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León AN, Rodriguez AJ, Richey ST, de la Peña AT, Wolters RM, Jackson AM, Webb K, Creech CB, Yoder S, Mudd PA, Crowe JE, Han J, Ward AB. Structural Mapping of Polyclonal IgG Responses to HA After Influenza Virus Vaccination or Infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.601940. [PMID: 39026813 PMCID: PMC11257458 DOI: 10.1101/2024.07.08.601940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Cellular and molecular characterization of immune responses elicited by influenza virus infection and seasonal vaccination have informed efforts to improve vaccine efficacy, breadth, and longevity. Here, we use negative stain electron microscopy polyclonal epitope mapping (nsEMPEM) to structurally characterize the humoral IgG antibody responses to hemagglutinin (HA) from human patients vaccinated with a seasonal quadrivalent flu vaccine or infected with influenza A viruses. Our data show that both vaccinated and infected patients had humoral IgGs targeting highly conserved regions on both H1 and H3 subtype HAs, including the stem and anchor, which are targets for universal influenza vaccine design. Responses against H1 predominantly targeted the central stem epitope in infected patients and vaccinated donors, whereas head epitopes were more prominently targeted on H3. Responses against H3 were less abundant, but a greater diversity of H3 epitopes were targeted relative to H1. While our analysis is limited by sample size, on average, vaccinated donors responded to a greater diversity of epitopes on both H1 and H3 than infected patients. These data establish a baseline for assessing polyclonal antibody responses in vaccination and infection, providing context for future vaccine trials and emphasizing the importance of carefully designing vaccines to boost protective responses towards conserved epitopes.
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Affiliation(s)
- André Nicolás León
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Alesandra J. Rodriguez
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Sara T. Richey
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Alba Torrents de la Peña
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Rachael M. Wolters
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN
- Oregon Health & Science University, Portland, OR
| | - Abigail M. Jackson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Katherine Webb
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN
| | - C. Buddy Creech
- Vanderbilt Vaccine Research Program, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN
| | - Sandra Yoder
- Vanderbilt Vaccine Research Program, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN
| | - Philip A. Mudd
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine in St. Louis, St. Louis, MO
- Center for Vaccines and Immunity to Microbial Pathogens, Washington University School of Medicine in St. Louis, St. Louis, MO
- Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO
| | - James E. Crowe
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, San Diego, CA
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2
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Lin HH, Wang CH, Huang SH, Lin SY, Kato T, Namba K, Hosogi N, Song C, Murata K, Yen CH, Hsu TL, Wong CH, Wu YM, Tu IP, Chang WH. Use of phase plate cryo-EM reveals conformation diversity of therapeutic IgG with 50 kDa Fab fragment resolved below 6 Å. Sci Rep 2024; 14:14079. [PMID: 38890341 PMCID: PMC11189423 DOI: 10.1038/s41598-024-62045-8] [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: 06/28/2023] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
While cryogenic electron microscopy (cryo-EM) is fruitfully used for harvesting high-resolution structures of sizable macromolecules, its application to small or flexible proteins composed of small domains like immunoglobulin (IgG) remain challenging. Here, we applied single particle cryo-EM to Rituximab, a therapeutic IgG mediating anti-tumor toxicity, to explore its solution conformations. We found Rituximab molecules exhibited aggregates in cryo-EM specimens contrary to its solution behavior, and utilized a non-ionic detergent to successfully disperse them as isolated particles amenable to single particle analysis. As the detergent adversely reduced the protein-to-solvent contrast, we employed phase plate contrast to mitigate the impaired protein visibility. Assisted by phase plate imaging, we obtained a canonical three-arm IgG structure with other structures displaying variable arm densities co-existing in solution, affirming high flexibility of arm-connecting linkers. Furthermore, we showed phase plate imaging enables reliable structure determination of Fab to sub-nanometer resolution from ab initio, yielding a characteristic two-lobe structure that could be unambiguously docked with crystal structure. Our findings revealed conformation diversity of IgG and demonstrated phase plate was viable for cryo-EM analysis of small proteins without symmetry. This work helps extend cryo-EM boundaries, providing a valuable imaging and structural analysis framework for macromolecules with similar challenging features.
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Affiliation(s)
- Hsin-Hung Lin
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chun-Hsiung Wang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Academia Sinica Cryo-EM Facility, Academia Sinica, Taipei, Taiwan
| | - Shih-Hsin Huang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Institute of Preventive Medicine, National Defense Medical Center, New Taipei City, Taiwan
| | - Sung-Yao Lin
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Takayuki Kato
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka, Japan
- Institute of Protein Research, Osaka University, Suita, Osaka, Japan
| | - Keiichi Namba
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka, Japan
| | - Naoki Hosogi
- JEOL Ltd., 1-2 Musashino 3-chome, Akishima, Tokyo, Japan
| | - Chihong Song
- Exploratory Research Center on Life and Living Systems (ExCELLS) and National Institute for Physiological Sciences (NIPS), National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki, Aichi, Japan
| | - Kazuyoshi Murata
- Exploratory Research Center on Life and Living Systems (ExCELLS) and National Institute for Physiological Sciences (NIPS), National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki, Aichi, Japan
| | | | - Tsui-Ling Hsu
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Chi-Huey Wong
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Yi-Min Wu
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Cryo-EM Facility, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - I-Ping Tu
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Wei-Hau Chang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
- Genomic Research Center, Academia Sinica, Taipei, Taiwan.
- Institute of Physics, Academia Sinica, Taipei, Taiwan.
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3
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Jiang J, Natarajan K, Margulies DH. Chaperone-mediated MHC-I peptide exchange in antigen presentation. IUCRJ 2024; 11:287-298. [PMID: 38656309 PMCID: PMC11067752 DOI: 10.1107/s2052252524002768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/26/2024] [Indexed: 04/26/2024]
Abstract
This work focuses on molecules that are encoded by the major histocompatibility complex (MHC) and that bind self-, foreign- or tumor-derived peptides and display these at the cell surface for recognition by receptors on T lymphocytes (T cell receptors, TCR) and natural killer (NK) cells. The past few decades have accumulated a vast knowledge base of the structures of MHC molecules and the complexes of MHC/TCR with specificity for many different peptides. In recent years, the structures of MHC-I molecules complexed with chaperones that assist in peptide loading have been revealed by X-ray crystallography and cryogenic electron microscopy. These structures have been further studied using mutagenesis, molecular dynamics and NMR approaches. This review summarizes the current structures and dynamic principles that govern peptide exchange as these relate to the process of antigen presentation.
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Affiliation(s)
- Jiansheng Jiang
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA
| | - Kannan Natarajan
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA
| | - David H. Margulies
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA
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Angelo M, Zhang W, Vilseck JZ, Aoki ST. In silico λ-dynamics predicts protein binding specificities to modified RNAs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577511. [PMID: 38328125 PMCID: PMC10849657 DOI: 10.1101/2024.01.26.577511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
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Affiliation(s)
- Murphy Angelo
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Wen Zhang
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
| | - Jonah Z. Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Scott T. Aoki
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA
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5
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Huang R, Warner Jenkins G, Kim Y, Stanfield RL, Singh A, Martinez-Yamout M, Kroon GJ, Torres JL, Jackson AM, Kelley A, Shaabani N, Zeng B, Bacica M, Chen W, Warner C, Radoicic J, Joh J, Dinali Perera K, Sang H, Kim T, Yao J, Zhao F, Sok D, Burton DR, Allen J, Harriman W, Mwangi W, Chung D, Teijaro JR, Ward AB, Dyson HJ, Wright PE, Wilson IA, Chang KO, McGregor D, Smider VV. The smallest functional antibody fragment: Ultralong CDR H3 antibody knob regions potently neutralize SARS-CoV-2. Proc Natl Acad Sci U S A 2023; 120:e2303455120. [PMID: 37722054 PMCID: PMC10523490 DOI: 10.1073/pnas.2303455120] [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: 03/06/2023] [Accepted: 07/15/2023] [Indexed: 09/20/2023] Open
Abstract
Cows produce antibodies with a disulfide-bonded antigen-binding domain embedded within ultralong heavy chain third complementarity determining regions. This "knob" domain is analogous to natural cysteine-rich peptides such as knottins in that it is small and stable but can accommodate diverse loops and disulfide bonding patterns. We immunized cattle with SARS-CoV-2 spike and found ultralong CDR H3 antibodies that could neutralize several viral variants at picomolar IC50 potencies in vitro and could protect from disease in vivo. The independent CDR H3 peptide knobs were expressed and maintained the properties of the parent antibodies. The knob interaction with SARS-CoV-2 spike was revealed by electron microscopy, X-ray crystallography, NMR spectroscopy, and mass spectrometry and established ultralong CDR H3-derived knobs as the smallest known recombinant independent antigen-binding fragment. Unlike other vertebrate antibody fragments, these knobs are not reliant on the immunoglobulin domain and have potential as a new class of therapeutics.
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Affiliation(s)
- Ruiqi Huang
- Applied Biomedical Science Institute, San Diego, CA92127
| | | | - Yunjeong Kim
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Robyn L. Stanfield
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Amrinder Singh
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Maria Martinez-Yamout
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Gerard J. Kroon
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Jonathan L. Torres
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Abigail M. Jackson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Abigail Kelley
- Applied Biomedical Science Institute, San Diego, CA92127
| | - Namir Shaabani
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA92037
| | | | | | - Wen Chen
- Ligand Pharmaceuticals, San Diego, CA92121
| | | | | | - Joongho Joh
- School of Medicine, Department of Medicine, University of Louisville, Louisville, KY40202
| | - Krishani Dinali Perera
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Huldah Sang
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Tae Kim
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Jianxiu Yao
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Fangzhu Zhao
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA92037
| | - Devin Sok
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA92037
| | - Dennis R. Burton
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA92037
| | - Jeff Allen
- Ligand Pharmaceuticals, San Diego, CA92121
| | | | - Waithaka Mwangi
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | - Donghoon Chung
- School of Medicine, Department of Microbiology and Immunology, University of Louisville, Louisville, KY40202
| | - John R. Teijaro
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA92037
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - H. Jane Dyson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Peter E. Wright
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Ian A. Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA92037
| | - Kyeong-Ok Chang
- College of Veterinary Medicine, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS66506
| | | | - Vaughn V. Smider
- Applied Biomedical Science Institute, San Diego, CA92127
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA92037
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6
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Martina CE, Crowe JE, Meiler J. Glycan masking in vaccine design: Targets, immunogens and applications. Front Immunol 2023; 14:1126034. [PMID: 37033915 PMCID: PMC10076883 DOI: 10.3389/fimmu.2023.1126034] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Glycan masking is a novel technique in reverse vaccinology in which sugar chains (glycans) are added on the surface of immunogen candidates to hide regions of low interest and thus focus the immune system on highly therapeutic epitopes. This shielding strategy is inspired by viruses such as influenza and HIV, which are able to escape the immune system by incorporating additional glycosylation and preventing the binding of therapeutic antibodies. Interestingly, the glycan masking technique is mainly used in vaccine design to fight the same viruses that naturally use glycans to evade the immune system. In this review we report the major successes obtained with the glycan masking technique in epitope-focused vaccine design. We focus on the choice of the target antigen, the strategy for immunogen design and the relevance of the carrier vector to induce a strong immune response. Moreover, we will elucidate the different applications that can be accomplished with glycan masking, such as shifting the immune response from hyper-variable epitopes to more conserved ones, focusing the response on known therapeutic epitopes, broadening the response to different viral strains/sub-types and altering the antigen immunogenicity to elicit higher or lower immune response, as desired.
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Affiliation(s)
- Cristina E. Martina
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | - James E. Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
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7
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res 2023; 51:D488-D508. [PMID: 36420884 PMCID: PMC9825554 DOI: 10.1093/nar/gkac1077] [Citation(s) in RCA: 155] [Impact Index Per Article: 155.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Paul A Craig
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Gregg V Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ben Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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8
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Facile repurposing of peptide-MHC-restricted antibodies for cancer immunotherapy. Nat Biotechnol 2023:10.1038/s41587-022-01567-w. [PMID: 36593402 DOI: 10.1038/s41587-022-01567-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 10/13/2022] [Indexed: 01/03/2023]
Abstract
Monoclonal antibodies (Abs) that recognize major histocompatability complex (MHC)-presented tumor antigens in a manner similar to T cell receptors (TCRs) have great potential as cancer immunotherapeutics. However, isolation of 'TCR-mimic' (TCRm) Abs is laborious because Abs have not evolved the structurally nuanced peptide-MHC restriction of αβ-TCRs. Here, we present a strategy for rapid isolation of highly peptide-specific and 'MHC-restricted' Abs by re-engineering preselected Abs that engage peptide-MHC in a manner structurally similar to that of conventional αβ-TCRs. We created structure-based libraries focused on the peptide-interacting residues of TCRm Ab complementarity-determining region (CDR) loops, and rapidly generated MHC-restricted Abs to both mouse and human tumor antigens that specifically killed target cells when formatted as IgG, bispecific T cell engager (BiTE) and chimeric antigen receptor-T (CAR-T). Crystallographic analysis of one selected pMHC-restricted Ab revealed highly peptide-specific recognition, validating the engineering strategy. This approach can yield tumor antigen-specific antibodies in several weeks, potentially enabling rapid clinical translation.
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9
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Pedroza-Escobar D, Castillo-Maldonado I, González-Cortés T, Delgadillo-Guzmán D, Ruíz-Flores P, Cruz JHS, Espino-Silva PK, Flores-Loyola E, Ramirez-Moreno A, Avalos-Soto J, Téllez-López MÁ, Velázquez-Gauna SE, García-Garza R, Vertti RDAP, Torres-León C. Molecular Bases of Protein Antigenicity and Determinants of Immunogenicity, Anergy, and Mitogenicity. Protein Pept Lett 2023; 30:719-733. [PMID: 37691216 DOI: 10.2174/0929866530666230907093339] [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: 03/29/2023] [Revised: 08/03/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND The immune system is able to recognize substances that originate from inside or outside the body and are potentially harmful. Foreign substances that bind to immune system components exhibit antigenicity and are defined as antigens. The antigens exhibiting immunogenicity can induce innate or adaptive immune responses and give rise to humoral or cell-mediated immunity. The antigens exhibiting mitogenicity can cross-link cell membrane receptors on B and T lymphocytes leading to cell proliferation. All antigens vary greatly in physicochemical features such as biochemical nature, structural complexity, molecular size, foreignness, solubility, and so on. OBJECTIVE Thus, this review aims to describe the molecular bases of protein-antigenicity and those molecular bases that lead to an immune response, lymphocyte proliferation, or unresponsiveness. CONCLUSION The epitopes of an antigen are located in surface areas; they are about 880-3,300 Da in size. They are protein, carbohydrate, or lipid in nature. Soluble antigens are smaller than 1 nm and are endocytosed less efficiently than particulate antigens. The more the structural complexity of an antigen increases, the more the antigenicity increases due to the number and variety of epitopes. The smallest immunogens are about 4,000-10,000 Da in size. The more phylogenetically distant immunogens are from the immunogen-recipient, the more immunogenicity increases. Antigens that are immunogens can trigger an innate or adaptive immune response. The innate response is induced by antigens that are pathogen-associated molecular patterns. Exogenous antigens, T Dependent or T Independent, induce humoral immunogenicity. TD protein-antigens require two epitopes, one sequential and one conformational to induce antibodies, whereas, TI non-protein-antigens require only one conformational epitope to induce low-affinity antibodies. Endogenous protein antigens require only one sequential epitope to induce cell-mediated immunogenicity.
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Affiliation(s)
- David Pedroza-Escobar
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Irais Castillo-Maldonado
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Tania González-Cortés
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Dealmy Delgadillo-Guzmán
- Facultad de Medicina, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Pablo Ruíz-Flores
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Jorge Haro Santa Cruz
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Perla-Karina Espino-Silva
- Centro de Investigacion Biomedica, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | - Erika Flores-Loyola
- Facultad de Ciencias Biologicas, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27276, Mexico
| | - Agustina Ramirez-Moreno
- Facultad de Ciencias Biologicas, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27276, Mexico
| | - Joaquín Avalos-Soto
- Cuerpo Academico Farmacia y Productos Naturales, Facultad de Ciencias Quimicas, Universidad Juarez del Estado de Durango, Gomez Palacio, Mexico
| | - Miguel-Ángel Téllez-López
- Cuerpo Academico Farmacia y Productos Naturales, Facultad de Ciencias Quimicas, Universidad Juarez del Estado de Durango, Gomez Palacio, Mexico
| | | | - Rubén García-Garza
- Facultad de Medicina, Universidad Autonoma de Coahuila, Unidad Torreon, Torreon, Coahuila, 27000, Mexico
| | | | - Cristian Torres-León
- Centro de Investigacion y Jardin Etnobiologico, Universidad Autonoma de Coahuila, Viesca, Coahuila, 27480, Mexico
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10
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Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Sali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young JY, Zardecki C. Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students. Biomolecules 2022; 12:1425. [PMID: 36291635 PMCID: PMC9599165 DOI: 10.3390/biom12101425] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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11
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Halabi S, Kaufman J. New vistas unfold: Chicken MHC molecules reveal unexpected ways to present peptides to the immune system. Front Immunol 2022; 13:886672. [PMID: 35967451 PMCID: PMC9372762 DOI: 10.3389/fimmu.2022.886672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/07/2022] [Indexed: 11/27/2022] Open
Abstract
The functions of a wide variety of molecules with structures similar to the classical class I and class II molecules encoded by the major histocompatibility complex (MHC) have been studied by biochemical and structural studies over decades, with many aspects for humans and mice now enshrined in textbooks as dogma. However, there is much variation of the MHC and MHC molecules among the other jawed vertebrates, understood in the most detail for the domestic chicken. Among the many unexpected features in chickens is the co-evolution between polymorphic TAP and tapasin genes with a dominantly-expressed class I gene based on a different genomic arrangement compared to typical mammals. Another important discovery was the hierarchy of class I alleles for a suite of properties including size of peptide repertoire, stability and cell surface expression level, which is also found in humans although not as extreme, and which led to the concept of generalists and specialists in response to infectious pathogens. Structural studies of chicken class I molecules have provided molecular explanations for the differences in peptide binding compared to typical mammals. These unexpected phenomena include the stringent binding with three anchor residues and acidic residues at the peptide C-terminus for fastidious alleles, and the remodelling binding sites, relaxed binding of anchor residues in broad hydrophobic pockets and extension at the peptide C-terminus for promiscuous alleles. The first few studies for chicken class II molecules have already uncovered unanticipated structural features, including an allele that binds peptides by a decamer core. It seems likely that the understanding of how MHC molecules bind and present peptides to lymphocytes will broaden considerably with further unexpected discoveries through biochemical and structural studies for chickens and other non-mammalian vertebrates.
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Affiliation(s)
- Samer Halabi
- Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Jim Kaufman
- Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
- *Correspondence: Jim Kaufman,
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12
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The Interaction of Anti-DNA Antibodies with DNA: Evidence for Unconventional Binding Mechanisms. Int J Mol Sci 2022; 23:ijms23095227. [PMID: 35563617 PMCID: PMC9105193 DOI: 10.3390/ijms23095227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022] Open
Abstract
Antibodies to DNA (anti-DNA) are the serological hallmark of systemic lupus erythematosus, a prototypic autoimmune disease. These antibodies bind to conserved sites on single-stranded and double-stranded DNA and display variable region somatic mutations consistent with antigen selection. Nevertheless, the interaction of anti-DNA with DNA has unconventional features. Anti-DNA antibodies bind by a mechanism called monogamous bivalency, in which stable interaction requires contact of both Fab sites with determinants on the same extended DNA molecule; the size of this DNA can be hundreds to thousands of bases, especially in solid phase assays. This binding also requires the presence of the Fc portion of IgG, a binding mechanism known as Fc-dependent monogamous bivalency. As shown by the effects of ionic strength in association and dissociation assays, anti-DNA binding is primarily electrostatic. Like anti-DNA autoantibodies, anti-DNA antibodies that bind specifically to non-conserved sites on bacterial DNA, a type of anti-DNA found in otherwise healthy individuals, also interact by monogamous bivalency. The unconventional features of anti-DNA antibodies may reflect the highly charged and polymeric nature of DNA and the need for molecular rearrangements to facilitate monogamous bivalency; the Fc portion contributes to binding in an as yet unknown way.
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13
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Pathogenesis of IgA Nephropathy: Current Understanding and Implications for Development of Disease-Specific Treatment. J Clin Med 2021; 10:jcm10194501. [PMID: 34640530 PMCID: PMC8509647 DOI: 10.3390/jcm10194501] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 09/22/2021] [Indexed: 12/20/2022] Open
Abstract
IgA nephropathy, initially described in 1968 as a kidney disease with glomerular “intercapillary deposits of IgA-IgG”, has no disease-specific treatment and is a common cause of kidney failure. Clinical observations and laboratory analyses suggest that IgA nephropathy is an autoimmune disease wherein the kidneys are damaged as innocent bystanders due to deposition of IgA1-IgG immune complexes from the circulation. A multi-hit hypothesis for the pathogenesis of IgA nephropathy describes four sequential steps in disease development. Specifically, patients with IgA nephropathy have elevated circulating levels of IgA1 with some O-glycans deficient in galactose (galactose-deficient IgA1) and these IgA1 glycoforms are recognized as autoantigens by unique IgG autoantibodies, resulting in formation of circulating immune complexes, some of which deposit in glomeruli and activate mesangial cells to induce kidney injury. This proposed mechanism is supported by observations that (i) glomerular immunodeposits in patients with IgA nephropathy are enriched for galactose-deficient IgA1 glycoforms and the corresponding IgG autoantibodies; (ii) circulatory levels of galactose-deficient IgA1 and IgG autoantibodies predict disease progression; and (iii) pathogenic potential of galactose-deficient IgA1 and IgG autoantibodies was demonstrated in vivo. Thus, a better understanding of the structure–function of these immunoglobulins as autoantibodies and autoantigens will enable development of disease-specific treatments.
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14
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Gierasch LM, Berman HM. How the Protein Data Bank changed biology: An introduction to the JBC Reviews thematic series, part 2. J Biol Chem 2021; 296:100748. [PMID: 33957128 PMCID: PMC8166930 DOI: 10.1016/j.jbc.2021.100748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In part 1 of this remarkable collection, we told you the story of The Protein Data Bank (PDB) (1), which was founded 50 years ago, and we illustrated the breadth of the science contained within it with ten informative review articles. The second half of this collection is a continuation of our celebrations to mark this momentous anniversary. Part 2 provides eight more superb articles describing how the PDB has influenced biology over the course of the last half-century and how biology has fueled the deposition of impactful structures in the PDB. Here are some brief synopses of the articles you will enjoy in part 2!
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Affiliation(s)
- Lila M Gierasch
- Departments of Biochemistry & Molecular Biology and Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, USA.
| | - Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, California, USA.
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15
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Ciuman RR. Understanding Human Body Maintenance, Protection, and Modification: Antibodies, Genetics, Stem Cells and Connected Artificial Intelligence Applications—Where Are We? Health (London) 2021. [DOI: 10.4236/health.2021.137059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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