102') of Mycobacterium tuberculosis (Mtb) was prepared and evaluated as a serological diagnostic antigen. With TRX at the N-terminal and His tag at the C-terminal, the multi-epitope Mtb recombinant diagnostic antigen including 11 predicted B-cell epitopes from 6 Mtb antigens (PstS1, ESAT6, CFP10, Ag85B, Ag85A and PPE54) was expressed in Escherichia coli BL21 (DE3) and purified by Ni²⁺-Chelating affinity and DEAE anion exchange chromatography. Based on the antigenicity of B102 confirmed in Western blotting analysis, we constructed and evaluated a double-antigen sandwich ELISA for diagnosis of Mtb infection. The protein B102 exists in the form of inclusion bodies, accounting for 31.25% of the total proteins of the bacteria. After purification and renaturation, protein B102 exists in soluble form with the concentration 3.124 mg/mL and the homogeneity 96.71%. WB analysis demonstrated that protein B102 could react with antibodies in Mtb positive serum. Using the novel antigen in ELISA, we tested 60 Mtb-related positive and negative serum; The results showed the sensitivity, specificity, positive and negative predictive values and coincidence rate of the detection method is 90.00%, 93.33%, 93.10%, 90.32% and 91.67%, respectively. The McNemer analysis suggested there was no statistical difference between the 'Gold standard' and the novel ELISA with kappa 0.833, which suggested the excellent consistency. By prokaryotic expression and chromatography purification, the multi-epitope recombinant antigen B102 was obtained with excellent antigenicity, which could be applied for Mtb-related serological detection.

Bioinformatics Analysis of Domain 1 of HCV-Core Protein: Iran.

Hepatitis C virus (HCV) infection is a serious global health problem and a cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (HCC). Bioinformatics software has been an effective tool to study the HCV genome as well as core domains. Our research was based on employing several bioinformatics software applications to find important mutations in domain 1 of core protein in Iranian HCV infected samples from 2006 to 2017, and an investigation of general properties, B-cell and T-cell epitopes, modification sites, and structure of domain 1. Domain 1 sequences of 188 HCV samples isolated from 2006 to 2017, Iran, were retrieved from NCBI gene bank. Using several tools, all sequences were analyzed for determination of mutations, physicochemical analysis, B-cell epitopes prediction, T-cell and CTL epitopes prediction, post modification, secondary and tertiary structure prediction. Our analysis determined several mutations in some special positions (70, 90, 91, and 110) that are associated with HCC and hepatocarcinogenesis, efficacy of triple therapy and sustained virological response, and interaction between core and CCR6. Several B-cell, T-cell, and CTL epitopes were recognized. Secondary and tertiary structures were mapped fordomain1 and core proteins. Our study, as a first report, offered inclusive data about frequent mutation in HCV-core gene domain 1 in Iranian sequences that can provide helpful analysis on structure and function of domain 1 of the core gene.

Analysis of Apical Membrane Antigen (AMA)-1 characteristics using bioinformatics tools in order to vaccine design against Plasmodium vivax

Plasmodium vivax, an intracellular protozoan, causes malaria which is characterized by fever, anemia, respiratory distress, liver and spleen enlargement. In spite of attempts to design an efficient vaccine, there is not a vaccine against P. vivax. Notable advances have recently achieved in the development of malaria vaccines targeting the surface antigens such as Apical Membrane Antigens (AMA)-1. AMA-1 is a micronemal protein synthesized during the erythrocyte-stage of Plasmodium species and plays a significant role in the invasion process of the parasite into host cells. P. vivax AMA-1 (PvAMA-1) can induce strong cellular and humoral responses, indicating that it can be an ideal candidate of vaccine against malaria. Identification and prediction of proteins characteristics increase our knowledge about them and leads to develop vaccine and diagnostic studies. In the present study several valid bioinformatics tools were applied to analyze the various characteristics of AMA-1 such as physical and chemical properties, secondary and tertiary structures, B- cell and T-cell prediction and other important features in order to introduce potential epitopes for designing a high-efficient vaccine. The results demonstrated that this protein had 57 potential PTM sites and only one transmembrane domain on its sequence. Also, multiple hydrophilic regions and classical high hydrophilic domains were predicted. Secondary structure prediction revealed that the proportions of random coil, alpha-helix and extended strand in the AMA-1 sequence were 53.74%, 27.22%, and 19.4%, respectively. Moreover, 5 disulfide bonds were predicted at positions 14–21aa, 162–192aa, 208–220aa, 247–265aa and 354–363aa. The data obtained from B-cell and T-cell epitopes prediction showed that there were several potential epitopes on AMA-1 that can be proper targets for diagnostic and vaccine studies. The current study presented interesting basic and theoretical information regarding PvAMA-1, being important for further studies in order to design a high-efficiency vaccine against malaria.

Advances in In-silico B-cell Epitope Prediction.

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

Towards the first multi-epitope recombinant vaccine against Crimean-Congo hemorrhagic fever virus: A computer-aided vaccine design approach.

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. For this purpose, linear B-cell and T-cell binding epitopes from two structural glycoproteins of CCHF virus including Gc and Gn were predicted. The epitopes were further studied regarding their antigenicity, allergenicity, hydrophobicity, stability, toxicity and population coverage. A total number of seven epitopes including five T-cell and two B-cell epitopes were screened for the final vaccine construct. Final vaccine construct composed of 382 amino acid residues which were organized in four domains including linear B-cell, T-cell epitopes and cholera toxin B-subunit (CTxB) along with heat labile enterotoxin IIc B subunit (LT-IIc) as adjuvants. All the segments were joined using appropriate linkers. The physicochemical properties as well as the presence of IFN-γ inducing epitopes in the proposed vaccine, was also checked to determining the vaccine stability, solubility and its ability to induce cell-mediated immune responses. The 3D structure of proposed vaccine was subjected to the prediction of computational B-cell epitopes and molecular docking studies with MHC-I and II molecules. Furthermore, molecular dynamics stimulations were performed to study the vaccine-MHCs complexes stability during stimulation time. The results suggest that our proposed vaccine was stable, well soluble in water and potentially antigenic. Results also demonstrated that the vaccine can induce both humoral and cell-mediated immune responses and could serve as a promising anti-CCHF vaccine candidate.

In Silico Analysis Brucella OMPs and CagA for Expansion of a Subunit Vaccine Candidate Versus Brucellosis

Brucellosis is one of the current zoonotic diseases, still a health hazard in many countries, and progressive research programs are necessary to control and eradicate this disease in endemic areas. Outer membrane proteins (OMPs) are capable of allocating immunity and protection antigen in mice. CagA is an immunogenic protein of Helicobacter pylori. Because OMPs are part of Brucella spp, they have been conferred as potential immunogenic and protective antigens. In this study, the gene sequence which encoding TN-OMPs was obtained from GenBank. To estimate the 3D structure of the protein, modeling, prediction of secondary and tertiary structure, immunogenicity, allergenic sites and antigenic B-cell and T-cell epitopes were performed. The conserved domain of proteins was obtained and the epitopes of TN-OMPs were capable of inducing both B-cell and T-cell mediated immune responses. CagA is a cellular immunogenic protein that induces a Th1 response. The OMPs of Brucella could be used as a suitable vaccine candidate versus brucellosis.

The Computational Prediction Methods for Linear B-cell Epitopes

Background: B-cell epitope prediction is an essential tool for a variety of immunological studies. For identifying such epitopes, several computational predictors have been proposed in the past 10 years. Objective: In this review, we summarized the representative computational approaches developed for the identification of linear B-cell epitopes. </P><P> Methods: We mainly discuss the datasets, feature extraction methods and classification methods used in the previous work. Results: The performance of the existing methods was not very satisfying, and so more effective approaches should be proposed by considering the structural information of proteins. Conclusion: We consider existing challenges and future perspectives for developing reliable methods for predicting linear B-cell epitopes.

Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes.

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.

In Silico screening of T-cell and B-cell Epitopes of Rotavirus VP7 and VP4 proteins for Effective Vaccine Design

Rotavirus is one of the deadliest causative agents of childhood diarrhea which causes half a million child death across the globe, mostly in developing countries. However, effective vaccine strategies against rotavirus are yet to be established to prevent these unwanted premature deaths. In this regard, in silico vaccine design for rotavirus could be a promising alternative for developing countries due to its efficiency in shortening valuable time and cost. The present study described an epitope-based peptide vaccine design against rotavirus, using a combination of T-cell and B-cell epitope predictions and molecular docking approach. To perform this, sequences of rotavirus VP7 and VP4 proteins were retrieved from the NCBI database and subjected to different bioinformatics tools to predict most immunogenic T-cell and B-cell epitopes. From the identified epitopes, the sequence VMSKRSRSL of VP7 and TQFTDFVSL of VP4 was identified as the most potential epitopes based on their antigenicity, conservancy and interaction with major histocompatability complex I (MHC-I) alleles. Moreover, the peptide VMSKRSRSL interacted with human leukocyte antigen, HLA-B*08:01 and TQFTDFVSL interacted with HLA-A*02:06 with considerable binding energy and affinity score. Combined population coverage for our identified epitopes was found 70.53% and 45.64% for world population and South Asian population respectively. All these results suggest that, the epitopes identified in this study could be a very good vaccine candidate for the strains of rotavirus circulating in Bangladesh. However, as this study is completely dependent on computational prediction algorithms, further in vivo screening is required to come up in a precise conclusion about these epitopes for effective rotavirus vaccination.
 Bangladesh J Microbiol, Volume 35 Number 1 June 2018, pp 45-55

Bioinformatics Approaches for Analysing the Spatial Structure and B-cell Epitope of Eg14-3-3 Protein

Echinococcus granulosus (E. granulosus) is a small parasitic flat worm that would cause a zoonosis named Cystic echinococcosis (CE), which leads to a serious healthy and economic damage all over the world. To prevent the spread of the disease, the vaccine against E. granulosus has become one of the most effective strategies nowadays. According to the previous research, Eg14-3-3 protein is an important vaccine candidate antigen against E. granulosus. Therefore, serval bioinformatics approaches were used to analyze the spatial structure and linear B-cell epitopes of Eg14-3-3 protein. The composition of secondary structural is 68.42% alpha helix, 7.69% extended strand, 0.81% beta turn and 23.08% random coil. The Phyre2 server was selected to construct the tertiary structure of Eg14-3-3. The BCPREDS Server 1.0 and ABCpred Prediction Server were chosen to predict the linear B-cell epitope. Comparing the result of two software, there are 5 dominate epitopes occurred: 66-71, 76-81, 121-127, 131-144, 227-232. Through the spatial structure and B-cell epitopes prediction, this study provides theoretical basis for the future vaccine against E. granulosus.

In Silico Homology Modeling and Epitope Prediction of Drug Target Protein in Human Herpes Virus 8 (HHV8)

Kaposi's sarcoma (KS) is a tumor, caused by Human herpes virus 8 (HHV8), also known as Kaposi's sarcoma-associated herpes virus (KSHV). The disease usually causes tumors in the tissues below the skin or face or in the mucous membranes of the nose, mouth or anus. In the present study we have performed in silico drug targets and epitope predictions of HHV8, which is a crucial step in designing of peptide vaccines. The dataset used in this study consists of 86 proteins of HHV8, out of these; 45proteins with unknown structures were selected. Comparative genome analysis of selected proteins between human and HHV8 was done using BLASTP. Out of 45 proteins, 44 proteins were found to be dissimilar and considered as drug target. Based on functional characterization, one membrane bound protein was selected for homology modeling and B-cell epitope prediction. Homology modeling was done using CPHmodels-3.0 Server and B-cell epitopes were obtained from BcePred Prediction Server. The accuracy of prediction based on propensities greater than 1.9. Swiss Pdb viewer was used to visualize the 3D structure of B-cell epitopes.

A Recurrent Neural Network Linear B-Epitope Predictor: BIRUNI

Experimental methods used for characterizing epitopes that play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research are time consuming and need huge resources. There are many online epitope prediction tools are available that can help scientists in short listing the candidate peptides. To predict B-cell epitopes in an antigenic sequence, Jordan recurrent neural network (BIRUNI) is found to besuccessful. To train and test neural networks, 262.583 B epitopes are retrieved from IEDB database. 99.9% of these epitopes have lengths in the interval 6-25 amino acids. For each of these lengths, committees of 11 expert recurrent neural networks are trained. To train these experts alongside epitopes, non-epitopes are needed. Non-epitopes are created as random sequences of amino acids of the same length followed by a filtering process. To distinguish epitopes and non-epitopes, the votes of eleven experts are aggregated by majority vote. An overall accuracy of 97.23% is achieved. Then these experts are used to predict the Linear Bepitopes of five antigens, Plasmodium Falciparum, Human Polio Virus Sabin Strain, Meningitis, Plasmodium Vivax and Mycobacterium Tuberculosis. The success of BIRUNU is compared with the five online prediction tools ABCPRED, BCPRED, K&T, BEPIPRED, and AAP.It is seen that BIRUNI outperforms all of them in the average.

Epitope-based immunoinformatics approach on RNA-dependent RNA polymerase (RdRp) protein complex of Nipah virus (NiV).

Persistent outbreaks of Nipah virus (NiV) with severe case fatality throw a major challenge on researchers to develop a drug or vaccine to combat the disease. With little knowledge of its molecular mechanisms, we utilized the proteome data of NiV to evaluate the potency of three major proteins (phosphoprotein, polymerase, and nucleocapsid protein) in the RNA-dependent RNA polymerase complex to count as a possible candidate for epitope-based vaccine design. Profound computational analysis was used on the above proteins individually to explore the T-cell immune properties like antigenicity, immunogenicity, binding to major histocompatibility complex class I and class II alleles, conservancy, toxicity, and population coverage. Based on these predictions the peptide 'ELRSELIGY' of phosphoprotein and 'YPLLWSFAM' of nulceocapsid protein were identified as the best-predicted T-cell epitopes and molecular docking with human leukocyte antigen-C (HLA-C*12:03) molecule was effectuated followed by validation with molecular dynamics simulation. The B-cell epitope predictions suggest that the sequence positions 421 to 471 in phosphoprotein, 606 to 640 in polymerase and 496 to 517 in nucleocapsid protein are the best-predicted regions for B-cell immune response. However, the further experimental circumstance is required to test and validate the efficacy of the subunit peptide for potential candidacy against NiV.

Babesia bovis RON2 contains conserved B-cell epitopes that induce an invasion-blocking humoral immune response in immunized cattle

BackgroundBabesia bovis belongs to the phylum Apicomplexa and is the major causal agent of bovine babesiosis, the most important veterinary disease transmitted by arthropods. In apicomplexan parasites, the interaction between AMA1 and RON2 is necessary for the invasion process, and it is a target for vaccine development. In B. bovis, the existence of AMA1 has already been reported; however, the presence of a homolog of RON2 is unknown. The aim of this study was to characterize RON2 in B. bovis.ResultsThe B. bovis ron2 gene has a similar synteny with the orthologous gene in the B. bigemina genome. The entire ron2 gene was sequenced from different B. bovis strains showing > 99% similarity at the amino acid and nucleotide level among all the sequences obtained, including the characteristic CLAG domain for cytoadherence in the amino acid sequence, as is described in other Apicomplexa. The in silico transcription analysis showed similar levels of transcription between attenuated and virulent B. bovis strains, and expression of RON2 was confirmed by western blot in the B. bovis T3Bo virulent strain. Four conserved peptides, containing predicted B-cell epitopes in hydrophilic regions of the protein, were designed and chemically synthesized. The humoral immune response generated by the synthetic peptides was characterized in bovines, showing that anti-RON2 antibodies against peptides recognized intraerythrocytic merozoites of B. bovis. Only peptides P2 and P3 generated partially neutralizing antibodies that had an inhibitory effect of 28.10% and 21.42%, respectively, on the invasion process of B. bovis in bovine erythrocytes. Consistently, this effect is additive since inhibition increased to 42.09% when the antibodies were evaluated together. Finally, P2 and P3 peptides were also recognized by 83.33% and 87.77%, respectively, of naturally infected cattle from endemic areas.ConclusionsThe data support RON2 as a novel B. bovis vaccine candidate antigen that contains conserved B-cell epitopes that elicit partially neutralizing antibodies.

Analysis and prediction of B-cell epitopes on ricin toxin A-Chain

Two different computational methodologies used to determine the linear B cell epitopes necessary to development a ricin vaccine First identify the epitopes that were predicted using nine different programs and the next step was obtained the B cell epitopes of ricin that were obtained from a database with experimental data IEDB With these two results it was determined that a consensus of linear B cell epitopes it could be the best possible candidates for the design of a ricin vaccine

Production, characterization, and epitope mapping of a monoclonal antibody against genotype VII Newcastle disease virus V protein

Newcastle disease virus (NDV) V protein is crucial for viral interferon (IFN) antagonism and virulence, determining its host range restriction. However, little information is available on the B cell epitopes of V protein and the subcellular movement of V protein in the process of NDV infection. In this study, the monoclonal antibody (mAb) clone 3D7 against genotype VII NDV V protein was generated by immunizing mice with a purified recombinant His-tagged carboxyl-terminal domain (CTD) region of V protein. Fine epitope mapping analysis and B-cell epitope prediction indicated that mAb 3D7 recognized a linear epitope 152RGPAELWK159, which is located in the V protein CTD region. Sequence alignment showed that the mAb clone 3D7-recognized epitope is highly conserved among Class II genotype VII NDV strains, but not among other genotypes, suggesting it could serve as a genetic marker to differentiate NDV genotypes. Furthermore, the movement of V protein during NDV replication in infected cells were determined by using this mAb. It was found that V protein localized around the nucleus during virus replication. The establishment of V protein-specific mAb and identification of its epitope extend our understanding of the antigenic characteristics of V protein and provide a basis for the development of epitope-based diagnostic assays.

Chikungunya virus: genomic microevolution in Eastern India and its in-silico epitope prediction.

This is the first study reporting whole genome sequences of two CHIKV strains (KJ679577 and KJ679578) isolated from Eastern Indian patients sera during 2010-2011 outbreak, both of which were of ECSA genotype, but from different subgroups: Indian Ocean outbreak and ECSA subtypes. Furthermore, viral sequences were analyzed using different in-silico approaches to identify potential genetic variations that might have functional implications on various aspects of virus replication, viral protein functionality, immunogenicity and transmission. Epitope prediction analysis revealed 70.9% increase in number of MHC Class-II interacting epitopes of KJ679578 and 25-28% increase in Class-I interacting epitopes of KJ679577 and KJ679578 compared to that of EF027141 (CHIKV of Asian genotype circulating in India during 1973, after which CHIKV infection disappeared from India for three decades). CHIKV peptides DLAKLAFKRSSKYDLECAQIPVHMKSDA and KVVLCGDPKQCGFFNMMQMKYNYNHNI were predicted to interact with maximum number of HLA Class-I (68 and 76.5%, respectively) and Class-II (47 and 100%, respectively) alleles present within Indian population with allele frequency of > 0.1 and were also recognized as predicted B-cell epitopes with BCPred score between 0.766 and 0.961 and with antigenicity ranging from 0.52 to 1.69; thus these peptides might be used to induce T- and B-cell-mediated immunity against CHIKV. Thus, the present study might help to bridge the gap between virus microevolution and its implication in host immunity by taking into account viral genetic and conformational changes. Predicted epitopes might be used as promising targets for peptide-based vaccine development and rapid diagnostics against CHIKV infection.

Validation Tools for Predicted Linear B-Epitopes: Surface Accessibility

Identifying B-cell epitopes plays an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. In this article the possibility of usage of accessible surface scores of peptides as a validation tool is studied. Janin et al. determined empirical amino acid accessible surface probabilities of twenty amino acids. With these fractional surface probabilities for amino acids, a surface probability (S) at sequence position n can be found using a formula given by Emini et. al. When a peptide is uploaded to the Emini Surface Accessibility Prediction in iedb Analysis Resource, prediction tool separates residues into two groups buried, and surface according to the average of S_ns. To create a criterion to decide whether a given peptide is a linear b-epitope or not, for 344,121 b-epitopes downloaded from iedb database, average buried and exposed probabilities, as well as the ratio ρ of averages for these b-epitopes are computed. The same is done for 111,306 artificially created non epitopes. It is seen that for b-epitopes, the ratio ρ is significantly larger than the ratio ρ for the non epitopes. Therefore if ρ is larger, the peptide is more likely is a b-epitope, and this property can be used as to rank peptides while choosing the most probable linear b-epitope from a long list.