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Miah SMS, Lelias S, Gutierrez AH, McAllister M, Boyle CM, Moise L, De Groot AS. A SARS-CoV-2 NSP7 homolog of a Treg epitope suppresses CD4+ and CD8+ T cell memory responses. Front Immunol 2023; 14:1290688. [PMID: 38124752 PMCID: PMC10731459 DOI: 10.3389/fimmu.2023.1290688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/03/2023] [Indexed: 12/23/2023] Open
Abstract
Pathogens escape host defenses by T-cell epitope mutation or deletion (immune escape) and by simulating the appearance of human T cell epitopes (immune camouflage). We identified a highly conserved, human-like T cell epitope in non-structural protein 7 (NSP7) of SARS-CoV-2, RNA-dependent RNA polymerase (RdRp) hetero-tetramer complex. Remarkably, this T cell epitope has significant homology to a T regulatory cell epitope (Tregitope) previously identified in the Fc region of human immunoglobulin G (IgG) (Tregitope 289). We hypothesized that the SARS-CoV-2 NSP7 epitope (NSP7-289) may induce suppressive responses by engaging and activating pre-existing regulatory T cells. We therefore compared NSP7-289 and IgG Tregitopes (289 and 289z, a shorter version of 289 that isolates the shared NSP7 epitope) in vitro. Tregitope peptides 289, 289z and NSP7-289 bound to multiple HLA-DRB1 alleles in vitro and suppressed CD4+ and CD8+ T cell memory responses. Identification and in vitro validation of SARS-CoV-2 NSP7-289 provides further evidence of immune camouflage and suggests that pathogens can use human-like epitopes to evade immune response and potentially enhance host tolerance. Further exploration of the role of cross-conserved Tregs in human immune responses to pathogens such as SARS-CoV-2 is warranted.
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Affiliation(s)
| | | | | | | | | | | | - Anne S. De Groot
- EpiVax, Inc., Providence, RI, United States
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, United States
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De Groot AS, Khan S, Mattei AE, Lelias S, Martin WD. Does human homology reduce the potential immunogenicity of non-antibody scaffolds? Front Immunol 2023; 14:1215939. [PMID: 38022550 PMCID: PMC10664710 DOI: 10.3389/fimmu.2023.1215939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Biologics developers are moving beyond antibodies for delivery of a wide range of therapeutic interventions. These non-antibody modalities are often based on 'natural' protein scaffolds that are modified to deliver bioactive sequences. Both human-derived and non-human-sourced scaffold proteins have been developed. New types of "non-antibody" scaffolds are still being discovered, as they offer attractive alternatives to monoclonals due to their smaller size, improved stability, and ease of synthesis. They are believed to have low immunogenic potential. However, while several human-sourced protein scaffolds have not been immunogenic in clinical studies, this may not predict their overall performance in other therapeutic applications. A preliminary evaluation of their potential for immunogenicity is warranted. Immunogenicity risk potential has been clearly linked to the presence of T "helper" epitopes in the sequence of biologic therapeutics. In addition, tolerogenic epitopes are present in some human proteins and may decrease their immunogenic potential. While the detailed sequences of many non-antibody scaffold therapeutic candidates remain unpublished, their backbone sequences are available for review and analysis. We assessed 12 example non-antibody scaffold backbone sequences using our epitope-mapping tools (EpiMatrix) for this perspective. Based on EpiMatrix scoring, their HLA DRB1-restricted T cell epitope content appears to be lower than the average protein, and sequences that may act as tolerogenic epitopes are present in selected human-derived scaffolds. Assessing the potential immunogenicity of scaffold proteins regarding self and non-self T cell epitopes may be of use for drug developers and clinicians, as these exciting new non-antibody molecules begin to emerge from the preclinical pipeline into clinical use.
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Affiliation(s)
- Anne S. De Groot
- EpiVax, Providence, RI, United States
- University of Georgia, Center for Vaccines and Immunology, Athens, GA, United States
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Abella JR, Antunes D, Jackson K, Lizée G, Clementi C, Kavraki LE. Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes. Proc Natl Acad Sci U S A 2020; 117:30610-30618. [PMID: 33184174 PMCID: PMC7720115 DOI: 10.1073/pnas.2007246117] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.
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Affiliation(s)
- Jayvee R Abella
- Department of Computer Science, Rice University, Houston, TX 77005
| | - Dinler Antunes
- Department of Computer Science, Rice University, Houston, TX 77005
| | - Kyle Jackson
- Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Gregory Lizée
- Department of Melanoma Medical Oncology-Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Cecilia Clementi
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, TX 77005;
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De Groot AS, Moise L, Terry F, Gutierrez AH, Hindocha P, Richard G, Hoft DF, Ross TM, Noe AR, Takahashi Y, Kotraiah V, Silk SE, Nielsen CM, Minassian AM, Ashfield R, Ardito M, Draper SJ, Martin WD. Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools. Front Immunol 2020; 11:442. [PMID: 32318055 PMCID: PMC7154102 DOI: 10.3389/fimmu.2020.00442] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 02/26/2020] [Indexed: 12/19/2022] Open
Abstract
Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. “Rapid-Fire” applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.
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Affiliation(s)
- Anne S De Groot
- EpiVax, Inc., Providence, RI, United States.,Institute for Immunology and Informatics, Providence, RI, United States
| | - Leonard Moise
- EpiVax, Inc., Providence, RI, United States.,Institute for Immunology and Informatics, Providence, RI, United States
| | | | - Andres H Gutierrez
- EpiVax, Inc., Providence, RI, United States.,Institute for Immunology and Informatics, Providence, RI, United States
| | | | | | - Daniel Fredric Hoft
- Departments of Molecular Microbiology & Immunology and Internal Medicine, Division of Infectious Diseases, Allergy & Immunology, Saint Louis University, St. Louis, MO, United States
| | - Ted M Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, United States
| | - Amy R Noe
- Leidos Life Sciences, Frederick, MD, United States
| | | | | | - Sarah E Silk
- Jenner Institute, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | - Simon J Draper
- Jenner Institute, University of Oxford, Oxford, United Kingdom
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Thakkar N, Bailey-Kellogg C. Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity. BMC Bioinformatics 2019; 20:241. [PMID: 31092185 PMCID: PMC6521430 DOI: 10.1186/s12859-019-2864-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 04/29/2019] [Indexed: 12/18/2022] Open
Abstract
Background Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition. Results We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies. Conclusion We conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities. Electronic supplementary material The online version of this article (10.1186/s12859-019-2864-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Neerja Thakkar
- Department of Computer Science, Dartmouth, Hanover, NH, USA
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