An overview of bioinformatics tools for epitope prediction: Implications on vaccine development

AbstractExploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

In silico Analysis of Immunologic Regions of Surface Antigens (Sags) of Toxoplasma gondii

Background: Surface antigens (SAGs) of Toxoplasma gondii are known candidates for diagnostic tests and vaccines. The present study argues about the main necessary properties for determination and prediction of T-cell agretopes and B-cell epitopes of surface antigens of Toxoplasma gondii.Materials and Methods: Primary, secondary and tertiary structures of the proteins were analyzed by different methods. The three-dimensional structures were determined by use of ab initio method for prediction of discontinues epitopes. The agretopes and epitopes were predicted via several various web servers with different methods employed.Results: The results of in silico analyses showed that the regions 129-GAPAGRNNDGSSAPT-143 for protein p22, 234-SENPWQGNASSD-245 for protein p30 and 348-PGTEGESQAGT-358 for protein p43, have the highest immunogenic potential.Conclusion: We reached to three antigenic epitopes for cloning and protein expression. In following the purified polypeptide will be applied for diagnosis of Toxoplasma gondii

Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of convolutional neural networks (CNNs) to classify diverse tissue types from whole slide microscope images accurately. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid Deep and ensemble machine learning model that surpassed all preceding solutions for this classification task. Our model achieved 96.74% accuracy on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in advancing the task, we have made them publicly available for further research and development.Comment: 28 pages, 9 figure

Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR- presented peptides and epitopes

MHC peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein Ags to elicit functional T cell responses. Liquid chromatography–mass spectrometry analysis of MHC-eluted ligand data has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of Ag presentation have reached a high level of accuracy for both MHC class I and class II. In this study, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte Ag class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by eluted ligand data derived from bovine cell lines expressing a range of DRB3 alleles prevalent in Holstein–Friesian populations. The model generated (NetBoLAIIpan, available as a Web server at www.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR–restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced mass spectrometry peptidomics with immunoinformatics for characterization of the BoLA-DR Ag presentation system and provide a prediction tool that can be used to assist in rational evaluation and selection of bovine CD4 T cell epitopes

Designing a potent L1 protein-based HPV peptide vaccine : a bioinformatics approach

Background: Oncogenic human papilloma viruses (HPV) are the cause of various types of cancer, specifically cervical cancer. L1 protein is the main protein of HPV capsid which targeted in many vaccine-producing attempts. However, they have not enough coverage on the various high risk HPV types. Therefore, having a low cost potent HPV vaccine to protect against all members of the ɑ-papillomaviridea family will be promising. In this study, L1 protein-based peptide vaccine was designed using immunoinformatics methods which provides physicochemical properties such as stability in room temperature, potential of antigenicity, non-allergic properties and no requirement with eukaryotic host system. Results: The designed vaccine has two HPV conserved epitopes with lengths 18 and 27 amino acids in all members of α-papillomaviridea. These peptides promote humoral and cellular immunity and INF-γ responses. In order to ensure strong induction of immune responses, Flagellin, a Toll like receptor 5(TLR-5) agonist, and a short synthetic toll like receptor 4 (TLR-4) agonist were also joined to the epitopes. Structure of the designed- vaccine was validated using Rampage and ERRAT and a high quality 3D structure of the vaccine protein was provided. Docking studies demonstrated an appropriate and stable interaction between the vaccine and TLR-5. Conclusions: The vaccine is expected to have a high quality structure and suitable properties including high stability, solubility and a high potential to be expressed in 'E.coli'. High potentiality of the vaccine in inducing humoral and cellular immune responses, may be considered as an anti-tumor vaccine

Emergence of Major Pandemics: Examining the Use of AI for the Fight Against Covid-19

Covid-19 is an infectious disease caused by the SARS-CoV-2 virus and which is considered today as a global health emergency. Long before this pandemic, several others such as the plague of Athens, the plague of Antonine, the black plague, the Spanish flu, cholera, the Asian flu, AIDS raged, with consequences as fatal, even more serious than covid-19. The emergence of AI over the past ten years has brought it to the forefront of the response to this disease. The objective of this work is to present the significant contribution of AI in the fight against the new coronavirus, comparing it to previous large pandemics. A preliminary search of information related to past pandemics and covid-19 has been carried out. Next, the contribution of AI following the WHO framework for combating pandemics was presented. Finally, the discussion part resulted in the conclusion that if AI had already been fundamentally implemented during the time of the other major pandemics, the damage to human losses would have been less

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein–protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes

Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes

Background: Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102. Methodology/Findings: Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1. Conclusions/Significance: A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology