Review of Immunoinformatic approaches to in-silico B-cell epitope prediction

In this paper, the current state of in-silico, B-cell epitope prediction is discussed. Recommendations for improving some of the approaches encountered are outlined, along with the presentation of an entirely novel technique, which uses molecular mechanics for epitope classification, evaluation and prediction

Reconnoitering Mycobacterium tuberculosis lipoproteins to design subunit vaccine by immunoinformatics approach

Background: Tuberculosis is an aerosol transmitted disease of human beings caused by Mycobacterium tuberculosis (Mtb). The only available vaccine for Mtb is Bacillus Calmette-Guérin (BCG). Currently no alternative or booster is available for BCG. The objective of this predictive approach was based on binding of MHC-I and MHC-II and B cell epitopes of Mtb for mouse host.Methods: Immunoinformatics approach was used to design subunit vaccine (SV) by joining 8 MHC-I bindings, 6 MHC-II bindings, and 8 B-Cell epitopes with AAV, GPGPG, and KK amino acid linkers, respectively. The efficacy of the SV was enhanced through Mtb protein Rv3763 (LpqH, PDB ID= 4ZJM) as an adjuvant at the N-terminal of SV. The in silico analyses evaluated the SV to predict allergenicity, antigenicity, and physico-chemical properties.Results: Predictions revealed that SV is non-allergic and highly antigenic. The physico-chemical analysis showed that the SV was stable and basic in nature. The three-dimensional structure of SV was stable with a high binding affinity against the mouse TLR2 receptor. In silico cloning suggested the effective transformation of SV into the eukaryotic expression vector.Conclusion: This study permits preclinical validation of the designed SV in mouse host to confirm its immunogenic potential and efficacy, which will help in controlling tuberculosis.Keywords: Immunoinformatics; Docking; Subunit vaccine; Lipoprotein; Tuberculosis

Predicting Candidate Epitopes on Ebola Virus for Possible Vaccine Development

Zaire ebolavirus, a member of family Filoviridae is the cause of hemorrhagic fever. Due to lack of appropriate antiviral or vaccine, this disease is very lethal. In this study, we tried to find epitopes for superficial glycoprotein and nucleoprotein of Zaire ebolavirus (that have high antigenicity for MHC I, II and B cells) by using in silico methods and immunoinformatics approach. By using CTLPred, SYFPEITHI and ProPred web applications for MHC class I and SYFPEITHI and ProPred1 web applications for MHC class II, we had been able to find epitopes (peptides) that have the highest score. Also ElliPro, IgPred and DiscoTope web tools had been performed to predict B cells conformational epitopes. Linear epitope prediction for B cell was performed with six methods from IEDB. All of the results that including candidate epitopes for T cells and B cells were reported. It was expected that these peptides could be stimulated immune response and used for designing the multipeptide vaccine against ZEV but these results should be reliable with experimental analysis

FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.</p> <p>Description</p> <p>We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens <it>Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis</it>. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, <it>C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum </it>and <it>P. brasiliensis </it>thus showing high sensitivity and specificity at a threshold of 0.511. In case of <it>P. brasiliensis </it>the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.</p> <p>Conclusion</p> <p>FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.</p

Machine learning-enhanced T cell neoepitope discovery for immunotherapy design

Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received funding from Fundação para a Ciência e a Tecnologia (FCT) contract IF/00474/2014; PhD scholarship SFRH/BD/132797/2017.info:eu-repo/semantics/publishedVersio

iVax: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

Computational vaccine design, also known as computational vaccinology, encompasses epitope mapping, antigen selection and immunogen design using computational tools. The iVAX toolkit is an integrated set of tools that has been in development since 1998 by De Groot and Martin. It comprises a suite of immunoinformatics algorithms for triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, eliminating regulatory T cell epitopes, and optimizing antigens for immunogenicity and protection against disease. iVAX has been applied to vaccine development programs for emerging infectious diseases, cancer antigens and biodefense targets. Several iVAX vaccine design projects have had success in pre-clinical studies in animal models and are progressing toward clinical studies. The toolkit now incorporates a range of immunoinformatics tools for infectious disease and cancer immunotherapy vaccine design. This article will provide a guide to the iVAX approach to computational vaccinology