Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Engineering Modified mRNA-Based Vaccine against Dengue Virus Using Computational and Reverse Vaccinology Approaches

Dengue virus belonging to the family Flaviviridae and its four serotypes are responsible for dengue infections, which extend over 60 countries in tropical and subtropical areas of the world including Pakistan. During the ongoing dengue outbreak in Pakistan (2022), over 30,000 cases have been reported, and over 70 lives have been lost. The only commercialized vaccine against DENV, Dengvaxia, cannot be administered as a prophylactic measure to cure this infection due to various complications. Using machine learning and reverse vaccinology approaches, this study was designed to develop a tetravalent modified nucleotide mRNA vaccine using NS1, prM, and EIII sequences of dengue virus from Pakistani isolates. Based on high antigenicity, non-allergenicity, and toxicity profiling, B-cell epitope, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) putative vaccine targets were predicted. Molecular docking confirmed favorable interactions between T-cell epitopes and their respective HLA alleles, while normal mode analysis validated high-affinity interactions of vaccine proteins with immune receptors. In silico immune simulations confirmed adequate immune responses to eliminate the antigen and generate memory. Codon optimization, physicochemical features, nucleotide modifications, and suitable vector availability further ensured better antigen expression and adaptive immune responses. We predict that this vaccine construct may prove to be a good vaccinal candidate against dengue virus in vitro as well.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

In-silico designing of epitope-based vaccine against the seven banded grouper nervous necrosis virus affecting fish species

Neural necrosis virus (NNV) of family Nodaviridae affect wide range of fish species with viral encephalopathy and retinopathy causing mass mortality up to 100%. Currently there is no effective treatment and depopulation is only suggested recommendation. New avenues and approach are required to control this harmful malady. In this study we developed an epitope-based vaccine (EBV), against NNV using computation approach. We have selected two conserved proteins RNA-dependent RNA polymerase (RdRP) and capsid proteins. Based on more than ~ 1000 epitopes we selected six antigenic epitopes. These were conjugated to adjuvant and linker peptides to generate a full-length vaccine candidate. Biochemical structural properties were analyzed by Phyre2 server. ProtParam, Molprobity. Ramachandran plot results indicate that 98.7% residues are in a favorable region and 93.4% residues in the favored region. The engineered EBV binds to toll like receptor-5 (TLR5) an important elicitor of immune response. Further molecular docking by PatchDock server reveals the atomic contact energy (i.e. − 267.08) for the best docked model of EBV and TLR5 receptor. The molecular simulation results suggest a stable interaction; the RMSD and RMSF values are 1–4 Ǻ and 1–12Ǻ, respectively. Further we have suggested the best possible codon optimized sequence for its cloning and subsequent purification of the protein. Overall, this is a first report to suggest an in-silico method for generation of an EBV candidate against NNV. We surmise that the method and approach suggested could be used as a promising cure for NNVs.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

In Silico Prediction of T and B Cell Epitopes of SAG1-Related Sequence 3 (SRS3) Gene for Developing Toxoplasma gondii Vaccine

: Toxoplasmosis is a worldwide infection that can lead to serious problems in immune-compromised individuals and fetuses. A DNA vaccine strategy would be an ideal tool against Toxoplasma gondii. One of the necessary measures to provide an effective vaccine is the selection of proteins with high antigenicity. The SAG1-related sequence 3 (SRS3) protein is a major surface antigen in T. gondii that can be used as a vaccine candidate. In the present study, bioinformatics and computational methods were utilized to predict protein characteristics, as well as secondary and tertiary structures. The in silico approach is highly suited to analyze, design, and evaluate DNA vaccine strategies. Hence, in silico prediction was used to identify B and T cell epitopes and compare the antigenicity of SRS3 and other candidate genes of Toxoplasma previously applied in the production of vaccines. The results of the analysis theoretically showed that SRS3 has multiple epitopes with high antigenicity, proposing that SRS3 is a promising immunogenic candidate for the development of DNA vaccines against toxoplasmosis.

Tyrophagus putrescentiae group 4 allergen allergenicity and epitope prediction

INTRODUCTION AND OBJECTIVES

Allergen-specific immunotherapy (ASIT) is the only allergic disease-modifying therapy available for children and adults, and recombinant allergens are an interesting approach to improve allergy diagnosis and ASIT. Tyrophagus putrescentiae is a common storage mite that produces potent allergens. The aim of this study was to express and characterize recombinant group 4 allergen protein of T. putrescentiae (Tyr p 4), and to further investigate allergenicity and potential epitopes of Tyr p 4.

MATERIALS AND METHODS

The cDNA encoding Tyr p 4 was generated by RT-PCR and subcloned into pET-28a(+) plasmid. The plasmid was then transformed into E. coli cells for expression. After purification by nickel affinity chromatography and identification by SDS-PAGE, recombinant Tyr p 4 protein was used for a skin prick test and an ELISA to determine the allergic response.

RESULTS

Study participants' allergic response rate to Tyr p 4 protein was 13.3% (16/120). Eight B-cell epitopes and three T-cell epitopes of Tyr p 4 were predicted.

CONCLUSIONS

Similar to group 4 allergens of other species of mite, allergenicity of Tyr p 4 is weak. The expression, characterization and epitope prediction of recombinant Tyr p 4 protein provide a foundation for further study of this allergen in the diagnosis and ASIT of storage mite allergy.

Immuno- and bio-informatic analysis of hexon protein in human adenovirus D8 isolated from patients with keratoconjunctivitis

Aim: In silico analysis of the hexon protein of human adenovirus serotype D-8 isolated from a patients with keratoconjunctivitis in Iran. Materials & methods: The hexon gene of HAdV-D8 was amplified by PCR. HAdV-D8 recovered from EKC outbreak was isolated by growing in A549 cells. Results: The hexon gene isolated from a patient with EKC comprised 2829 nt and 942 aa. The analyses of selected B-cell epitopes prediction (KTFQPEPQIGENNWQD) and T-cell epitopes prediction (TENFDIDLAFFDIPQ), showed high score immunogenicity, which may prove this to be a promising candidate for epitope vaccine development. Conclusion: In silico analysis of selected B-cell epitopes prediction (KTFQPEPQIGENNWQD) and T-cell epitopes prediction (TENFDIDLAFFDIPQ) are immunogenic and provoke B- and T-cell responses.

Immunoinformatic and systems biology approaches to predict and validate peptide vaccines against Epstein-Barr virus (EBV)

Epstein-Barr virus (EBV), also known as human herpesvirus 4 (HHV-4), is a member of the Herpesviridae family and causes infectious mononucleosis, Burkitt's lymphoma, and nasopharyngeal carcinoma. Even in the United States of America, the situation is alarming, as EBV affects 95% of the young population between 35 and 40 years of age. In this study, both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted by using the ElliPro and NetCTL.1.2 webservers for EBV proteins (GH, GL, GB, GN, GM, GP42 and GP350). Molecular modelling tools were used to predict the 3D coordinates of peptides, and these peptides were then docked against the MHC molecules to obtain peptide-MHC complexes. Studies of their post-docking interactions helped to select potential candidates for the development of peptide vaccines. Our results predicted a total of 58 T-cell epitopes of EBV;  where the most potential were selected based on their TAP, MHC binding and C-terminal Cleavage score. The top most peptides were subjected to MD simulation and stability analysis. Validation of our predicted epitopes using a 0.45 µM concentration was carried out by using a systems biology approach. Our results suggest a panel of epitopes that could be used to immunize populations to protect against multiple diseases caused by EBV.

Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes

Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

Design of peptide-based epitope vaccine and further binding site scrutiny led to groundswell in drug discovery against Lassa virus

Lassa virus (LASV) is responsible for an acute viral hemorrhagic fever known as Lassa fever. Sequence analyses of LASV proteome identified the most immunogenic protein that led to predict both T-cell and B-cell epitopes and further target and binding site depiction could allow novel drug findings for drug discovery field against this virus. To induce both humoral and cell-mediated immunity peptide sequence SSNLYKGVY, conserved region 41-49 amino acids were found as the most potential B-cell and T-cell epitopes, respectively. The peptide sequence might intermingle with 17 HLA-I and 16 HLA-II molecules, also cover 49.15-96.82% population coverage within the common people of different countries where Lassa virus is endemic. To ensure the binding affinity to both HLA-I and HLA-II molecules were employed in docking simulation with suggested epitope sequence. Further the predicted 3D structure of the most immunogenic protein was analyzed to reveal out the binding site for the drug design against Lassa Virus. Herein, sequence analyses of proteome identified the most immunogenic protein that led to predict both T-cell and B-cell epitopes and further target and binding site depiction could allow novel drug findings for drug discovery field against this virus.

Mapping HLA-A2, -A3 and -B7 supertype-restricted T-cell epitopes in the ebolavirus proteome

Background

Ebolavirus (EBOV) is responsible for one of the most fatal diseases encountered by mankind. Cellular T-cell responses have been implicated to be important in providing protection against the virus. Antigenic variation can result in viral escape from immune recognition. Mapping targets of immune responses among the sequence of viral proteins is, thus, an important first step towards understanding the immune responses to viral variants and can aid in the identification of vaccine targets. Herein, we performed a large-scale, proteome-wide mapping and diversity analyses of putative HLA supertype-restricted T-cell epitopes of Zaire ebolavirus (ZEBOV), the most pathogenic species among the EBOV family.

Methods

All publicly available ZEBOV sequences (14,098) for each of the nine viral proteins were retrieved, removed of irrelevant and duplicate sequences, and aligned. The overall proteome diversity of the non-redundant sequences was studied by use of Shannon’s entropy. The sequences were predicted, by use of the NetCTLpan server, for HLA-A2, -A3, and -B7 supertype-restricted epitopes, which are relevant to African and other ethnicities and provide for large (~86%) population coverage. The predicted epitopes were mapped to the alignment of each protein for analyses of antigenic sequence diversity and relevance to structure and function. The putative epitopes were validated by comparison with experimentally confirmed epitopes.

Results & discussion

ZEBOV proteome was generally conserved, with an average entropy of 0.16. The 185 HLA supertype-restricted T-cell epitopes predicted (82 (A2), 37 (A3) and 66 (B7)) mapped to 125 alignment positions and covered ~24% of the proteome length. Many of the epitopes showed a propensity to co-localize at select positions of the alignment. Thirty (30) of the mapped positions were completely conserved and may be attractive for vaccine design. The remaining (95) positions had one or more epitopes, with or without non-epitope variants. A significant number (24) of the putative epitopes matched reported experimentally validated HLA ligands/T-cell epitopes of A2, A3 and/or B7 supertype representative allele restrictions. The epitopes generally corresponded to functional motifs/domains and there was no correlation to localization on the protein 3D structure. These data and the epitope map provide important insights into the interaction between EBOV and the host immune system.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4328-8) contains supplementary material, which is available to authorized users.

Designing of CD8+ and CD8+-overlapped CD4+ epitope vaccine by targeting late and early proteins of human papillomavirus

BACKGROUND AND AIM

Human papillomavirus (HPV) is an oncogenic agent that causes over 90% of cases of cervical cancer in the world. Currently available prophylactic vaccines are type specific and have less therapeutic efficiency. Therefore, we aimed to predict the potential species-specific and therapeutic epitopes from the protein sequences of HPV45 by using different immunoinformatics tools.

METHODS

Initially, we determined the antigenic potential of late (L1 and L2) and early (E1, E2, E4, E5, E6, and E7) proteins. Then, major histocompatibility complex class I-restricted CD8⁺ T-cell epitopes were selected based on their immunogenicity. In addition, epitope conservancy, population coverage (PC), and target receptor-binding affinity of the immunogenic epitopes were determined. Moreover, we predicted the possible CD8⁺, nested interferon gamma (IFN-γ)-producing CD4⁺, and linear B-cell epitopes. Further, antigenicity, allergenicity, immunogenicity, and system biology-based virtual pathway associated with cervical cancer were predicted to confirm the therapeutic efficiency of overlapped epitopes.

RESULTS

Twenty-seven immunogenic epitopes were found to exhibit cross-protection (≥55%) against the 15 high-risk HPV strains (16, 18, 31, 33, 35, 39, 51, 52, 56, 58, 59, 68, 69, 73, and 82). The highest PC was observed in Europe (96.30%), North America (93.98%), West Indies (90.34%), North Africa (90.14%), and East Asia (89.47%). Binding affinities of 79 docked complexes observed as global energy ranged from -10.80 to -86.71 kcal/mol. In addition, CD8⁺ epitope-overlapped segments in CD4⁺ and B-cell epitopes demonstrated that immunogenicity and IFN-γ-producing efficiency ranged from 0.0483 to 0.5941 and 0.046 to 18, respectively. Further, time core simulation revealed the overlapped epitopes involved in pRb, p53, COX-2, NF-X1, and HPV45 infection signaling pathways.

CONCLUSION

Even though the results of this study need to be confirmed by further experimental peptide sensitization studies, the findings on immunogenic and IFN-γ-producing CD8⁺ and overlapped epitopes provide new insights into HPV vaccine development.

Analysis of viral diversity for vaccine target discovery

BACKGROUND

Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.

RESULTS

This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.

CONCLUSION

These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

Homology modeling and prediction of B‑cell and T‑cell epitopes of the house dust mite allergen Der f 20

House dust mite allergens can cause allergic diseases, including asthma, atopic dermatitis and rhinitis. Der f 20 is a novel allergen of Dermatophagoides farina (Der f), which is an arginine kinase. In the present study, the B‑cell and T‑cell epitopes of Der f 20 were predicted. The protein attribution, patterns, physicochemical properties and secondary structure of Der f 20 were also predicted. Der f 20 is a member of the ATP:guanido phosphotransferase family and contains a phosphagen kinase pattern. Using homology modeling, the present study constructed a reasonable tertiary structure of Der f 20. Using BcePred, ABCpred, BCPred and BPAP systems, B‑cell epitopes at 20‑25, 41‑49, 111‑118, 131‑141, 170‑174 and 312‑321 were predicted. Using NetMHCIIpan‑3.0 and NetMHCII‑2.2, T‑cell epitopes were predicted at 194‑202, 239‑247 and 274‑282. These results provide a theoretical basis for the design off Der f 20 epitope‑based vaccines.

A novel in silico framework to improve MHC-I epitopes and break the tolerance to melanoma

Tolerance toward tumor antigens, which are shared by normal tissues, have often limited the efficacy of cancer vaccines. However, wild type epitopes can be tweaked to activate cross-reactive T-cell clones, resulting in antitumor activity. The design of these analogs (i.e., heteroclitic peptides) can be difficult and time-consuming since no automated in silico tools are available. Hereby we describe the development of an in silico framework to improve the selection of heteroclitic peptides. The Epitope Discovery and Improvement System (EDIS) was first validated by studying the model antigen SIINFEKL. Based on artificial neural network (ANN) predictions, we selected two mutant analogs that are characterized by an increased MHC-I binding affinity (SIINFAKL) or increased TCR stimulation (SIIWFEKL). Therapeutic vaccination using optimized peptides resulted in enhanced antitumor activity and against B16.OVA melanomas in vivo. The translational potential of the EDIS platform was further demonstrated by studying the melanoma-associated antigen tyrosinase related protein 2 (TRP2). Following therapeutic immunization with the EDIS-derived epitope SVYDFFAWL, a significant reduction in the growth of established B16.F10 tumors was observed, suggesting a break in the tolerance toward the wild type epitope. Finally, we tested a multi vaccine approach, demonstrating that combination of wild type and mutant epitopes targeting both TRP2 and OVA antigens increases the antitumor response. In conclusion, by taking advantage of available prediction servers and molecular dynamics simulations, we generated an innovative platform for studying the initial sequences and selecting lead candidates with improved immunological features. Taken together, EDIS is the first automated algorithm-driven platform to speed up the design of heteroclitic peptides that can be publicly queried online.

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.

Immunomics: a 21st century approach to vaccine development for complex pathogens

Immunomics is a relatively new field of research which integrates the disciplines of immunology, genomics, proteomics, transcriptomics and bioinformatics to characterize the host-pathogen interface. Herein, we discuss how rapid advances in molecular immunology, sophisticated tools and molecular databases are facilitating in-depth exploration of the immunome. In our opinion, an immunomics-based approach presides over traditional antigen and vaccine discovery methods that have proved ineffective for highly complex pathogens such as the causative agents of malaria, tuberculosis and schistosomiasis that have evolved genetic and immunological host-parasite adaptations over time. By using an integrative multidisciplinary approach, immunomics offers enormous potential to advance 21st century antigen discovery and rational vaccine design against complex pathogens such as the Plasmodium parasite.

From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector-borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the protein's ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell-mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4⁺ and CD8⁺ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC-I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope-based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Immunogenicity Assessment of Tumor Necrosis Factor Antagonists in the Clinical Laboratory

BACKGROUND

Tumor necrosis factor (TNF) antagonists are increasingly used for the treatment of inflammatory and autoimmune diseases. Immunogenicity of these drugs poses therapeutic challenges such as therapeutic failure and adverse effects in a number of patients. Evaluation of clinical nonresponsiveness includes laboratory testing for drug concentrations and detecting the presence of antidrug antibodies.

CONTENT

This review provides an overview of the immunogenicity of TNF antagonists and testing methodologies currently available for measuring antidrug antibody response, which decreases treatment efficacy and may result in therapeutic failure. This review summarizes methodologies such as binding assays, including ELISA and HPLC-based homogenous mobility shift assay, as well as functional cell-based assays such as reporter gene assay. Furthermore, based on the laboratory findings of testing for antidrug antibody response, as well as serum drug concentrations, an algorithm is provided for interpretation, based on the current available literature and guidelines, which may aid in determining optimal therapy after treatment failure.

SUMMARY

Laboratory testing methodologies for measuring serum concentrations of TNF inhibitors and antidrug antibodies are clinically available. These methods provide an evidence-based, personalized approach for the workup of patients showing treatment failure, which saves time and resources, and contributes to improved patient care.

Computer-aided vaccine designing approach against fish pathogens Edwardsiella tarda and Flavobacterium columnare using bioinformatics softwares

Edwardsiella tarda and Flavobacterium columnare are two important intracellular pathogenic bacteria that cause the infectious diseases edwardsiellosis and columnaris in wild and cultured fish. Prediction of major histocompatibility complex (MHC) binding is an important issue in T-cell epitope prediction. In a healthy immune system, the T-cells must recognize epitopes and induce the immune response. In this study, T-cell epitopes were predicted by using in silico immunoinformatics approach with the help of bioinformatics tools that are less expensive and are not time consuming. Such identification of binding interaction between peptides and MHC alleles aids in the discovery of new peptide vaccines. We have reported the potential peptides chosen from the outer membrane proteins (OMPs) of E. tarda and F. columnare, which interact well with MHC class I alleles. OMPs from E. tarda and F. columnare were selected and analyzed based on their antigenic and immunogenic properties. The OMPs of the genes TolC and FCOL_04620, respectively, from E. tarda and F. columnare were taken for study. Finally, two epitopes from the OMP of E. tarda exhibited excellent protein-peptide interaction when docked with MHC class I alleles. Five epitopes from the OMP of F. columnare had good protein-peptide interaction when docked with MHC class I alleles. Further in vitro studies can aid in the development of potential peptide vaccines using the predicted peptides.

Software tools for simultaneous data visualization and T cell epitopes and disorder prediction in proteins

We have developed EpDis and MassPred, extendable open source software tools that support bioinformatic research and enable parallel use of different methods for the prediction of T cell epitopes, disorder and disordered binding regions and hydropathy calculation. These tools offer a semi-automated installation of chosen sets of external predictors and an interface allowing for easy application of the prediction methods, which can be applied either to individual proteins or to datasets of a large number of proteins. In addition to access to prediction methods, the tools also provide visualization of the obtained results, calculation of consensus from results of different methods, as well as import of experimental data and their comparison with results obtained with different predictors. The tools also offer a graphical user interface and the possibility to store data and the results obtained using all of the integrated methods in the relational database or flat file for further analysis. The MassPred part enables a massive parallel application of all integrated predictors to the set of proteins. Both tools can be downloaded from http://bioinfo.matf.bg.ac.rs/home/downloads.wafl?cat=Software. Appendix A includes the technical description of the created tools and a list of supported predictors.

Review on the identification and role of Toxoplasma gondii antigenic epitopes

Toxoplasma gondii is an obligate intracellular protozoan parasite with a broad range of hosts, and it causes severe toxoplasmasis in both humans and animals. It is well known that the progression and severity of a disease depend on the immunological status of the host. Immunological studies on antigens indicate that antigens do not exert their functions through the entire protein molecule, but instead, specific epitopes are responsible for the immune response. Protein antigens not only contain epitope structures used by B, T, cytotoxic T lymphocyte (CTL), and NK cells to mediate immunological responses but can also contain structures that are unfavorable for protective immunity. Therefore, the study of antigenic epitopes from T. gondii has not only enhanced our understanding of the structure and function of antigens, the reactions between antigens and antibodies, and many other aspects of immunology but it also plays a significant role in the development of new diagnostic reagents and vaccines. In this review, we summarized the immune mechanisms induced by antigen epitopes and the latest advances in identifying T. gondii antigen epitopes. Particular attention was paid to the potential clinical usefulness of epitopes in this context. Through a critical analysis of the current state of knowledge, we elucidated the latest data concerning the biological effects of epitopes and the immune results aimed at the development of future epitope-based applications, such as vaccines and diagnostic reagents.

Peptides and peptidomimetics as immunomodulators

Peptides and peptidomimetics can function as immunomodulating agents by either blocking the immune response or stimulating the immune response to generate tolerance. Knowledge of B- or T-cell epitopes along with conformational constraints is important in the design of peptide-based immunomodulating agents. Work on the conformational aspects of peptides, synthesis and modified amino acid side chains have contributed to the development of a new generation of therapeutic agents for autoimmune diseases and cancer. The design of peptides/peptidomimetics for immunomodulation in autoimmune diseases such as multiple sclerosis, rheumatoid arthritis, systemic lupus and HIV infection is reviewed. In cancer therapy, peptide epitopes are used in such a way that the body is trained to recognize and fight the cancer cells locally as well as systemically.

Transgenic mouse model expressing P53(R172H), luciferase, EGFP, and KRAS(G12D) in a single open reading frame for live imaging of tumor

Genetically engineered mouse cancer models allow tumors to be imaged in vivo via co-expression of a reporter gene with a tumor-initiating gene. However, differential transcriptional and translational regulation between the tumor-initiating gene and the reporter gene can result in inconsistency between the actual tumor size and the size indicated by the imaging assay. To overcome this limitation, we developed a transgenic mouse in which two oncogenes, encoding P53^R172H and KRAS^G12D, are expressed together with two reporter genes, encoding enhanced green fluorescent protein (EGFP) and firefly luciferase, in a single open reading frame following Cre-mediated DNA excision. Systemic administration of adenovirus encoding Cre to these mice induced specific transgene expression in the liver. Repeated bioluminescence imaging of the mice revealed a continuous increase in the bioluminescent signal over time. A strong correlation was found between the bioluminescent signal and actual tumor size. Interestingly, all liver tumors induced by P53^R172H and KRAS^G12D in the model were hepatocellular adenomas. The mouse model was also used to trace cell proliferation in the epidermis via live fluorescence imaging. We anticipate that the transgenic mouse model will be useful for imaging tumor development in vivo and for investigating the oncogenic collaboration between P53^R172H and KRAS^G12D.

Structural and electrostatic analysis of HLA B-cell epitopes: inference on immunogenicity and prediction of humoral alloresponses

PURPOSE OF REVIEW

The immunogenic capacity of donor human leukocyte antigen (HLA) to induce humoral immune responses is not an intrinsic property of the mismatched alloantigen but depends on the HLA phenotype of the recipient. In recent years, advances in molecular sequence technology and information from X-ray crystallography have enabled structural comparison of donor and recipient HLA type providing an opportunity for a more rational approach for determining HLA compatibility. In this article, we review studies investigating the molecular basis of antibody-antigen interactions and present computational approaches to determine the complex physiochemical and structural properties of B-cell epitopes.

RECENT FINDINGS

The relative immunogenicity of individual HLA mismatches may be predicted from analysis of polymorphic amino acids at continuous and discontinuous HLA sequence positions. The use of alloantigen sequence information alone, however, provides limited insight into key determinants of B-cell epitope immunogenicity, such as the orientation, accessibility and physiochemical properties of amino acid side chains. Advances in computational molecular modelling techniques now enable assessment of HLA-alloantibody interactions at the atomic level. Recent evidence supports a strong link between HLA B-cell epitope surface electrostatic potential and their immunogenicity.

SUMMARY

Assessment of the surface electrostatic properties of HLA alloantigens and computational analyses of HLA-alloantibody interactions represent a promising area for future research into the molecular basis of HLA immunogenicity and antigenicity.

Identification and validation of T-cell epitopes in outer membrane protein (OMP) of Salmonella typhi

This study aims to design epitope-based peptides for the utility of vaccine development by targeting outer membrane protein F (Omp F), because two available licensed vaccines, live oral Ty21a and injectable polysaccharide, are 50% to 80% protective with a higher rate of side effects. Conventional vaccines take longer time for development and have less differentiation power between vaccinated and infected cells. On the other hand, Peptide-based vaccines present few advantages over other vaccines, such as stability of peptide, ease to manufacture, better storage, avoidance of infectious agents during manufacture, and different molecules can be linked with peptides to enhance their immunogenicity. Omp F is highly conserved and facilitates attachment and fusion of Salmonella typhi with host cells. Using various databases and tools, immune parameters of conserved sequences from Omp F of different isolates of Salmonella typhi were tested to predict probable epitopes. Binding analysis of the peptides with MHC molecules, epitopes conservancy, population coverage, and linear B cell epitope prediction were analyzed. Among all those predicted peptides, ESYTDMAPY epitope interacted with six MHC alleles and it shows highest amount of interaction compared to others. The cumulative population coverage for these epitopes as vaccine candidates was approximately 70%. Structural analysis suggested that epitope ESYTDMAPY fitted well into the epitope-binding groove of HLA-C*12:03, as this HLA molecule was common which interact with each and every predicted epitopes. So, this potential epitope may be linked with other molecules to enhance its immunogenicity and used for vaccine development.

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.

B-cell epitope engineering: A matter of recognizing protein features and motives

Using in vivo and in vitro studies B-cell epitopes have been identified on a number of proteins. These epitopes were used to develop predictive methods. After comparison of existing and emerging technologies, this review concludes that antigenicity is not described by physicochemical and structural characteristics of a protein alone. Molecular characteristics of the antigenic amino acids are required. How the structural context affects the selection of these amino acids by the antibody is unknown.:

Epitope distribution in ordered and disordered protein regions - part A. T-cell epitope frequency, affinity and hydropathy

Highly disordered protein regions are prevalently hydrophilic, extremely sensitive to proteolysis in vitro, and are expected to be under-represented as T-cell epitopes. The aim of this research was to find out whether disorder and hydropathy prediction methods could help in understanding epitope processing and presentation. According to the pan-specific T-cell epitope predictors NetMHCpan and NetMHCIIpan and 9 publicly available disorder predictors, frequency of epitopes presented by human leukocyte antigens (HLA) class-I or -II was found to be more than 2.5 times higher in ordered than in disordered protein regions (depending on the disorder predictor). Both HLA class-I and HLA class-II binding epitopes are prevalently hydrophilic in disordered and prevalently hydrophobic in ordered protein regions, whereas epitopes recognized by HLA class-II alleles are more hydrophobic than those recognized by HLA class-I. As regards both classes of HLA molecules, high-affinity binding epitopes display more hydrophobicity than low affinity-binding epitopes (in both ordered and disordered regions). Epitopes belonging to disordered protein regions were not predicted to have poor affinity to HLA class-II molecules, as expected from disorder intrinsic proteolytic instability. The relation of epitope hydrophobicity and order/disorder location was also valid if alleles were grouped according to the HLA class-I and HLA class-II supertypes, except for the class-I supertype A3 in which the main part of recognized epitopes was prevalently hydrophilic. Regarding specific supertypes, the affinity of epitopes belonging to ordered regions varies only slightly (depending on the disorder predictor) compared to the affinity of epitopes in corresponding disordered regions. The distribution of epitopes in ordered and disordered protein regions has revealed that the curves of order-epitope distribution were convex-like while the curves of disorder-epitope distribution were concave-like. The percentage of prevalently hydrophobic epitopes increases with the enhancement of epitope promiscuity level and moving from disordered to ordered regions. These data suggests that reverse vaccinology, oriented towards promiscuous and high-affinity epitopes, is also oriented towards prevalently hydrophobic, ordered regions. The analysis of predicted and experimentally evaluated epitopes of cancer-testis antigen MAGE-A3 has confirmed that the majority of T-cell epitopes, particularly those that are promiscuous or naturally processed, was located in ordered and disorder/order boundary protein regions overlapping hydrophobic regions.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Increased diversity of the HLA-B40 ligandome by the presentation of peptides phosphorylated at their main anchor residue

Human leukocyte antigen (HLA) class I molecules bind peptides derived from the intracellular degradation of endogenous proteins and present them to cytotoxic T lymphocytes, allowing the immune system to detect transformed or virally infected cells. It is known that HLA class I-associated peptides may harbor posttranslational modifications. In particular, phosphorylated ligands have raised much interest as potential targets for cancer immunotherapy. By combining affinity purification with high-resolution mass spectrometry, we identified more than 2000 unique ligands bound to HLA-B40. Sequence analysis revealed two major anchor motifs: aspartic or glutamic acid at peptide position 2 (P2) and methionine, phenylalanine, or aliphatic residues at the C terminus. The use of immobilized metal ion and TiO2 affinity chromatography allowed the characterization of 85 phosphorylated ligands. We further confirmed every sequence belonging to this subset by comparing its experimental MS2 spectrum with that obtained upon fragmentation of the corresponding synthetic peptide. Remarkably, three phospholigands lacked a canonical anchor residue at P2, containing phosphoserine instead. Binding assays showed that these peptides bound to HLA-B40 with high affinity. Together, our data demonstrate that the peptidome of a given HLA allotype can be broadened by the presentation of peptides with posttranslational modifications at major anchor positions. We suggest that ligands with phosphorylated residues at P2 might be optimal targets for T-cell-based cancer immunotherapy.

Designing of interferon-gamma inducing MHC class-II binders

Background

The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4⁺ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides.

Results

It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC.

Conclusion

Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).

Reviewers

This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.

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.

The structural basis of antibody-antigen recognition

The function of antibodies (Abs) involves specific binding to antigens (Ags) and activation of other components of the immune system to fight pathogens. The six hypervariable loops within the variable domains of Abs, commonly termed complementarity determining regions (CDRs), are widely assumed to be responsible for Ag recognition, while the constant domains are believed to mediate effector activation. Recent studies and analyses of the growing number of available Ab structures, indicate that this clear functional separation between the two regions may be an oversimplification. Some positions within the CDRs have been shown to never participate in Ag binding and some off-CDRs residues often contribute critically to the interaction with the Ag. Moreover, there is now growing evidence for non-local and even allosteric effects in Ab-Ag interaction in which Ag binding affects the constant region and vice versa. This review summarizes and discusses the structural basis of Ag recognition, elaborating on the contribution of different structural determinants of the Ab to Ag binding and recognition. We discuss the CDRs, the different approaches for their identification and their relationship to the Ag interface. We also review what is currently known about the contribution of non-CDRs regions to Ag recognition, namely the framework regions (FRs) and the constant domains. The suggested mechanisms by which these regions contribute to Ag binding are discussed. On the Ag side of the interaction, we discuss attempts to predict B-cell epitopes and the suggested idea to incorporate Ab information into B-cell epitope prediction schemes. Beyond improving the understanding of immunity, characterization of the functional role of different parts of the Ab molecule may help in Ab engineering, design of CDR-derived peptides, and epitope prediction.

Computational Resources for MHC Ligand Identification

Advances in the high-throughput determination of functional modulators of major histocompatibility complex (MHC) and improved computational predictions of MHC ligands have rendered the rational design of immunomodulatory peptides feasible. Proteome-derived peptides and 'reverse vaccinology' by computational means will play a driving role in future vaccine design. Here we review the molecular mechanisms of the MHC mediated immune response, present the computational approaches that have emerged in this area of biotechnology, and provide an overview of publicly available computational resources for predicting and designing new peptidic MHC ligands.

Immunogenicity of anti-TNF biologic therapies for rheumatoid arthritis

Currently, five anti-TNF biologic agents are approved for the treatment of rheumatoid arthritis (RA): adalimumab, infliximab, etanercept, golimumab and certolizumab pegol. Formation of anti-drug antibodies (ADA) has been associated with all five agents. In the case of adalimumab and infliximab, immunogenicity is strongly linked to subtherapeutic serum drug levels and a lack of clinical response, but for the other three agents, data on immunogenicity are scarce, suggesting that further research would be valuable. Low ADA levels might not influence the efficacy of anti-TNF therapy, whereas high ADA levels impair treatment efficacy by considerably reducing unbound drug levels. Immunogenicity is not only an issue in patients treated with anti-TNF biologic agents; the immunogenicity of other therapeutic proteins, such as factor VIII and interferons, is well known and has been investigated for many years. The results of such studies suggest that investigations to determine the optimal treatment regimen (drug dosing, treatment schedule and co-medication) required to minimize the likelihood of ADA formation might be an effective and practical way to deal with the immunogenicity of anti-TNF biologic agents for RA.

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

BACKGROUND

MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently.

RESULTS

The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II.

CONCLUSIONS

Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.