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Kelly JJ, Bloodworth N, Shao Q, Shabanowitz J, Hunt D, Meiler J, Pires MM. A Chemical Approach to Assess the Impact of Post-translational Modification on MHC Peptide Binding and Effector Cell Engagement. ACS Chem Biol 2024. [PMID: 39150956 DOI: 10.1021/acschembio.4c00312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
The human major histocompatibility complex (MHC) plays a pivotal role in the presentation of peptidic fragments from proteins, which can originate from self-proteins or from nonhuman antigens, such as those produced by viruses or bacteria. To prevent cytotoxicity against healthy cells, thymocytes expressing T cell receptors (TCRs) that recognize self-peptides are removed from circulation (negative selection), thus leaving T cells that recognize nonself-peptides. Current understanding suggests that post-translationally modified (PTM) proteins and the resulting peptide fragments they generate following proteolysis are largely excluded from negative selection; this feature means that PTMs can generate nonself-peptides that potentially contribute to the development of autoreactive T cells and subsequent autoimmune diseases. Although it is well-established that PTMs are prevalent in peptides present on MHCs, the precise mechanisms by which PTMs influence the antigen presentation machinery remain poorly understood. In the present work, we introduce chemical modifications mimicking PTMs on synthetic peptides. This is the first systematic study isolating the impact of PTMs on MHC binding and also their impact on TCR recognition. Our findings reveal various ways PTMs alter antigen presentation, which could have implications for tumor neoantigen presentation.
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Affiliation(s)
- Joey J Kelly
- Department of Chemistry University of Virginia Charlottesville, Virginia 22904, United States
| | - Nathaniel Bloodworth
- Division of Clinical Pharmacology, Department of MedicineVanderbilt University Medical Center, Nashville, Tennessee 37240, United States
| | - Qianqian Shao
- Department of Chemistry University of Virginia Charlottesville, Virginia 22904, United States
| | - Jeffrey Shabanowitz
- Department of Chemistry University of Virginia Charlottesville, Virginia 22904, United States
| | - Donald Hunt
- Department of Chemistry University of Virginia Charlottesville, Virginia 22904, United States
| | - Jens Meiler
- Division of Clinical Pharmacology, Department of MedicineVanderbilt University Medical Center, Nashville, Tennessee 37240, United States
- Institute of Drug Discovery, Faculty of MedicineUniversity of Leipzig, Leipzig, SAC 04103, Germany
- Center for Structural Biology Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Marcos M Pires
- Department of Chemistry University of Virginia Charlottesville, Virginia 22904, United States
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2
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Wilson E, Cava JK, Chowell D, Raja R, Mangalaparthi KK, Pandey A, Curtis M, Anderson KS, Singharoy A. The electrostatic landscape of MHC-peptide binding revealed using inception networks. Cell Syst 2024; 15:362-373.e7. [PMID: 38554709 DOI: 10.1016/j.cels.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/24/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Wilson
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Kevin Cava
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85207, USA
| | - Diego Chowell
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Remya Raja
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Marion Curtis
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA; College of Medicine and Science, Mayo Clinic, Scottsdale, AZ 85259, USA; Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Karen S Anderson
- School of Life Sciences, Arizona State University, Tempe, AZ 85207, USA.
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA.
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3
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Fasoulis R, Rigo MM, Lizée G, Antunes DA, Kavraki LE. APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries. J Chem Inf Model 2024; 64:1730-1750. [PMID: 38415656 PMCID: PMC10936522 DOI: 10.1021/acs.jcim.3c01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/29/2024]
Abstract
The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.
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Affiliation(s)
- Romanos Fasoulis
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Mauricio M. Rigo
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Gregory Lizée
- Department
of Melanoma Medical Oncology—Research, The University of Texas MD Anderson Cancer Center, Houston, Texas 77054, United States
| | - Dinler A. Antunes
- Department
of Biology and Biochemistry, University
of Houston, Houston, Texas 77004, United States
| | - Lydia E. Kavraki
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
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4
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Shortreed NA, Panicker AJ, Mangalaparthi KK, Zhong J, Pandey A, Griffiths LG. Optimization of a high-throughput shotgun immunoproteomics pipeline for antigen identification. J Proteomics 2023; 281:104906. [PMID: 37059220 PMCID: PMC10399726 DOI: 10.1016/j.jprot.2023.104906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/28/2023] [Accepted: 04/11/2023] [Indexed: 04/16/2023]
Abstract
Identification of proteins which initiate and/or perpetuate adaptive immune responses has potential to greatly impact pre-clinical and clinical work across numerous fields. To date, however, the methodologies available to identify antigens responsible for driving adaptive immune responses have been plagued by numerous issues which have drastically limited their widespread adoption. Therefore, in this study, we sought to optimize a shotgun immunoproteomics approach to alleviate these persistent issues and create a high-throughput, quantitative methodology for antigen identification. Three individual components of a previously published approach, namely the protein extraction, antigen elution, and LC-MS/MS analysis steps, were optimized in a systematic manner. These studies determined that preparation of protein extracts using a one-step tissue disruption method in immunoprecipitation (IP) buffer, eluting antigens from affinity chromatography columns with 1% trifluoroacetic acid (TFA), and TMT-labeling & multiplexing equal volumes of eluted samples for LC-MS/MS analysis, resulted in quantitative longitudinal antigen identification, with reduced variability between replicates and increased total number of antigens identified. This optimized pipeline provides a multiplexed, highly reproducible, and fully quantitative approach to antigen identification which is broadly applicable to determine the role of antigenic proteins in inciting (i.e., primary antigens) and perpetuating (i.e., secondary antigens) a wide range of diseases. SIGNIFICANCE: Using a systematic, hypothesis-driven approach, we identified potential improvements for three individual steps of a previously published approach for antigen-identification. Optimization of each step created a methodology which resolved many of the persistent issues associated with previous antigen identification approaches. The optimized high-throughput shotgun immunoproteomics approach described herein identifies more than five times as many unique antigens as the previously published method, greatly reduces protocol cost and mass spectrometry time per experiment, minimizes both inter- and intra-experimental variability, and ensures each experiment is fully quantitative. Ultimately, this optimized antigen identification approach has the potential to facilitate novel antigen identification studies, allowing evaluation of the adaptive immune response in a longitudinal manner and encourage innovations in a wide array of fields.
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Affiliation(s)
- Nicholas A Shortreed
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Immunology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Anjali J Panicker
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Immunology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Jun Zhong
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Leigh G Griffiths
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Physiology & Biomedical Engineering, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
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Bedran G, Polasky DA, Hsiao Y, Yu F, da Veiga Leprevost F, Alfaro JA, Cieslik M, Nesvizhskii AI. Unraveling the glycosylated immunopeptidome with HLA-Glyco. Nat Commun 2023; 14:3461. [PMID: 37308510 PMCID: PMC10258777 DOI: 10.1038/s41467-023-39270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.
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Affiliation(s)
- Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yi Hsiao
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Javier A Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Michigan Center for Translational Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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Phulphagar KM, Ctortecka C, Jacome ASV, Klaeger S, Verzani EK, Hernandez GM, Udeshi ND, Clauser KR, Abelin JG, Carr SA. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry. Mol Cell Proteomics 2023:100563. [PMID: 37142057 DOI: 10.1016/j.mcpro.2023.100563] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.
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