Information

MHC restricted peptide


What is an MHC restricted peptide?

I got this definition from wikipaedia, but cannot exactly extract what the phrase MHC restricted peptide means.

MHC-restricted antigen recognition, or MHC restriction, refers to the fact that a given T cell will recognize a peptide antigen only when it is bound to a host body's own MHC molecule. Normally, as T cells are stimulated only in the presence of self-MHC molecules, antigen is recognized only as peptides bound to self-MHC molecules.

I have not studied biology since last 8 years and now I am going through it because I need it for my research. So if someone can describe it in simple language it would be very helpful.


First of all MHC stands for major histocompatibility complex. There are two types of MHC.

MHC type one is present on all of our cells with a nucleus. The purpose of these protein complexes is called antigen presentation. T-cells cannot recognize free antigens on their own, it has to be presented to them in the proper way. This is what these proteins do. In every cell there are lots and lots of proteins, that get digested to small polypeptides (short aminoacid sequences) by proteases during the natural recycling process. The cell takes a small portion of these polypeptides and presents them on its surface through MHC I complexes, that the immune system can read. This is like the cell saying to the immune syytem that "hey I got these proteins in me". Now if the cell is infected (by a virus most likely) then the "attacker's" proteins get digested and presented as well and its like saying " hey I'm infected and the attacker has these proteins".

MHC II serves similar function but it is only present on professional antigen presenting cells (APCs). These complexes present peptides derived from proteins consumed and digested through phagocytosis or receptor mediated endocytosis. It's like saying "hey we got a larger attacker and it has these proteins"

So, long story short T-cells cannot recognise antigens on their own it needs to be presented to them on MHC complexes and then and only then can they be activated.

Edit: A list of useful articles in the topic of antigen processing / presentation for further details:

January 14, 1998 14:59 Annual Reviews AR052-12 Annu. Rev. Immunol. 1998. 16:323-58 MECHANISMS OF MHC CLASS I-RESTRICTED ANTIGEN PROCESSING Eric Pamer and Peter Cresswell

Cell, Vol. 76, 287-299, January 28, 1994. MHC-Dependent Antigen Processing and Peptide Presentation: Providing Ligands for T Lymphocyte Activation

Terry Y Nakagawa, Alexaiider Y Kudensky The role of lysosomal proteinases in MHC class Il-mediated antigen processing and presentation
This one may require subscription.

and the wiki page :)


Regulatory CD4 + T cells recognize MHC-II-restricted peptide epitopes of apolipoprotein B

CD4 + T cells play an important role in atherosclerosis, but their antigen specificity is poorly understood. Immunization with apolipoprotein B (ApoB, core protein of low density lipoprotein) is known to be atheroprotective in animal models. Here, we report on a human APOB peptide, p18, that is sequence-identical in mouse ApoB and binds to both mouse and human MHC-II.

Methods

We constructed p18-tetramers to detect human and mouse APOB-specific T cells and assayed their phenotype by flow cytometry including CD4 lineage transcription factors, intracellular cytokines, and TCR activation. Apoe −/− mice were vaccinated with p18 peptide or adjuvants alone and atherosclerotic burden in the aorta was determined.

Results

In human peripheral blood mononuclear cells from donors without cardiovascular disease (CVD), p18 specific CD4 + T cells detected by a new HLA-DR-p18 tetramers were mostly Foxp3 + regulatory T cells (Tregs). Donors with subclinical CVD as detected by carotid artery ultrasound had Tregs co-expressing RORγt or T-bet which were both almost absent in donors without CVD. In Apoe −/− mice, immunization with p18 induced Tregs and reduced atherosclerotic lesions. After peptide restimulation, responding CD4 + T cells identified by Nur77-GFP were highly enriched in Tregs. A new mouse I-A b -p18 tetramer identified the expansion of p18-specific CD4 + T cells upon vaccination, which were enriched for IL-10-producing Tregs.

Conclusion

These findings show that APOB p18 specific CD4 + T cells are mainly Tregs in healthy donors, but co-express other CD4 lineage transcription factors in donors with subclinical CVD. This study identifies ApoB peptide 18 as the first Treg epitope in human and mouse atherosclerosis.


Act II: MHC Restriction and Broader Concepts of Immunological Specificity

Investigators have clearly demonstrated that a given TCR can recognize the same self-MHC molecule presenting more than one nominal antigen peptide with varying affinities that are above some necessary threshold to permit signal transduction via TCR/CD3 and cellular activation (3,37). Therefore, TCR specificity for antigen, such as antibody specificity for antigen, is not absolute, as argued earlier in discussing HLA supertypes and as addressed later in discussing how to reconcile MHC restriction with the high potency of alloimmune responses by both CD4 + and CD8 + T cells.

Previously, I proposed (14) that immunological specificity is actually a family of concepts, including specificity defined with respect to: (i) monovalent recognition, (ii) multivalent recognition, (iii) cellular activation and effector function, and (iv) endpoints that are the result of the functioning of the whole immune system. In exploring this concept, I focused on antibodies and B lymphocytes. I would now suggest that a similar framework can be applied to TCR-based antigen recognition and T cells.

According to this concept, it is of interest to investigate both magnitudes of binding between TCR variable domains and different MHC/peptide complexes and see how well they predict measures of T cell activation and function. Reasons to expect some deviations from absolute correlation between TCR binding affinity and cellular phenotypes involve not only the complexities of signal transduction through the TCR/CD3 complex but also the contributions to cellular activation of signal transduction through other receptors on the T cell surface, such as CD4 or CD8, CD28, and numerous other cell surface glycoproteins that have ligands on antigen-presenting cells. Another way to state this point is that specificity as assessed solely by binding assays involving TCR and MHC/peptide ligands may differ from specificity as evaluated on the basis of measures of cellular behavior.

The preceding point is also consistent with results from experiments using what have been termed “altered peptide ligands,” which correspond to cognate nominal antigen peptides with one or a small number of amino acid substitutions. In 1991, Evavold and Allen demonstrated, using a mouse model, that some such peptides when presented by the appropriate class II MHC molecule to a clonal population of CD4 + T cells specific for the same MHC restriction element and the cognate peptide can elicit a range of responses by the T cells that differ in one or more respects from the responses induced by the recognition of the cognate MHC/peptide ligand (8). In their 1991 experiments, stimulation with the altered peptide/MHC antigen complex elicited cytokine production but not clonal proliferation whereas stimulation with the cognate ligand elicited both cytokine production and clonal proliferation.

Subsequent work over the years since 1991 has revealed that stimulation of clonal T cells by altered peptide ligands presented by the cognate MHC molecules can elicit diverse responses corresponding to various types of partial agonism to antagonism of T cell activation (5). Therefore, investigators of these phenomena have appropriately inferred that signal transduction through the TCR-CD3 complex can vary in a variety of ways that yield distinctive constellations of functional outputs.

Another set of ideas related to molecular interactions that I have explicitly applied to how antibodies recognize antigens can equally be applied to the recognition of MHC/peptide complexes by TCRs. In a series of publications (13,16,17) beginning in the early 1990s, I suggested that the term “epitope” can be associated with at least three operational meanings. For the purposes of this discussion, I will assume that both receptor and ligand are proteins composed of amino acids.

These three senses of “epitope” in the context of MHC restriction are: (i) the set of HLA and nominal antigen peptide amino acid residues that make van der Waals contact with TCR residues, (ii) the set of MHC/peptide residues that contribute substantially to the free energy of complex formation as typically assessed through amino acid substitution (a useful but not always straightforward means), and (iii) the set of MHC/peptide residues that contribute substantially to the differential free energy of complex formation for a given TCR when comparing cognate and non-cognate MHC/peptide complexes. The necessity for the third definition arises in part from the fact that an amino acid residue of the cognate MHC/peptide complex might be weakly contributory or effectively neutral with respect to the energetics of the interaction with the relevant TCR, but a different amino acid at the same position in the non-cognate MHC/peptide complex may massively oppose an interaction. In that case, the residue in question in the cognate ligand is an unimportant component of the epitope in sense 2 but critical in sense 3.

A comprehensive analysis of these ideas applied to the full range of TCR-MHC/peptide interactions involving standard or nominal antigens as well as major and minor alloantigens is beyond the scope of this article. My purpose here has been to illustrate the connection between my ideas on the intricacies of immune recognition and those of Peter and Rolf on the basis of T cell specificity.


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MHC-I peptides get out of the groove and enable a novel mechanism of HIV-1 escape

Major histocompatibility complex class I (MHC-I) molecules play a crucial role in immunity by capturing peptides for presentation to T cells and natural killer (NK) cells. The peptide termini are tethered within the MHC-I antigen-binding groove, but it is unknown whether other presentation modes occur. Here we show that 20% of the HLA-B*57:01 peptide repertoire comprises N-terminally extended sets characterized by a common motif at position 1 (P1) to P2. Structures of HLA-B*57:01 presenting N-terminally extended peptides, including the immunodominant HIV-1 Gag epitope TW10 (TSTLQEQIGW), showed that the N terminus protrudes from the peptide-binding groove. The common escape mutant TSNLQEQIGW bound HLA-B*57:01 canonically, adopting a dramatically different conformation than the TW10 peptide. This affected recognition by killer cell immunoglobulin-like receptor (KIR) 3DL1 expressed on NK cells. We thus define a previously uncharacterized feature of the human leukocyte antigen class I (HLA-I) immunopeptidome that has implications for viral immune escape. We further suggest that recognition of the HLA-B*57:01-TW10 epitope is governed by a 'molecular tension' between the adaptive and innate immune systems.


Materials & methods

Selection of methods

As a first step, we compiled a list of all freely available CD8+ T cell epitope prediction methods by querying Google and Google Scholar. We identified 44 methods (S1 Table) that had executable algorithms freely available publicly (excluding those that required us to train a prediction model), and excluding commercial prediction tools that required us to obtain licenses. Out of these 44 methods, we selected those that had trained models available for the two mouse alleles for which we had benchmarking data (H-2D b & H-2K b ). Further, we contacted the authors of the selected methods and excluded the ones that the authors explicitly wanted to be excluded from the benchmarking for different reasons (mostly because the methods were not updated recently or new version of the methods were to be released soon). The final list included 15 methods that were selected to be included in the benchmarking: ARB [9], BIMAS [2], IEDB Consensus [7], MHCflurry [10], MHCLovac [11], NetMHC-4.0 [12], NetMHCpan-3.0 [13], NetMHCpan-4.0 [14], PAComplex [15], PREDEP [16], ProPred1 [17], Rankpep [18], SMM [19], SMMPMBEC [20], SYFPEITHI [3]. Out of the 15 methods, NetMHCpan-4.0 offered two different outputs, the first one being the predicted binding affinity of a peptide (referred as NetMHCpan-4.0-B), and the second the predicted probability of a peptide being a ligand in terms of a probability score (NetMHCpan-4.0-L). Both these outputs were evaluated separately. Similarly, MHCflurry could use two different models, first one trained with only binding data (MHCflurry-B) and second one incorporating data on peptides identified by mass-spectrometry (MHCflurry-L). Both these models were evaluated separately. Considering these as separate methods, a total of 17 methods were included in the benchmark, and are described in more detail in S1 Table. The methods differed widely in the peptide lengths that they could predict for each allele. For example, while MHCLovac could predict lengths 7–13 for both alleles, PAComplex could predict for only 8-mers of H-2K b and none of the lengths in case of H-2D b . The methods also differed in the kind of prediction scores provided but ultimately they all represented a score that was intended to correlate with the probability of a peptide being an epitope in the context of the given MHC molecule. A complete list of the peptide lengths allowed for prediction per allele by each method and the kind of prediction scores they provide are given in S2 Table.

Dataset of VACV peptides

For the benchmark analysis, we used the peptide data set described in Croft et al., 2019 (S3 Table). This dataset represented a comprehensive set of peptides naturally processed and eluted from VACV-infected cells in addition to any previously identified epitopes. The total of 220 VACV peptides were tested for T cell immune responses in infected mice. Of these peptides, 172 were eluted from H-2D b and K b molecules from VACV-infected cells as described in detail in Croft et al., 2019. In brief, DC2.4 cells (derived from C57BL/6 mice [21] that expressed H-2 b MHC molecules were infected with VACV. The H-2D b and K b molecules were then individually isolated and the bound peptides eluted. The peptides were then analyzed by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). The remaining peptides in the set were not detected by LC-MS/MS and included 46 VACV-derived H-2 b restricted peptides/epitopes from the IEDB [22] and one entirely unpublished epitope and another that was mapped from a longer published sequence [23] identified by the Tscharke laboratory. Immune reactivity for each of these 220 peptides was then tested 8 times and the peptides that tested positive more than four times were classified as “major epitopes” and those tested positive four or fewer times were classified as “minor epitopes”. All peptides that were never positive were classified as “nonimmunogenic”. There were 83 peptides classified as “major” positives (S3 Table), ranging in lengths 7–13. In addition to the 220 peptides tested for immunogenicity, we generated all possible peptides of lengths 7–13 from the VACV reference proteome (https://www.uniprot.org/proteomes/UP000000344) (S1 File), which were also considered non-immunogenic, based on them not being found in elution assays on infected cells, and not being found positive in any of the many studies recorded in the IEDB. The entire dataset comprised 767,788 peptide/allele combinations.

Performance evaluations

The performance of the prediction methods was evaluated mainly by generating ROC curves (Receiver operating characteristic curve) and calculating the AUCROC (Area under the curve of ROC curve). The ROC curve shows the performance of a prediction model by plotting the True positive rate (TPR, fraction of true positives out of the all real positives) against the False positive rate (FPR, fraction of false positives out of the all real negatives) as the threshold of the predicted score is varied. AUCROC is the area under the ROC curve which summarizes the curve information and acts as a single value representing the performance of the classifier system. A predictor whose prediction is equivalent to random will have an AUC = 0.5 whereas a perfect predictor will have AUC = 1.0. That is, the closer the AUC is to 1.0, the better the prediction method. AUC values were first calculated on different sets of peptides grouped by length and allele separately. Secondly, overall AUCs were calculated by taking peptides of all lengths and both alleles together, which reflects the real life usability of having to decide which peptides to test. In this calculation, poor scores were assigned to peptides of lengths where predictions were not available for a given method. For example, in the case of SMM, lower numerical values of the prediction score indicate better epitope candidates, with scores ranging from 0 to 100. So a score of 999 was assigned to all peptides of lengths for which predictions were not available in SMM (lengths 7, 12 and 13 for both alleles). Similarly a score of -100 was assigned in case of SYFPEITHI (H-2D b : 7–8, 11–13 H-2K b : 7, 9–13) where a higher numerical value of predicted score indicates better epitope candidate and the scores ranging from -4 to 32.

Fully automated pipeline to generate benchmarking metrics

The Python scikit-learn package [24] was used for calculating the AUCs and Python matplotlib package [25] was used for plotting. A python script that can generate all results and plots along with the input file containing all peptides and their prediction scores from each method, immunogenicity category, T cell response scores, the "ProteinPilot confidence scores" representing the mass-spectrometry (MS) identification confidence level of the peptides and the number of times the peptides were identified by MS are provided in the GitLab repository (https://gitlab.com/iedb-tools/cd8-t-cell-epitope-prediction-benchmarking). The repository also contains the outputs from the script, i.e. the relevant results and plots. This will enable interested users to check our results and add their own prediction algorithms.


Materials and methods

Sequence retrieval and analyses

The sequences of 56 MHC class I genes (including predicted genes) from bats were retrieved from the NCBI database (S1 Table). Higher mammal MHC I heavy chain sequences were retrieved from the Immuno Polymorphism Database (IPD) (www.ebi.ac.uk/ipd/mhc) and the UniProt database (www.uniprot.org). Previously deposited marsupial (opossum, tammar wallaby, koala, Tasmanian devil) and platypus MHC I transcripts were included in these analyses (S1 Table). Sequence alignments were generated with ClustalX [48] and ESPript [49]. Similarities were calculated using DNAMAN (https://www.lynnon.com/).

The proteomes of 1,000 MERS-CoV genomes and 1,000 SARS-CoV genomes were retrieved from GenBank, respectively. After sequence alignment with MAFFT, the dominant amino acid for each site was elected as a reference sequence. The mutation frequency = the number of overall mutations for each amino acid/(the number of occurrences of the amino acid in the reference sequence×total number of sequences).

Peptide synthesis and preparation of expression constructs

To screen potential peptides for binding to Ptal-N*01:01, the proteomes of the bat-related viruses EBOV (NP: GenBank no. AF054908.1 GP: GenBank no. AKG65250.1), MERS-CoV (GenBank no. AXN92228.1), H17N10 influenza-like virus (A/little yellow-shouldered bat/Guatemala/060/2010(H17N10)), and H18N11 influenza-like virus (A/flat-faced bat/Peru/033/2010(H18N11)) were utilized to predict the candidate peptides. The candidate peptides were predicted and selected according to the recently reported motif, by which the two Ptal-N*01:01–binding peptides, HeV1 and HeV2, derived from HeV were also synthesized (S2 Table) [30]. The potential binding scores of the selected peptides were also predicted through the online NetMHCpan 4.0 server (http://www.cbs.dtu.dk/services/NetMHCpan/) [50] and Rosetta FlexPepDock, which is based on structure modeling [51,52], so that we prefer choose peptides that conform to the motif of Ptal-N*01:01 [30]. The peptide purity was determined to be >95% by analytical HPLC and mass spectrometry. The peptides were stored at −80°C as freeze-dried powders and were dissolved in DMSO before use.

The cDNAs for the heavy chain of P. alecto MHC I Ptal-N*01:01 (GenBank no. KT987929) [30] and bat β2m (GenBank no. XP_006920478.1) were synthesized (Genewiz, Beijing, China). Ptal-N*01:01 sequence was deposited to GenBank by Wynne and colleagues, and Ptal-N*01:01–binding peptides HeV1 and HeV2 were identified in their study [30]. Although Ng and colleagues reported the first Ptal-N*01:01 [29], the sequence is not available online. To investigate the function of Met 52 Asp 53 Leu 54 in Ptal-N*01:01, a mutant termed Ptal-N*01:01(-3aa) with a deletion of these three amino acids was constructed. The amplified products expressing the extracellular domain (residues 1–277) of Ptal-N*01:01 and bat β2m (residues 1–98) were cloned into a pET28a vector (Novagen). The expression plasmid for human β2m (residues 1–99) was previously constructed in our laboratory [53].

Refolding and purification of bat class I complexes

Renaturation and purification of Ptal-N*01:01 assembled with peptides were performed as previously described [54,55]. Generally, bat MHC I Ptal-N*01:01 heavy chain and bat β2m were overexpressed as inclusion bodies in the BL21(DE3) strain of Escherichia coli, and the purified inclusion bodies of the proteins were solubilized in 6 M guanidine-HCl buffer with a concentration of 30 mg/mL. Then, injection and dilution of MHC heavy chain, β2m, and peptide occurred at a molar ratio of 1:1:3 in refolding buffer (100 mM Tris-HCl [pH 8], 2 mM EDTA, 400 mM L-Arg, 0.5 mM oxidized glutathione, and 5 mM reduced glutathione) [34]. After 24 hours for protein refolding, the Ptal-N*01:01 complexes were concentrated and exchanged into a buffer of 20 mM Tris-HCl (pH 8) and 50 mM NaCl and then purified using a Superdex 200 16/60 HiLoad (GE Healthcare, Beijing, China) size-exclusion column.

Crystallization, data collection, and processing

Crystallization was performed using the sitting drop vapor diffusion technique. The Ptal-N*01:01/peptide complexes were screened through Crystal Screen kit I/II, Index Screen kit, PEGIon kit I/II, and the PEGRx kit (Hampton Research). Plates were incubated at 291 K and 277 K and assessed for crystal growth after 1–2 weeks. Ptal-N*01:01/HeV1 crystals were observed in 0.2 M NaCl, 0.1 M Bis-Tris (pH 5.5), and 25% (w/v) polyethylene glycol 3,350 at a concentration of 7.5 mg/mL. Ptal-N*01:01/HeV1(human β2m) crystals were observed in 0.1 M HEPES, pH 7.0, 2% w/v polyethylene glycol 3,350. Monomer bat β2m were grown in 0.1 M BIS-TRIS, pH 6.5, 8% w/v polyethylene glycol monomethyl ether 5,000. Single crystals of Ptal-N*01:01/HeV2 were grown in 0.075 M HEPES (pH 7.5), 15% (w/v) polyethylene glycol 10,000, and 25% (v/v) glycerol at a protein concentration of 10 mg/mL. Single crystals of Ptal-N*01:01/EBOV-NP1 were grown in 0.1 M succinic acid (pH 7.0) and 15% (w/v) polyethylene glycol 3,350. Single crystals of Ptal-N*01:01/EBOV-NP2 were grown in 0.2 M ammonium acetate, 0.1 M Tris (pH 8.0), and 16% (w/v) polyethylene glycol 10,000. Single crystals of Ptal-N*01:01(-3aa)/HeV1 were grown in 0.2 M sodium formate and 20% (w/v) polyethylene glycol 3,350. Single crystals of Ptal-N*01:01/H17N10-NP were grown in 0.075 M HEPES (pH 7.5), 15% (w/v) polyethylene glycol 10,000, and 25% (v/v) glycerol. Single crystals of Ptal-N*01:01/MERS-CoV-S3 were grown in 0.2 M sodium acetate trihydrate and 20% (w/v) polyethylene glycol 3,350. For cryoprotection, crystals were transferred to reservoir solutions containing 20% glycerol and then flash-cooled in a stream of gaseous nitrogen at 100 K. X-ray diffraction data were collected at beamline BL19U of the Shanghai Synchrotron Radiation Facility. The data collection statistics are shown in Table 1.

Structure determination and analyses

The collected intensities were subsequently processed and scaled using the Denzo program and the HKL2000 software package (HKL Research). The structures were determined using molecular replacement with the program Phaser MR in CCP4 [56]. The model used was the structure coordinates with Protein Data Bank (PDB) code 5F1I [35], and restrained refinement was performed using REFMAC5 from CCP4. Extensive model building was performed by hand using COOT [57]. The stereochemical quality of the final model was assessed with the program REFINE in Phenix or CCP4 (Table 1). Structure-related figures were generated using PyMOL (http://www.pymol.org/) and COOT.

Determination of protein thermostability using CD spectroscopy

The thermostabilities of Ptal-N*01:01 with two group key peptides were tested by CD spectroscopy. All complexes were refolded, purified, and measured at 0.2 mg/mL in a solution of 20 mM Tris (pH 8) and 50 mM NaCl. CD spectra at 218 nm were measured on a Chirascan spectrometer (Applied Photophysics) using a thermostatically controlled cuvette at temperature intervals of 0.2°C at an ascending rate of 1°C/minute between 20 and 90°C. The unfolded fraction (%) is expressed as (θ−θa)/(θa−θb), where θa and θb are the mean residue ellipticity values in the fully folded and fully unfolded states, respectively. The denaturation curves were generated by nonlinear fitting with OriginPro 8.0 (OriginLab) [58]. The Tm was calculated by fitting data to the denaturation curves and using inflection-determining derivatives.


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In: PLoS Computational Biology , Vol. 16, No. 5, e1007757, 26.05.2020.

Research output : Contribution to journal › Article › Research › peer-review

T1 - Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system

N2 - T 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 C57BL/6 mice (expressing H-2Db and H-2Kb), 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 N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. 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.

AB - T 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 C57BL/6 mice (expressing H-2Db and H-2Kb), 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 N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. 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.


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