scholarly journals epitopepredict: a tool for integrated MHC binding prediction

Gigabyte ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Damien Farrell
Keyword(s):  
Adaptive Immune Response ◽  
Computational Prediction ◽  
T Cell Epitopes ◽  
Web Interface ◽  
Binding Prediction ◽  
Single Interface ◽  
Whole Genomes ◽  
Multiple Binding ◽  
Command Line Tool ◽  
Mhc Alleles

A key step in the cellular adaptive immune response is the presentation of antigens to T cells. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics, which allows in silico screening of peptides before experiments are performed. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open-source license from https://github.com/dmnfarrell/epitopepredict

scholarly journals epitopepredict: A tool for integrated MHC binding prediction

2021 ◽  
Author(s):  
Damien Farrell
Keyword(s):  
Immune Response ◽  
T Cell ◽  
Adaptive Immune Response ◽  
Computational Prediction ◽  
T Cell Epitopes ◽  
Binding Prediction ◽  
Single Interface ◽  
Mhc Molecules ◽  
Whole Genomes ◽  
Command Line Tool

AbstractA key step in the cellular adaptive immune response is the presentation of antigen to T cells. During this process short peptides processed from self or foreign proteins may be presented on the surface bound to MHC molecules for binding to T cell receptors. Those that bind and activate an immune response are called epitopes. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics which allows in silico screening of peptides before experiments are performed. The most effective approach is to estimate the binding affinity of a given peptide fragment to MHC class I or II molecules. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open source license from https://github.com/dmnfarrell/epitopepredict

scholarly journals SARS-CoV-2 Human T cell Epitopes: adaptive immune response against COVID-19.

2021 ◽  
Author(s):  
Alba Grifoni ◽  
John Sidney ◽  
Randi Vita ◽  
Bjoern Peters ◽  
Shane Crotty ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cell ◽  
Adaptive Immune Response ◽  
T Cell Epitopes ◽  
Adaptive Immune ◽  
Human T Cell

scholarly journals Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets

Vaccines ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 290 ◽  
Author(s):  
Sumit Mukherjee ◽  
Dmitry Tworowski ◽  
Rajesh Detroja ◽  
Sunanda Biswas Mukherjee ◽  
Milana Frenkel-Morgenstern
Keyword(s):  
Peptide Vaccine ◽  
Vaccine Design ◽  
Computational Prediction ◽  
Effective Vaccine ◽  
Cell Mediated Immunity ◽  
T Cell Epitopes ◽  
Protective Antigens ◽  
Global Pandemic ◽  
Time Required ◽  
Immunodominant Epitopes

A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks.

scholarly journals Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies

2020 ◽  
Author(s):  
Syed Faraz Ahmed ◽  
Ahmed A. Quadeer ◽  
Matthew R. McKay
Keyword(s):  
T Cell ◽  
B Cell ◽  
T Cell Epitopes ◽  
Population Coverage ◽  
Preliminary Identification ◽  
Coverage Analysis ◽  
Potential Vaccine ◽  
Novel Virus ◽  
Novel Coronavirus ◽  
Mhc Alleles

AbstractThe beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the available SARS-CoV-2 sequences (as of 9 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

scholarly journals Vaccine Design against Coronavirus Spike (S) Glycoprotein in Chicken: Immunoinformatic and Computational Approaches

2020 ◽  
Author(s):  
Sumaia Awad Elkariem Ali ◽  
Eman Ali Awadelkareem
Keyword(s):  
T Cell ◽  
B Cell ◽  
Rna Virus ◽  
Economic Loss ◽  
T Cell Epitopes ◽  
Resistance Response ◽  
Docking Analysis ◽  
S Protein ◽  
Infectious Bronchitis ◽  
Mhc Alleles

Abstract Background: Infectious bronchitis (IB) is a highly contagious respiratory disease in chickens and produces economic loss within the poultry industry. It is caused by a single stranded RNA virus belonging to Cronaviridae family. Methods: The present study used various tools in Immune Epitope Database (IEDB) to predict conserved B and T cell epitopes against IBV spike (S) protein that may perform a significant role in provoking the resistance response to IBV infection. Results: In B cell prediction methods, three epitopes (1139KKSSYY1144, 1140KSSYYT1145, 1141SSYYT1145) were selected as surface, linear and antigenic epitopes. Many MHCI and MHCII epitopes were predicted for IBV S protein. Among them 982YYITARDMY990 and 983YITARDMYM991 epitopes displayed high antigenicity, no allergenicity and no toxicity as well as great linkage with MHCI and MHCII alleles. Moreover, docking analysis of MHCI epitope produced strong binding affinity with BF2 alleles. Conclusion: Five conserved epitopes were expected from spike glycoprotein of IBV as the best B cell and T cell epitopes due to high antigenicity, no allergenicity and no toxicity. In addition, MHC epitopes showed great linkage with MHC alleles as well as strong interaction with BF2 alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV.

A Review on the Methods of Peptide-MHC Binding Prediction

2021 ◽  
Vol 15 (8) ◽  
pp. 878-888
Author(s):  
Yang Liu ◽  
Xia-hui Ouyang ◽  
Zhi-Xiong Xiao ◽  
Le Zhang ◽  
Yang Cao
Keyword(s):  
Machine Learning ◽  
T Cell ◽  
Structural Information ◽  
Three Dimensional ◽  
Learning Model ◽  
T Cell Epitopes ◽  
Binding Prediction ◽  
Future Directions ◽  
Mhc Molecules ◽  
Machine Learning Model

Background: T lymphocyte achieves an immune response by recognizing antigen peptides (also known as T cell epitopes) through major histocompatibility complex (MHC) molecules. The immunogenicity of T cell epitopes depends on their source and stability in combination with MHC molecules. The binding of the peptide to MHC is the most selective step, so predicting the binding affinity of the peptide to MHC is the principal step in predicting T cell epitopes. The identification of epitopes is of great significance in the research of vaccine design and T cell immune response. Objective: The traditional method for identifying epitopes is to synthesize and test the binding activity of peptide by experimental methods, which is not only time-consuming, but also expensive. In silico methods for predicting peptide-MHC binding emerge to pre-select candidate peptides for experimental testing, which greatly saves time and costs. By summarizing and analyzing these methods, we hope to have a better insight and provide guidance for future directions. Methods: Up to now, a number of methods have been developed to predict the binding ability of peptides to MHC based on various principles. Some of them employ matrix models or machine learning models based on the sequence characteristic embedded in peptides or MHC to predict the binding ability of peptides to MHC. Some others utilize the three-dimensional structural information of peptides or MHC, for example, by extracting three-dimensional structural information to construct a feature matrix or machine learning model, or directly using protein structure prediction, molecular docking to predict the binding mode of peptides and MHC. Results: Although the methods in predicting peptide-MHC binding based on the feature matrix or machine learning model can achieve high-throughput prediction, the accuracy of which depends heavily on the sequence characteristic of confirmed binding peptides. In addition, it cannot provide insights into the mechanism of antigen specificity. Therefore, such methods have certain limitations in practical applications. Methods in predicting peptide-MHC binding based on structural prediction or molecular docking are computationally intensive compared to the methods based on feature matrix or machine learning model and the challenge is how to predict a reliable structural model. Conclusion: This paper reviews the principles, advantages and disadvantages of the methods of peptide-MHC binding prediction and discussed the future directions to achieve more accurate predictions.

scholarly journals Conditional Immune Escape during Chronic Simian Immunodeficiency Virus Infection

2015 ◽  
Vol 90 (1) ◽  
pp. 545-552 ◽  
Author(s):  
Dane D. Gellerup ◽  
Alexis J. Balgeman ◽  
Chase W. Nelson ◽  
Adam J. Ericsen ◽  
Matthew Scarlotta ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Cd8 T Cells ◽  
Immune Escape ◽  
Cd8 T Cell ◽  
T Cell Epitopes ◽  
Cell Responses ◽  
Immunodeficiency Virus ◽  
Siv Infection ◽  
Mhc Alleles

ABSTRACT Anti-HIV CD8 T cells included in therapeutic treatments will need to target epitopes that do not accumulate escape mutations. Identifying the epitopes that do not accumulate variants but retain immunogenicity depends on both host major histocompatibility complex (MHC) genetics and the likelihood for an epitope to tolerate variation. We previously found that immune escape during acute SIV infection is conditional; the accumulation of mutations in T cell epitopes is limited, and the rate of accumulation depends on the number of epitopes being targeted. We have now tested the hypothesis that conditional immune escape extends into chronic SIV infection and that epitopes with a preserved wild-type sequence have the potential to elicit epitope-specific CD8 T cells. We deep sequenced simian immunodeficiency virus (SIV) from Mauritian cynomolgus macaques (MCMs) that were homozygous and heterozygous for the M3 MHC haplotype and had been infected with SIV for about 1 year. When interrogating variation within individual epitopes restricted by M3 MHC alleles, we found three categories of epitopes, which we called categories A, B, and C. Category B epitopes readily accumulated variants in M3-homozygous MCMs, but this was less common in M3-heterozygous MCMs. We then determined that chronic CD8 T cells specific for these epitopes were more likely preserved in the M3-heterozygous MCMs than M3-homozygous MCMs. We provide evidence that epitopes known to escape from chronic CD8 T cell responses in animals that are homozygous for a set of MHC alleles are preserved and retain immunogenicity in a host that is heterozygous for the same MHC alleles. IMPORTANCE Anti-HIV CD8 T cells that are part of therapeutic treatments will need to target epitopes that do not accumulate escape mutations. Defining these epitope sequences is a necessary precursor to designing approaches that enhance the functionality of CD8 T cells with the potential to control virus replication during chronic infection or after reactivation of latent virus. Using MHC-homozygous and -heterozygous Mauritian cynomolgus macaques, we have now obtained evidence that epitopes known to escape from chronic CD8 T cell responses in animals that are MHC homozygous are preserved and retain immunogenicity in a host that is heterozygous for the same MHC alleles. Importantly, our findings support the conditional immune escape hypothesis, such that the potential to present a greater number of CD8 T cell epitopes within a single animal can delay immune escape in targeted epitopes. As a result, certain epitope sequences can retain immunogenicity into chronic infection.

scholarly journals Benchmarking predictions of MHC class I restricted T cell epitopes

2019 ◽  
Author(s):  
Sinu Paul ◽  
Nathan P. Croft ◽  
Anthony W. Purcell ◽  
David C. Tscharke ◽  
Alessandro Sette ◽  
...  
Keyword(s):  
Neural Network ◽  
T Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Computational Prediction ◽  
Prediction Methods ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Prediction Tools ◽  
T Cell Epitope Prediction

AbstractT cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected mice, considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top 0.04% (N = 277) of peptides in the VACV proteome (N = 767,788). These performance metrics provide guidance for immunologists as to which prediction methods to use. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.Author summaryComputational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

scholarly journals CancerVar: a web server for improved evidence-based clinical interpretation of cancer somatic mutations and copy number abnormalities

2020 ◽  
Author(s):  
Quan Li ◽  
Zilin Ren ◽  
Yunyun Zhou ◽  
Kai Wang
Keyword(s):  
Clinical Significance ◽  
Copy Number ◽  
Somatic Mutations ◽  
Relevant Information ◽  
Web Interface ◽  
Clinical Interpretation ◽  
Link Type ◽  
Command Line Tool ◽  
Hotspot Mutations ◽  
User Friendly

ABSTRACTSeveral knowledgebases, such as CIViC, CGI and OncoKB, have been manually curated to support clinical interpretations of somatic mutations and copy number abnormalities (CNAs) in cancer. However, these resources focus on known hotspot mutations, and discrepancies or even conflicting interpretations have been observed between these knowledgebases. To standardize clinical interpretation, AMP/ASCO/CAP/ACMG/CGC jointly published consensus guidelines for the interpretations of somatic mutations and CNAs in 2017 and 2019, respectively. Based on these guidelines, we developed a standardized, semi-automated interpretation tool called CancerVar (Cancer Variants interpretation), with a user-friendly web interface to assess the clinical impacts of somatic variants. Using a semi-supervised method, CancerVar interpret the clinical impacts of cancer variants as four tiers: strong clinical significance, potential clinical significance, unknown clinical significance, benign/likely benign. CancerVar also allows users to specify criteria or adjust scoring weights as a customized interpretation strategy, and allows phenotype-driven scoring for specific types of cancer. Importantly, CancerVar generates automated texts to summarize clinical evidence on somatic variants, which greatly reduces manual workload to write interpretations that include relevant information from harmonized knowledgebases. CancerVar can be accessed at http://cancervar.wglab.org and it is open to all users without login requirements. The command line tool is also available at https://github.com/WGLab/CancerVar.

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