INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

AbstractIn silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

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INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features

BioMed Research International ◽

10.1155/2020/5798356 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Guangzhi Wang ◽

Huihui Wan ◽

Xingxing Jian ◽

Yuyu Li ◽

Jian Ouyang ◽

...

Keyword(s):

Amino Acid ◽

T Cell ◽

Antigen Processing ◽

External Validation ◽

Cell Epitope ◽

Epitope Prediction ◽

Validation Data ◽

Data Set ◽

Immunogenic Peptide ◽

Related Amino Acid

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called “neoantigens.” Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool “INeo-Epp” was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

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CD4+ T-cell epitope prediction using antigen processing constraints

Journal of Immunological Methods ◽

10.1016/j.jim.2016.02.013 ◽

2016 ◽

Vol 432 ◽

pp. 72-81 ◽

Cited By ~ 12

Author(s):

Ramgopal R. Mettu ◽

Tysheena Charles ◽

Samuel J. Landry

Keyword(s):

T Cell ◽

Antigen Processing ◽

Cell Epitope ◽

Epitope Prediction ◽

Cd4 T Cell ◽

T Cell Epitope ◽

T Cell Epitope Prediction

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Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Journal of Healthcare Engineering ◽

10.1155/2021/9591670 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Syed Nisar Hussain Bukhari ◽

Amit Jain ◽

Ehtishamul Haq ◽

Moaiad Ahmad Khder ◽

Rahul Neware ◽

...

Keyword(s):

Machine Learning ◽

T Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

Ensemble Model ◽

T Cell Epitope ◽

T Cell Epitopes ◽

Peptide Vaccines ◽

Data Set ◽

T Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

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CD4+ T-cell epitope prediction by combined analysis of antigen conformational flexibility and peptide-MHCII binding affinity

10.1101/2020.05.21.109967 ◽

2020 ◽

Author(s):

Tysheena Charles ◽

Daniel L. Moss ◽

Pawan Bhat ◽

Peyton W. Moore ◽

Nicholas A. Kummer ◽

...

Keyword(s):

T Cell ◽

Crystal Structures ◽

Binding Affinity ◽

Antigen Processing ◽

Cell Epitope ◽

Epitope Prediction ◽

Conformational Flexibility ◽

Cd4 T Cell ◽

T Cell Epitope ◽

X Ray

AbstractAntigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition between antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in the plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy without considering peptide-MHCII binding affinity. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease-sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, peptide binding to MHCII proteins, or both. For a sample of 13 diverse antigens larger than 200 residues, accuracy of epitope prediction by the combination of APL and I-Ab-MHCII-peptide affinity approached 40%. When MHCII allele specificity is also diverse, such as in human immunity, prediction of dominant epitopes by APL alone approached 40%. Since dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL; and thus, the requirement for a crystal structure is not a severe limitation.

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Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽

10.1136/bmjopen-2020-040778 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040778

Author(s):

Vineet Kumar Kamal ◽

Ravindra Mohan Pandey ◽

Deepak Agrawal

Keyword(s):

Hospital Mortality ◽

External Validation ◽

Trauma Centre ◽

Unfavourable Outcome ◽

Motor Score ◽

Validation Data ◽

Data Set ◽

Development Data ◽

Level 1 ◽

Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

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CD4+ Th immunogenicity of the Ascaris spp. secreted products

10.1101/699231 ◽

2019 ◽

Author(s):

Friederike Ebner ◽

Eliot Morrison ◽

Miriam Bertazzon ◽

Ankur Midha ◽

Susanne Hartmann ◽

...

Keyword(s):

T Cell ◽

Antigen Processing ◽

Cell Epitope ◽

Processing System ◽

Healthy Human ◽

Major Health Problem ◽

Major Health ◽

T Cell Lines

SummaryAscaris spp. is a major health problem of humans and animals alike, and understanding the immunogenicity of its antigens is required for developing urgently needed vaccines. The parasite-secreted products represent the most relevant, yet highly complex (>250 proteins) antigens of Ascaris spp. as defining the pathogen-host interplay. We applied an in vitro antigen processing system coupled to quantitative proteomics to identify potential CD4+ Th cell epitopes in Ascaris suum-secreted products. This approach restricts the theoretical list of epitopes, based on affinity prediction, by a factor of ∼1200. More importantly, selection of 2 candidate peptides based on experimental evidence demonstrated the presence of epitope-reactive T cells in Ascaris-specific T cell lines generated from healthy human individuals. Thus, this stringent work pipeline identifies a human haplotype-specific T cell epitope of a major human pathogen. The methodology described represents an easily adaptable platform for characterization of highly complex pathogenic antigens and their MHCII-restriction.

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T-Cell Epitope Prediction Methods: An Overview

Methods in Molecular Biology - Immunoinformatics ◽

10.1007/978-1-4939-1115-8_19 ◽

2014 ◽

pp. 333-364 ◽

Cited By ~ 33

Author(s):

Dattatraya V. Desai ◽

Urmila Kulkarni-Kale

Keyword(s):

T Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

Prediction Methods ◽

T Cell Epitope ◽

T Cell Epitope Prediction

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A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbz051 ◽

2020 ◽

Vol 21 (4) ◽

pp. 1119-1135 ◽

Cited By ~ 27

Author(s):

Shutao Mei ◽

Fuyi Li ◽

André Leier ◽

Tatiana T Marquez-Lago ◽

Kailin Giam ◽

...

Keyword(s):

Machine Learning ◽

T Cell ◽

Peptide Binding ◽

Hla Class I ◽

Machine Learning Algorithms ◽

Class I ◽

Target Cells ◽

Binding Prediction ◽

Validation Data ◽

Data Set

Abstract Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

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Identification of naturally processed viral nonapeptides allows their quantification in infected cells and suggests an allele-specific T cell epitope forecast.

Journal of Experimental Medicine ◽

10.1084/jem.174.2.425 ◽

1991 ◽

Vol 174 (2) ◽

pp. 425-434 ◽

Cited By ~ 174

Author(s):

K Falk ◽

O Rötzschke ◽

K Deres ◽

J Metzger ◽

G Jung ◽

...

Keyword(s):

Amino Acid ◽

T Cell ◽

Synthetic Peptides ◽

Cell Epitope ◽

Reversed Phase ◽

T Cell Epitope ◽

Amino Acid Residues ◽

Allele Specific ◽

Infected Cells ◽

Natural Peptides

Virus-specific cytotoxic T lymphocytes (CTL) recognize virus-derived peptides presented by major histocompatibility complex (MHC) class I molecules on virus-infected cells. Such peptides have been isolated from infected cells and were compared to synthetic peptides. We found previously the Kd- or Db-restricted natural influenza nucleoprotein peptides to coelute on reversed phase high performance liquid chromatography columns with certain peptidic by-products present in synthetic peptide preparations. Here we show by extensive biochemical and immunological comparison that the natural peptides in all respects behave as the surmised synthetic nonapeptides, and thus, must be identical to them. The absolute amounts of these natural peptides contained in infected cells could be determined to be between 220 and 540 copies by comparing with defined amounts of pure synthetic nonapeptides. The comparison of the natural Kd-restricted peptide with published synthetic peptides known to contain other Kd-restricted CTL epitopes suggested a new MHC allele-specific T cell epitope forecast method, based on the defined length of nine amino acid residues and on critical amino acid residues at the second and the last position.

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