Advances of bioinformatics tools applied in virus epitopes prediction

Identification and evaluation in-vitro of conserved peptides with high affinity to MHC-I as potential protective epitopes for Newcastle disease virus vaccines

BACKGROUND

Newcastle disease (ND) is a major threat to the poultry industry, leading to significant economic losses. The current ND vaccines, usually based on active or attenuated strains, are only partially effective and can cause adverse effects post-vaccination. Therefore, the development of safer and more efficient vaccines is necessary. Epitopes represent the antigenic portion of the pathogen and their identification and use for immunization could lead to safer and more effective vaccines. However, the prediction of protective epitopes for a pathogen is a major challenge, especially taking into account the immune system of the target species.

RESULTS

In this study, we utilized an artificial intelligence algorithm to predict ND virus (NDV) peptides that exhibit high affinity to the chicken MHC-I complex. We selected the peptides that are conserved across different NDV genotypes and absent in the chicken proteome. From the filtered peptides, we synthesized the five peptides with the highest affinities for the L, HN, and F proteins of NDV. We evaluated these peptides in-vitro for their ability to elicit cell-mediated immunity, which was measured by the lymphocyte proliferation in spleen cells of chickens previously immunized with NDV.

CONCLUSIONS

Our study identified five peptides with high affinity to MHC-I that have the potential to serve as protective epitopes and could be utilized for the development of multi-epitope NDV vaccines. This approach can provide a safer and more efficient method for NDV immunization.

A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases

Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.

Immunoinformatics and Pepscan strategies on the path of a peptide-based serological diagnosis of COVID19

Several diagnostic tools have been developed for clinical and epidemiological assays. RT-PCR and antigen detection tests are more useful for diagnosis of acute disease, while antibody tests allow the estimation of exposure in the population. Currently, there is an urgent need for the development of diagnostic tests for COVID-19 that can be used for large-scale epidemiological sampling. Through a comprehensive strategy, potential 16 mer antigenic peptides suited for antibody-based SARS-CoV-2 diagnosis were identified. A systematic scan of the three structural proteins (S,N and M) and the non-structural proteins (ORFs) present in the SARS-CoV-2 virus was conducted through the combination of immunoinformatic methods, peptide SPOT synthesis and an immunoassay with cellulose-bound peptides (Pepscan). The Pepscan filter paper sheets with synthetic peptides were tested against pools of sera of COVID-19 patients. Antibody recognition showed a strong signal for peptides corresponding to the S, N and M proteins of SARS-CoV-2 virus, but not for the ORFs proteins. The peptides exhibiting higher signal intensity were found in the C-terminal region of the N protein. Several peptides of this region showed strong recognition with all three immunoglobulins in the pools of sera. The differential reactivity observed between the different immunoglobulin isotypes (IgA, IgM and IgG) within different regions of the S and N proteins, can be advantageous for ensuring accurate diagnosis of all infected patients, with different times of exposure to infection. Few peptides of the M protein showed antibody recognition and no recognition was observed for peptides of the ORFs proteins.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations.Communicated by Ramaswamy H. Sarma.

Efficacy of co-immunization with the DNA and peptide vaccines containing SYCP1 and ACRBP epitopes in a murine triple-negative breast cancer model

Multiepitope cancer vaccines have gained lots of attention for prophylactic and therapeutic purposes in cancer patients. In our previous study, multiepitope DNA and peptide cancer vaccines consisted of the most immunodominant epitopes of ACRBP and SYCP1 antigens were designed by bioinformatic tools. In this study, the effect of prophylactic co-immunization with these DNA and peptide cancer vaccines in the 4T1 breast cancer animal model was assessed. Serum levels of the peptide-specific IgG total, IgG2a and IgG1 were measured by enzyme-linked immunosorbent assay (ELISA). Also, the efficacy of the immunized mice splenocytes' for producing interleukin-4 (IL-4) and interferon-γ (IFN-γ) was evaluated. The co-immunization caused a significant (P < .05) increase in the serum levels of IgG1 and IgG2a. The co-immunized mice splenocytes exhibited significantly enhanced IL-4 (6.6-fold) and IFN-γ (19-fold) production. Also, their lymphocytes exhibited higher proliferation rate (3-fold) and granzyme B production (6.5-fold) in comparison with the control. The prophylactic co-immunization significantly decreased the breast tumors' volume (78%) and increased the tumor-bearing mice survival time (37.5%) in comparison with the control. Taking together, prophylactic co-immunization with these multiepitope DNA and peptide cancer vaccines can activate the immune system against breast cancer. However, further experiments are needed to evaluate their efficacy from different angles.

In silico analysis of transmembrane protein 31 (TMEM31) antigen to design novel multiepitope peptide and DNA cancer vaccines against melanoma

Multiepitope cancer vaccines are announcing themselves as the future of melanoma treatment. Herein, high immunogenic regions of transmembrane protein 31 (TMEM31) antigen were selected according to cytotoxic T lymphocytes' (CTL) epitopes and major histocompatibility complex (MHC) binding affinity through in silico analyses. The 32-62, 77-105, and 125-165 residues of the TMEM31 were selected as the immunodominant fragments. They were linked together by RVRR and HEYGAEALERAG motifs to improve epitopes separation and presentation. In addition, to activate helper T lymphocytes (HTL), Pan HLA DR-binding epitope (PADRE) peptide sequence and tetanus toxin fragment C (TTFrC) were incorporated into the final construct. Also, the Beta-defensin conserved domain was utilized in the final construct as a novel adjuvant for Toll-like receptor 4/myeloid differentiation factor (TLR4-MD) activation. The CTL epitopes, cleavage sites, post-translational modifications, TAP transport efficiency, and B cells epitopes were predicted for the peptide vaccine. The final construct contained multiple CTL and B cell epitopes. In addition, it showed 93.55% and 99.13% population coverage in the world for HLA I and HLA II, respectively. According to these preliminary results, the multiepitope cancer vaccine can be an appropriate choice for further experimental investigations.

Determination of immunodominant scaffolds of Com1 and OmpH antigens of Coxiella burnetii

Today, there is an increasing emphasis on recombinant vaccines to eliminate the side effects of conventional vaccines such as whole-cell bacteria. Query fever is an emerging disease that causes irreparable complications for both humans and domestic animals. The cause of this disease is Coxiella burnetii, a gram-negative intracellular bacteria. In order to determine the most immunodominant epitopes of Com1 and OmpH antigens of C. burnetii, the most reliable bioinformatics tools with high rates of citation in predicting B cell and T cell epitopes were used. Finally, by comparing the results of all servers, the best overlapped epitopes with the highest antigenicity among different servers were selected. In this regard, epitopes in 18-27and 67-82 amino acids residues were introduced for MHCI and MHCII of T cell, respectively, whereas epitope in 16-25 amino acids residues was introduced for B cell of OmpH antigen. The epitopes in the range of 193-202, 100-108 and 215-223 amino acid residues were preferred for MHCI class of T cell, MHCII class of T cell and B cell of Com1 antigen, respectively. For each antigen, some empirical common epitopic regions were introduced, which included both T and B cells epitopes, 53-65 and 102-111 amino acid residues of OmpH antigen as well as 38-54 range of the amino acid of Com1 antigen. All the predicted epitopes were selected based on their high antigenicity scores and number of non-digestive enzymes. To optimize the application of reported epitopes, various orders of epitopes were arranged in three categories of B cell, T cell and common T and B cells epitopes for each antigen. Then, the best immunodominant scaffolds for each antigen were proposed in these categories. The results demonstrated that the scaffold arranged based on B cell epitopes had the highest antigenicity in both antigens.

Computational Design of Epitope-Enriched HIV-1 Gag Antigens with Preserved Structure and Function for Induction of Broad CD8+ T Cell Responses

The partially protective phenotype observed in HIV-infected long-term-non-progressors is often associated with certain HLA alleles, thus indicating that cytotoxic T lymphocyte (CTL) responses play a crucial role in combating virus replication. However, both the vast variability of HIV and the HLA diversity impose a challenge on elicitation of broad and effective CTL responses. Therefore, we conceived an algorithm for the enrichment of CD8⁺ T cell epitopes in HIV’s Gag protein, respecting functional preservation to enable cross-presentation. Experimentally identified epitopes were compared to a Gag reference sequence. Amino-acid-substitutions (AAS) were assessed for their impact on Gag’s budding-function using a trained classifier that considers structural models and sequence conservation. Experimental assessment of Gag-variants harboring selected AAS demonstrated an apparent classifier-precision of 100%. Compatible epitopes were assigned an immunological score that incorporates features such as conservation or HLA-association in a user-defined weighted manner. Using a genetic algorithm, the epitopes were incorporated in an iterative manner into novel T-cell-epitope-enriched Gag sequences (TeeGag). Computational evaluation showed that these antigen candidates harbor a higher fraction of epitopes with higher score as compared to natural Gag isolates and other artificial antigen designs. Thus, these designer sequences qualify as next-generation antigen candidates for induction of broader CTL responses.

Identification and evaluation of the novel immunodominant antigen Rv2351c from Mycobacterium tuberculosis

There is an urgent need for new immunodominant antigens to improve the diagnosis of tuberculosis (TB) and the efficacy of the TB vaccine to control the disease worldwide. In this study, we evaluated the diagnostic potential of a novel Mycobacterium tuberculosis (MTB)-specific antigen, Rv2351c, from region of difference (RD) 7 of the MTB genome, and investigated the potency of the vaccine by identifying its immunological function in human and animal immunological experiments. Twenty T-cell epitopes were identified using TEpredict and prediction tools from the Immune Epitope Database and Analysis Resource. A total of 159 subjects, including 61 patients with pulmonary TB, 38 patients with no TB and 55 healthy donors, were recruited and analyzed with an enzyme-linked immunospot (ELISpot) assay. The ELISpot assay using Rv2351c to detect TB infection, as compared with bacteriological tests as the gold standard, had a sensitivity and specificity of 61.4% (35/57) and 91.4% (85/93), respectively. The ELISpot assay using Rv2351c had a good conformance (κ=0.554) as compared with the bacteriological test. Rv2351c also elicited a potent cellular immune response with a high expression of cytokines (IFN-γ (4978±596.7 μg/mL) and IL-4 (68.3±15.5 μg/mL)) and a potent humoral immune response with a high concentration of IgG (1:2.2 × 10⁶), IgG1 (1:4.5 × 10⁵) and IgG2a (1:1.6 × 10⁶) in immunized BALB/c mice. In addition, the ratio of IgG2a/IgG1 indicated that Rv2351c induced cellular immunity in the mice. The results of this study indicated that Rv2351c is an antigen with good immunogenicity that may potentially be used to develop diagnostic techniques and new TB vaccines.

Design and Construction of Chimeric VP8-S2 Antigen for Bovine Rotavirus and Bovine Coronavirus

PURPOSE

Bovine Rotavirus and Bovine Coronavirus are the most important causes of diarrhea in newborn calves and in some other species such as pigs and sheep. Rotavirus VP8 subunit is the major determinant of the viral infectivity and neutralization. Spike glycoprotein of coronavirus is responsible for induction of neutralizing antibody response.

METHODS

In the present study, several prediction programs were used to predict B and T-cells epitopes, secondary and tertiary structures, antigenicity ability and enzymatic degradation sites. Finally, a chimeric antigen was designed using computational techniques. The chimeric VP8-S2 antigen was constructed. It was cloned and sub-cloned into pGH and pET32a(+) expression vector. The recombinant pET32a(+)-VP8-S2 vector was transferred into E.oli BL21CodonPlus (DE3) as expression host. The recombinant VP8-S2 protein was purified by Ni-NTA chromatography column.

RESULTS

The results of colony PCR, enzyme digestion and sequencing showed that the VP8-S2 chimeric antigen has been successfully cloned and sub-cloned into pGH and pET32a(+).The results showed that E.coli was able to express VP8-S2 protein appropriately. This protein was expressed by induction of IPTG at concentration of 1mM and it was confirmed by Ni-NTA column, dot-blotting analysis and SDS-PAGE electrophoresis.

CONCLUSION

The results of this study showed that E.coli can be used as an appropriate host to produce the recombinant VP8-S2 protein. This recombinant protein may be suitable to investigate to produce immunoglobulin, recombinant vaccine and diagnostic kit in future studies after it passes biological activity tests in vivo in animal model and or other suitable procedure.

Design and Antigenic Epitopes Prediction of a New Trial Recombinant Multiepitopic Rotaviral Vaccine: In Silico Analyses

Rotavirus is the major etiologic factor of severe diarrheal disease. Natural infection provides protection against subsequent rotavirus infection and diarrhea. This research presents a new vaccine designed based on computational models. In this study, three types of epitopes are considered-linear, conformational, and combinational-in a proposed model protein. Several studies on rotavirus vaccines have shown that VP6 and VP4 proteins are good candidates for vaccine production. In the present study, a fusion protein was designed as a new generation of rotavirus vaccines by bioinformatics analyses. This model-based study using ABCpred, BCPREDS, Bcepred, and Ellipro web servers showed that the peptide presented in this article has the necessary properties to act as a vaccine. Prediction of linear B-cell epitopes of peptides is helpful to investigate whether these peptides are able to activate humoral immunity.

Cloning, molecular analysis and epitopics prediction of a new chaperone GroEL Brucella melitensis antigen

OBJECTIVES

Brucellosis is a well-known domestic animal infectious disease, which is caused by Brucella bacterium. GroEL antigen increases Brucella survival and is one of the major antigens that stimulates the immune system. Hence, the objective of the present study was cloning and bioinformatics analysis of GroEL gene.

MATERIALS AND METHODS

The full-length open reading frame of this gene was amplified by specific primers and cloned into pTZ57R/T vector. Also, the sequence of this gene in the Brucella melitensis strain Rev 1 was submitted to the NCBI gene bank for the first time. Several prediction software applications were also used to predict B and T-cell epitopes, secondary and tertiary structures, antigenicity ability and enzymatic degradation sites. The used software applications validated experimental results.

RESULTS

The results of phylogenetic analysis showed that the GroEL sequence had near homology with other species instead of other Brucella spp. The bioinformatics tools used in the current study were validated by the results of four different experimental epitope predictions. Bioinformatics analysis identified eight B and seven T-cell epitopes.

CONCLUSION

According to the antigenic ability and proteasomal cleavage sites, four (150-160, 270-285,351-361 and 385-395) common epitopes were predicted for GroEL gene. Bioinformatics analysis showed that these regions had proper epitope characterization and so may be useful for stimulation of cell-mediated and humoral immunity system.

B-cell epitopes of antigenic proteins in Leishmania infantum: an in silico analysis

Serodiagnosis of visceral leishmaniasis is often hindered by cross-reactions to other parasitic diseases. Identifying specific B-cell epitopes in proteins is therefore important for immunodiagnostics, as well as for disease control by vaccines. This study aimed to identify linear and conformational B-cell epitopes and to evaluate the secondary structure of antigen proteins in Leishmania infantum using in silico analysis. Linear epitopes were predicted using the Immune Epitope Database and Analysis Resource (IEDB), BepiPred and BcePred programs. The conformational B-cell epitopes were identified using the CBTOPE server. The combination of the predictions using IEDB, BepiPred and BcePred generated 148 linear epitopes from the calpain-like cysteine peptidase (CP), thiol-dependent reductase 1 (TDR1) and HSP70 proteins. In total, 164 conformational epitopes were predicted, mostly located in the linear epitope region. The predicted epitopes are located in α helix and random coil regions in the thiol-dependent reductase 1 and HSP70 proteins. New linear and conformational B-cell epitopes of L. infantum proteins were identified in silico, and the prediction using various programs ensures greater accuracy of the results, as suggested by confirmation of previously identified HSP70 epitopes.

Predicting alloreactivity in transplantation

Human leukocyte Antigen (HLA) mismatching leads to severe complications after solid-organ transplantation and hematopoietic stem-cell transplantation. The alloreactive responses underlying the posttransplantation complications include both direct recognition of allogeneic HLA by HLA-specific alloantibodies and T cells and indirect T-cell recognition. However, the immunogenicity of HLA mismatches is highly variable; some HLA mismatches lead to severe clinical B-cell- and T-cell-mediated alloreactivity, whereas others are well tolerated. Definition of the permissibility of HLA mismatches prior to transplantation allows selection of donor-recipient combinations that will have a reduced chance to develop deleterious host-versus-graft responses after solid-organ transplantation and graft-versus-host responses after hematopoietic stem-cell transplantation. Therefore, several methods have been developed to predict permissible HLA-mismatch combinations. In this review we aim to give a comprehensive overview about the current knowledge regarding HLA-directed alloreactivity and several developed in vitro and in silico tools that aim to predict direct and indirect alloreactivity.

An immunodominant HLA-A*1101-restricted CD8+ T-cell response targeting hepatitis B surface antigen in chronic hepatitis B patients

Hepatitis B virus (HBV) infection is a worldwide public health problem. HBV-specific CD8(+) CTLs are vital for viral clearance. Identification of immunodominant CTL epitopes from HBV-associated antigens is necessary for therapeutic vaccine development. We showed that the HLA-A*1101 allele is one of the most common alleles in both healthy individuals and chronic hepatitis B (CHB) patients in the Chongqing area, China. However, less than 10% of epitopes of HBV-associated antigens have been identified in an HLA-A*1101 context. Here, we describe an immunodominant CD8(+) T-cell response targeting a hepatitis B surface antigen determinant (HBs(295-304)) restricted by HLA-A*1101 in both healthy individuals and CHB patients. Moreover, HBs(295-304) is more immunogenic for CTL induction than a known naturally HLA-A*1101-processed epitope from hepatitis B core antigen (HBc(88-96)). Therefore, the newly identified epitope, HBs(295-304), will benefit the development of immunotherapeutic approaches for HBV infection.

Inferring epitopes of a polymorphic antigen amidst broadly cross-reactive antibodies using protein microarrays: a study of OspC proteins of Borrelia burgdorferi

Epitope mapping studies aim to identify the binding sites of antibody-antigen interactions to enhance the development of vaccines, diagnostics and immunotherapeutic compounds. However, mapping is a laborious process employing time- and resource-consuming ‘wet bench’ techniques or epitope prediction software that are still in their infancy. For polymorphic antigens, another challenge is characterizing cross-reactivity between epitopes, teasing out distinctions between broadly cross-reactive responses, limited cross-reactions among variants and the truly type-specific responses. A refined understanding of cross-reactive antibody binding could guide the selection of the most informative subsets of variants for diagnostics and multivalent subunit vaccines. We explored the antibody binding reactivity of sera from human patients and Peromyscus leucopus rodents infected with Borrelia burgdorferi to the polymorphic outer surface protein C (OspC), an attractive candidate antigen for vaccine and improved diagnostics for Lyme disease. We constructed a protein microarray displaying 23 natural variants of OspC and quantified the degree of cross-reactive antibody binding between all pairs of variants, using Pearson correlation calculated on the reactivity values using three independent transforms of the raw data: (1) logarithmic, (2) rank, and (3) binary indicators. We observed that the global amino acid sequence identity between OspC pairs was a poor predictor of cross-reactive antibody binding. Then we asked if specific regions of the protein would better explain the observed cross-reactive binding and performed in silico screening of the linear sequence and 3-dimensional structure of OspC. This analysis pointed to residues 179 through 188 the fifth C-terminal helix of the structure as a major determinant of type-specific cross-reactive antibody binding. We developed bioinformatics methods to systematically analyze the relationship between local sequence/structure variation and cross-reactive antibody binding patterns among variants of a polymorphic antigen, and this method can be applied to other polymorphic antigens for which immune response data is available for multiple variants.

Linear and conformational B cell epitope prediction of the HER 2 ECD-subdomain III by in silico methods

Human epidermal growth factor receptor 2 (HER2) is a member of the epidermal growth factor receptor family of receptor tyrosine kinases that play important roles in all processes of cell development. Their overexpression is related to many cancers, including examples in the breast, ovaries and stomach. Anticancer therapies targeting the HER2 receptor have shown promise, and monoclonal antibodies against subdomains II and IV of the HER2 extra-cellular domain (ECD), Pertuzumab and Herceptin, are currently used in treatments for some types of breast cancers. Since anti HER2 antibodies targeting distinct epitopes have different biological effects on cancer cells; in this research linear and conformational B cell epitopes of HER2 ECD, subdomain III, were identified by bioinformatics analyses using a combination of linear B cell epitope prediction web servers such as ABCpred, BCPREDs, Bepired, Bcepred and Elliprro. Then, Discotope, CBtope and SUPERFICIAL software tools were employed for conformational B cell epitope prediction. In contrast to previously reported epitopes of HER2 ECD we predicted conformational B cell epitopes P1C: 378-393 (PESFDGDPASNTAPLQ) and P2C: 500-510 (PEDECVGEGLA) by the integrated strategy and and P4: PESFDGD-X-TAPLQ; P5: PESFDGDP X TAPLQ; P6: ESFDGDP X NTAPLQP; P7: PESFDGDP-X-NTAPLQ; P8: ESFDG-XX-TAPLQPEQL and P9: ESFDGDP- X-NTAPLQP by SUPERFICIAL software. These epitopes could be further used as peptide antigens to actively immune mice for development of new monoclonal antibodies and peptide cancer vaccines that target different epitopes or structural domains of HER2 ECD.

Comprehensive analysis of MHC ligands in clinical material by immunoaffinity-mass spectrometry

Major histocompatibility complexes (MHC) are expressed on antigen-presenting cells (APC) that display peptide antigens. This is a crucial step to activate a T-cell response. Since immunogenic ligand of MHC is closely related with autoimmunity, inflammatory diseases, and cancer, comprehensive analysis of MHC ligands (the so-called Ligandome) is essential to unveil disease pathogenesis. Recently, immunotherapies such as vaccination have been focused on as new therapies of cancer, HIV, and infectious diseases. Therefore, the importance of comprehensive analysis of MHC ligands is increasing. Mass spectrometry has been the core technology of ligand identification since the 1990s. The sensitivity of mass spectrometers has been improved dramatically in recent years; thus, it enables to identify MHC ligands in clinical materials. This chapter lays out the workflow of MHC ligand identification in clinical materials, especially human bronchoalveolar (BAL) cells. MHC-ligand complexes are enriched by immunoaffinity extraction and captured ligand peptides are identified by LC-MS/MS. MHC class II ligand in BAL cells is described in this text; however, this approach is applicable to MHC class I and other clinical materials such as tissues.

BEST: improved prediction of B-cell epitopes from antigen sequences

Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods.