PLOS PATHOGENS OPINION In silico veritas? Potential limitations for SARS- CoV-2 vaccine development based on T-cell epitope prediction Sandra Silva-Arrieta1, Philip J. R. Goulder2,3,4, Christian Brander ID1,5,6* 1 IrsiCaixa AIDS Research Institute, Hospital Germans Trias i Pujol, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain, 2 Department of Paediatrics, University of Oxford, Oxford, United Kingdom, 3 HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa, 4 Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, United States of America, 5 University of Vic–Central University of Catalonia, Catalonia, Vic, Spain, 6 Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain * cbrander@irsicaixa.es a1111111111 a1111111111 a1111111111 The development of a vaccine able to prevent infection or severe disease course of SARS-CoV- a1111111111 2 is a priority to stem the current COVID-19 pandemic and to be better prepared for future a1111111111 flare-ups. To accelerate T-cell immunogen design, many current approaches are employing epitope prediction strategies. Although such approaches have great merit, it is also important that unbiased approaches to characterizing the T-cell response to SARS-CoV-2 are incorpo- rated into vaccine design, in order to generate a comprehensive picture of the total virus-spe- OPEN ACCESS cific T-cell response and to define correlates of protective immunity against the virus. Citation: Silva-Arrieta S, Goulder PJR, Brander C Ever since the first identification of binding motifs for T-cell antigens presented by HLA (2020) In silico veritas? Potential limitations for class I molecules by Rammensee and colleagues almost 30 years ago, epitope identification has SARS-CoV-2 vaccine development based on T-cell been greatly facilitated by epitope prediction algorithms [1]. Over the years, many vaccines epitope prediction. PLoS Pathog 16(6): e1008607. designs targeting infectious pathogens as well as cancer neoantigens have been based on in sil- https://doi.org/10.1371/journal.ppat.1008607 ico prediction of potential HLA class I–restricted epitopes, and a series of vaccine candidates Editor: Kasturi Haldar, University of Notre Dame, that apply such strategies to SARS-CoV-2 are currently in development. However, even though UNITED STATES numerous prediction algorithms have been developed and gradually improved, there are sev- Published: June 4, 2020 eral considerations that may threaten or limit the success of such approaches. Copyright: © 2020 Silva-Arrieta et al. This is an open access article distributed under the terms of Epitope length is highly variable and not properly captured by the Creative Commons Attribution License, which permits unrestricted use, distribution, and prediction algorithms reproduction in any medium, provided the original Although the initial characterization of eluted HLA class I epitopes by the Rammensee lab in author and source are credited. 1991 produced predominantly 9-mer epitopes, there was already the notion of longer and Funding: The present work was in parts support by shorter sequences that can be bound and effectively presented by HLA class I molecules [1]. grants from the Wellcome Trust WT104748MA This observation has been validated many times over since, with some HLA class I alleles for and NIH RO1 AI133673 to PJRG and by the which the 9-mer seems to be rather the exceptional length than the rule (such as HLA-B8, B35, European Union’s Horizon 2020 research and B57, B27, and others). In fact, even the first identified epitopes in influenza and HIV infection, innovation program under grant agreement 681137-EAVI2020 and NIH grant P01-AI131568 to defined by other approaches a couple of years before the first binding motif was described, CB. The funders had no role in study design, data turned out to be 13 and 10 amino acids in length, respectively [2,3]. Especially for HIV, we collection and analysis, decision to publish, or have equally fallen victim to consider “optimal” HLA class I epitopes those for which func- preparation of the manuscript. tional experiments have identified the shortest reactive peptide and thereby unwillingly con- Competing interests: The authors have declared tributed to cementing the dogma of 9-mer optimal epitope length as the rule [4]. However, that no competing interests exist. based on elution studies by many laboratories and crystallographic structural analyses, it is PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1008607 June 4, 2020 1/5 PLOS PATHOGENS evident that many epitope variants presented by HLA class I molecules can exceed the 9-mer length [5,6]. In fact, even for members of the HLA-A11 allele family that have very strict and limited C-anchor binding requirements, elution studies have demonstrated that only about 20% of the (self and viral) epitopes are exactly 9 amino acids in length [7]. Of note, this was done in a system that uses soluble class I molecules secreted from the antigen-presenting cells and thus limits the bias to also identify (longer) processing intermediates. Similar observations have been made for other HLA class I alleles—for instance, HLA-B8, which consistently pres- ents peptides that are shorter than 9-mers; or HLA-B57, for which the immunodominant and protective HIV-specific epitope is an 11-mer; or where epitopes have been found to be entirely embedded in other epitopes [8], all cases that are generally not identified or scored as indepen- dent epitopes by prediction algorithms. Multiple specificities toward the same epitope regions can be elicited by allowing for length variants One major limitation of using predicted epitope sequences of 9 amino acids in T-cell vaccine designs is that T-cell responses to only that specific region will be induced by the vaccine. However, the example of HIV infection (which likely has the best-characterized epitope “land- scape” described) shows how this can limit the vaccine-induced response: by (1) screening just 5 amino acids up- and downstream of each described optimal (mostly 9-mer) epitope for suit- able anchor positions satisfying the currently known allele-specific binding motif and (2) allowing for an epitope length of 9 to 12 amino acids, the number of potential independent epitopes that could bind the given HLA class I molecule and induce T-cell responses to the same region more than doubles [5]. Although this may be of paramount importance to cope with HIV variability and to prevent rapid cytotoxic T-lymphocyte (CTL) escape, other viruses, even genetically robust DNA-based viruses such as Epstein-Barr virus (EBV), have been shown to present as swarms of quasi-species and to be able to develop effective CTL escape variants as well [9,10]. Thus, it seems critical that SARS-CoV-2 vaccine development take this aspect into account and test immunogen designs that can target the same epitopic region by multiple specificities, ideally composed of a polyclonal T-cell receptor repertoire to (1) broaden the response and thus increase the chances of eliciting more potent clonotypes and (2) more effectively prevent T-cell escape. HLA class I binding is promiscuous and motifs are poorly defined for less well-studied HLA class I alleles There are to date more than 20,000 HLA class I alleles described that translate into more than 11,000 different HLA class A, B, and C molecules. For many of these alleles, sequence similari- ties and structural analyses have allowed grouping them into larger, so-called HLA supertypes that share epitope binding similarities [11]. However, there is extensive binding promiscuity even by short 9-mer peptides that go well beyond the specific allele and its assigned supertype and for which commonly used minimal cutoffs of binding affinities would not identify the reactive peptides [12]. In fact, screening data using several hundred epitopes derived from dif- ferent viral infections suggest that the current prediction algorithm may miss a large number of reactive “optimal” epitopes and possibly an even larger proportion of responses when pre- dicting potential epitopes on a full protein sequence [12]. An extreme example of this may be epitopes predicted (and presented) by HLA-E, for which binding peptides showed an unex- pected broad permissiveness at anchor positions and extensive structural freedom to bind to the presenting class I molecule [13]. The current urgency by which the scientific community attempts to achieve a viable T-cell vaccine to SARS-CoV-2 will, however, not allow generation PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1008607 June 4, 2020 2/5 PLOS PATHOGENS of larger training sets that could improve these predictions and thus may mislead the design into regions of the virus that are rich in epitopes fulfilling binding motifs of the most com- monly studied class I alleles. In addition, these approaches will not be able to sufficiently cover the genetically different, and often more numerous, populations with less well-characterized genetics and, in parallel, less well-established healthcare systems in which vaccines may be the only way to stem against the pandemic. Antigen-processing preferences and TAP-mediated peptide translocation Another critical limitation that is not properly addressed by most epitope prediction tools is the need to identify appropriate antigen-processing sites that could give rise to predicted, pre- sented epitopes. As the composition of the antigen-processing machinery changes upon proin- flammatory signals and can differ between different cell types, currently known prediction algorithms will likely not be suited to estimate the processing efficacy of viral antigens in the context of an infected target cell. Thus, without functional validation, there exists the risk that predicted epitopes may represent great theoretical or actual binder to HLA class I molecules, but they are either not processed at all or not processed at sufficient levels to induce a strong immune response and to sensitize infected target cells for CTL-mediated killing. In addition, the allelic diversity of TAP (transporter associated with antigen processing) genes, which encode for proteins that form a bottleneck for the translocation of processed antigen into the lumen of the endoplasmatic reticulum, are only partly integrated into peptide prediction algorithms. Functional characterization, cross-reactivity, and immunopathology Finally, there are evident advantages in identifying antigens not only to protect from SARS-- CoV-2 infection but to develop vaccine candidates that could act against past and future coro- navirus outbreaks. This would call for focus on particularly conserved regions between isolates and different coronaviruses, something that can be readily implemented in epitope prediction strategies, even if conservation on an epitope level does not need to be complete. Whether such cross-reactive T-cell responses could indeed mediate cross-protection and are associated with effective control of different coronaviruses, remains to be seen. Of note, emerging data start providing some insights into T-cell effector function profiles that may be protective and those that may be associated with a more severe clinical course of SARS-CoV-2 infection [14]. This is reminiscent of the situation in Dengue virus infection, in which some cross-reactive T- cell responses have been associated with the observed immunopathological consequence [15]. Thus, a functional assessment of T-cell responses in individuals with mild, moderate, and severe COVID-19 disease courses may be indicated, so that potential detrimental effects of preexisting, cross-reactive T-cell immunity can be avoided. However, without an unbiased screening for total virus-specific T-cell responses with classical and alternative T-cell effector functions [16], it will be difficult to differentiate most beneficial from potentially harmful T- cell specificities. Conclusions With the urgency to develop effective measures to control the COVID-19 pandemic and to design vaccine strategies useful to confront future outbreaks and epidemics of SARS-CoV-2 and related coronaviruses, accelerated programs for T-cell immunogen design are needed. T- PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1008607 June 4, 2020 3/5 PLOS PATHOGENS cell epitope prediction algorithms are an effective tool to narrow down the potential immuno- gen cargo in a future SARS-CoV-2 vaccine from the total viral proteome. However, approaches exclusively based on 9-mer epitope prediction will potentially miss critically important responses, and even those based on 9-mer and 10-mer epitope prediction have similar short- comings, for the reasons described above. In addition, a relatively unbiased approach to char- acterizing the T-cell response using overlapping peptides will facilitate understanding of immune correlates of SARS-CoV-2 control versus disease. The benefit of the predicted 9-mer/ 10-mer approach would be that, when used in combination with panels of overlapping pep- tides spanning the viral proteome, optimal epitopes will be more rapidly identified and, at the same time, immune correlates of disease protection evaluated in an unbiased fashion. These considerations should not be overlooked, as invaluable time and resources could be directed in directions that may not yield the desired success. References 1. Falk K, Rötzschke O, Stevanovic S, Jung G, Rammensee HG. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature. 1991; 351(6324):290–6. 2. Gotch F, Rothbard J, Howland K, Townsend A, McMichael A. Cytotoxic T lymphocytes recognize a frag- ment of influenza virus matrix protein in association with HLA-A2. Nature. 1987; 326(6116):881–2. 3. Nixon DF, Townsend AR, Elvin JG, Rizza CR, Gallwey J, McMichael AJ. HIV-1 gag-specific cytotoxic T lymphocytes defined with recombinant vaccinia virus and synthetic peptides Nature. 1988; 336 (6198):484–7. 4. Brander C, Walker BD. The HLA-class I restricted CTL response in HIV-1 infection: Identification of opti- mal epitopes. In: Korber B, Brander C, Walker BD, Koup R, Moore J, Haynes B, Myers G, editors. HIV Molecular Immunology Database 1995. Los Alamos, NM: Los Alamos National Laboratory: Theoretical Biology and Biophysics; 1995. p. 1–9. 5. Llano A, Cedeño S, Silva-Arrieta S, Brander C. The 2019 Optimal HIV CTL epitopes update: Growing diversity in epitope length and HLA restriction. In: Yusim K, Korber BTM, Brander C, Barouch D, de Boer R, Haynes BF, Koup R, Moore JP, Walker BD, Watkins DI, editors. HIV Molecular Immunology 2018. Los Alamos, NM: Los Alamos National Laboratory, Theoretical Biology and Biophysics; 2019. p. 3–27. 6. Burrows SR, Rossjohn J, McCluskey J. Have we cut ourselves too short in mapping CTL epitopes? Trends Immunol. 2006; 27:11–6. 7. Yaciuk JC, Skaley M, Bardet W, Schafer F, Mojsilovic D, Cate S, et al. Direct interrogation of viral pep- tides presented by the class I HLA of HIV-infected T cells. J Virol. 2014; 88(22):12992–3004. https://doi. org/10.1128/JVI.01914-14 PMID: 25165114 8. Goulder PJ, Tang Y, Pelton SI, Walker BD. HLA-B57-restricted cytotoxic T-lymphocyte activity in a sin- gle infected subject toward two optimal epitopes, one of which is entirely contained within the other. J Virol. 2000; 74(11):5291–9. 9. de Campos-Lima PO, Gavioli R, Zhang QJ, Wallace LE, Dolcetti R, Rowe M, et al. HLA-A11 epitope loss isolates of Epstein-Barr virus from a highly A111 population. Science. 1993; 260:98–100. 10. Gutiérrez MI, Spangler G, Kingma D, Raffeld M, Guerrero I, Misad O, et al. Epstein-Barr virus in nasal lymphomas contains multiple ongoing mutations in the EBNA-1 gene. Blood. 1998; 92(2):600–6. 11. Sidney J, Peters B, Frahm N, Brander C, Sette A. HLA class I supertypes: a revised and updated classi- fication. BMC Immunol. 2008; 9:1. https://doi.org/10.1186/1471-2172-9-1 PMID: 18211710 12. Frahm N, Yusim K, Suscovich TJ, Adams S, Sidney J, Hraber P, et al. Extensive HLA class I allele pro- miscuity among viral CTL epitopes. Eur J Immunol. 2007; 37(9):2419–33 13. Walters LC, Harlos K, Brackenridge S, Rozbesky D, Barrett JR, Jain V, et al. Pathogen-derived HLA-E bound epitopes reveal broad primary anchor pocket tolerability and conformationally malleable peptide binding. Nat Commun. 2018; 9(1):3137. https://doi.org/10.1038/s41467-018-05459-z PMID: 30087334 14. Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H, et al. Clinical and immunologic features in severe and moderate Coronavirus Disease 2019. J Clin Invest. 2020; 130(5)2620–2629. https://doi.org/10. 1172/JCI137244 Epub 2020 Mar 27. PMID: 32217835 15. Friberg H, Beaumier CM, Park S, Pazoles P, Endy TP, Mathew A, et al. Protective versus pathologic pre-exposure cytokine profiles in dengue virus infection. PLoS Negl Trop Dis. 2018; 12(12):e0006975. https://doi.org/10.1371/journal.pntd.0006975 eCollection 2018 Dec. PMID: 30557313 PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1008607 June 4, 2020 4/5 PLOS PATHOGENS 16. Ruiz-Riol M, Llano A, Ibarrondo J, Zamarreño J, Yusim K, Bach V, et al. Alternative effector-function profiling identifies broad HIV-specific T-cell responses in highly HIV-exposed individuals who remain uninfected. J Infect Dis. 2015; 211(6):936–46. https://doi.org/10.1093/infdis/jiu534 Epub 2014 Sep 23. PMID: 25249264 PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1008607 June 4, 2020 5/5