1
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Bhattacharya T, Alleman EM, Noyola AC, Emerman M, Malik HS. A conserved opal termination codon optimizes a temperature-dependent tradeoff between protein production and processing in alphaviruses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609082. [PMID: 39229031 PMCID: PMC11370586 DOI: 10.1101/2024.08.21.609082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Alphaviruses are enveloped, single-stranded, positive-sense RNA viruses that often require transmission between arthropod and vertebrate hosts for their sustained propagation. Most alphaviruses encode an opal (UGA) termination codon in nonstructural protein 3 (nsP3) upstream of the viral polymerase, nsP4. The selective constraints underlying the conservation of the opal codon are poorly understood. Using primate and mosquito cells, we explored the role and selective pressure on the nsP3 opal codon through extensive mutational analysis in the prototype alphavirus, Sindbis virus (SINV). We found that the opal codon is highly favored over all other codons in primate cells under native 37ºC growth conditions. However, this preference is diminished in mosquito and primate cells grown at a lower temperature. Thus, the primary determinant driving the selection of the opal stop codon is not host genetics but the passaging temperature. We show that the opal codon is preferred over amber and ochre termination codons because it results in the highest translational readthrough and polymerase production. However, substituting the opal codon with sense codons leads to excessive full-length polyprotein (P1234) production, which disrupts optimal nsP polyprotein processing, delays the switch from minus-strand to positive-strand RNA production, and significantly reduces SINV fitness at 37°C; this fitness defect is relieved at lower temperatures. A naturally occurring suppressor mutation unexpectedly compensates for a delayed transition from minus to genomic RNA production by also delaying the subsequent transition between genomic and sub-genomic RNA production. Our study reveals that the opal stop codon is the best solution for alphavirus replication at 37ºC, producing enough nsP4 protein to maximize replication without disrupting nsP processing and RNA replication transitions needed for optimal fitness. Our study uncovers the intricate strategy dual-host alphaviruses use at a single codon to optimize fitness.
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2
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Hong Z, Shimagaki KS, Barton JP. popDMS infers mutation effects from deep mutational scanning data. Bioinformatics 2024; 40:btae499. [PMID: 39115383 PMCID: PMC11335369 DOI: 10.1093/bioinformatics/btae499] [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: 02/19/2024] [Revised: 07/10/2024] [Accepted: 08/06/2024] [Indexed: 08/22/2024] Open
Abstract
SUMMARY Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions. AVAILABILITY AND IMPLEMENTATION popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.
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Affiliation(s)
- Zhenchen Hong
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, United States
| | - Kai S Shimagaki
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, PA 15260, United States
| | - John P Barton
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, United States
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, PA 15260, United States
- Department of Physics and Astronomy, University of Pittsburgh, PA 15260, United States
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3
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Álvarez-Rodríguez B, Velandia-Álvarez S, Toft C, Geller R. Mapping mutational fitness effects across the coxsackievirus B3 proteome reveals distinct profiles of mutation tolerability. PLoS Biol 2024; 22:e3002709. [PMID: 39012844 PMCID: PMC11251597 DOI: 10.1371/journal.pbio.3002709] [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: 04/03/2024] [Accepted: 06/13/2024] [Indexed: 07/18/2024] Open
Abstract
RNA viruses have notoriously high mutation rates due to error-prone replication by their RNA polymerase. However, natural selection concentrates variability in a few key viral proteins. To test whether this stems from different mutation tolerance profiles among viral proteins, we measured the effect of >40,000 non-synonymous mutations across the full proteome of coxsackievirus B3 as well as >97% of all possible codon deletions in the nonstructural proteins. We find significant variation in mutational tolerance within and between individual viral proteins, which correlated with both general and protein-specific structural and functional attributes. Furthermore, mutational fitness effects remained stable across cell lines, suggesting selection pressures are mostly conserved across environments. In addition to providing a rich dataset for understanding virus biology and evolution, our results illustrate that incorporation of mutational tolerance data into druggable pocket discovery can aid in selecting targets with high barriers to drug resistance.
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Affiliation(s)
| | | | - Christina Toft
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Valencia, Spain
| | - Ron Geller
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Valencia, Spain
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4
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Liu T, Reiser WK, Tan TJC, Lv H, Rivera-Cardona J, Heimburger K, Wu NC, Brooke CB. Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin. Virus Evol 2024; 10:veae046. [PMID: 38915760 PMCID: PMC11196192 DOI: 10.1093/ve/veae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/30/2024] [Accepted: 06/12/2024] [Indexed: 06/26/2024] Open
Abstract
The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor-binding avidity of HA and receptor-cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor-binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.
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Affiliation(s)
- Tongyu Liu
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - William K Reiser
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Timothy J C Tan
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huibin Lv
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Joel Rivera-Cardona
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Kyle Heimburger
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas C Wu
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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5
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Yu Y, Kass MA, Zhang M, Youssef N, Freije CA, Brock KP, Aguado LC, Seifert LL, Venkittu S, Hong X, Shlomai A, de Jong YP, Marks DS, Rice CM, Schneider WM. Deep mutational scanning of hepatitis B virus reveals a mechanism for cis-preferential reverse transcription. Cell 2024; 187:2735-2745.e12. [PMID: 38723628 PMCID: PMC11127778 DOI: 10.1016/j.cell.2024.04.008] [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: 08/02/2023] [Revised: 02/12/2024] [Accepted: 04/10/2024] [Indexed: 05/22/2024]
Abstract
Hepatitis B virus (HBV) is a small double-stranded DNA virus that chronically infects 296 million people. Over half of its compact genome encodes proteins in two overlapping reading frames, and during evolution, multiple selective pressures can act on shared nucleotides. This study combines an RNA-based HBV cell culture system with deep mutational scanning (DMS) to uncouple cis- and trans-acting sequence requirements in the HBV genome. The results support a leaky ribosome scanning model for polymerase translation, provide a fitness map of the HBV polymerase at single-nucleotide resolution, and identify conserved prolines adjacent to the HBV polymerase termination codon that stall ribosomes. Further experiments indicated that stalled ribosomes tether the nascent polymerase to its template RNA, ensuring cis-preferential RNA packaging and reverse transcription of the HBV genome.
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Affiliation(s)
- Yingpu Yu
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Maximilian A Kass
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA; Department of Infectious Diseases, Molecular Virology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany
| | - Mengyin Zhang
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Noor Youssef
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Organismic and Evolutionary Biology, Broad Institute of MIT and Harvard, Harvard University, Cambridge, MA 02138, USA
| | - Catherine A Freije
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Organismic and Evolutionary Biology, Broad Institute of MIT and Harvard, Harvard University, Cambridge, MA 02138, USA
| | - Lauren C Aguado
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Leon L Seifert
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA; Center for Clinical and Translational Science, The Rockefeller University, New York, NY 10065, USA
| | - Sanjana Venkittu
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Xupeng Hong
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Amir Shlomai
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA
| | - Ype P de Jong
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA; Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Organismic and Evolutionary Biology, Broad Institute of MIT and Harvard, Harvard University, Cambridge, MA 02138, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA.
| | - William M Schneider
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY 10065, USA.
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6
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Claussnitzer M, Parikh VN, Wagner AH, Arbesfeld JA, Bult CJ, Firth HV, Muffley LA, Nguyen Ba AN, Riehle K, Roth FP, Tabet D, Bolognesi B, Glazer AM, Rubin AF. Minimum information and guidelines for reporting a multiplexed assay of variant effect. Genome Biol 2024; 25:100. [PMID: 38641812 PMCID: PMC11027375 DOI: 10.1186/s13059-024-03223-9] [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: 06/18/2023] [Accepted: 03/25/2024] [Indexed: 04/21/2024] Open
Abstract
Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
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Affiliation(s)
- Melina Claussnitzer
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, 02142, USA
| | - Victoria N Parikh
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA
| | - Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Dept of Medical Genetics, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Lara A Muffley
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Daniel Tabet
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Benedetta Bolognesi
- Institute for Bioengineering of Catalunya (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Andrew M Glazer
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
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7
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Liu T, Reiser WK, Tan TJC, Lv H, Rivera-Cardona J, Heimburger K, Wu NC, Brooke CB. Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585603. [PMID: 38562808 PMCID: PMC10983940 DOI: 10.1101/2024.03.18.585603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor binding avidity of HA and receptor cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.
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8
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Hong Z, Barton JP. popDMS infers mutation effects from deep mutational scanning data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577759. [PMID: 38352383 PMCID: PMC10862717 DOI: 10.1101/2024.01.29.577759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.
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Affiliation(s)
- Zhenchen Hong
- Department of Physics and Astronomy, University of California, Riverside, USA
| | - John P. Barton
- Department of Physics and Astronomy, University of California, Riverside, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, USA
- Department of Physics and Astronomy, University of Pittsburgh, USA
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9
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Kikawa C, Cartwright-Acar CH, Stuart JB, Contreras M, Levoir LM, Evans MJ, Bloom JD, Goo L. The effect of single mutations in Zika virus envelope on escape from broadly neutralizing antibodies. J Virol 2023; 97:e0141423. [PMID: 37943046 PMCID: PMC10688354 DOI: 10.1128/jvi.01414-23] [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: 09/11/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023] Open
Abstract
IMPORTANCE The wide endemic range of mosquito-vectored flaviviruses-such as Zika virus and dengue virus serotypes 1-4-places hundreds of millions of people at risk of infection every year. Despite this, there are no widely available vaccines, and treatment of severe cases is limited to supportive care. An avenue toward development of more widely applicable vaccines and targeted therapies is the characterization of monoclonal antibodies that broadly neutralize all these viruses. Here, we measure how single amino acid mutations in viral envelope protein affect neutralizing antibodies with both broad and narrow specificities. We find that broadly neutralizing antibodies with potential as vaccine prototypes or biological therapeutics are quantifiably more difficult to escape than narrow, virus-specific neutralizing antibodies.
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Affiliation(s)
- Caroline Kikawa
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Medical Scientist Training Program, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, USA
| | | | - Jackson B. Stuart
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, USA
| | - Maya Contreras
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, USA
| | - Lisa M. Levoir
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, USA
| | - Matthew J. Evans
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jesse D. Bloom
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Basic Sciences, Fred Hutch Cancer Center, Seattle, Washington, USA
- Computational Biology, Fred Hutch Cancer Center, Seattle, Washington, USA
- Howard Hughes Medical Institute, Seattle, Washington, USA
| | - Leslie Goo
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, USA
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10
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Li Y, Arcos S, Sabsay KR, te Velthuis AJW, Lauring AS. Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein. J Virol 2023; 97:e0132923. [PMID: 37882522 PMCID: PMC10688322 DOI: 10.1128/jvi.01329-23] [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: 08/28/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
IMPORTANCE The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus' natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.
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Affiliation(s)
- Yuan Li
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sarah Arcos
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kimberly R. Sabsay
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | | | - Adam S. Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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11
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Maes S, Deploey N, Peelman F, Eyckerman S. Deep mutational scanning of proteins in mammalian cells. CELL REPORTS METHODS 2023; 3:100641. [PMID: 37963462 PMCID: PMC10694495 DOI: 10.1016/j.crmeth.2023.100641] [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: 05/12/2023] [Revised: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.
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Affiliation(s)
- Stefanie Maes
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Nick Deploey
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Frank Peelman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Eyckerman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium.
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12
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Zhang Y, Cui P, Shi J, Chen Y, Zeng X, Jiang Y, Tian G, Li C, Chen H, Kong H, Deng G. Key Amino Acid Residues That Determine the Antigenic Properties of Highly Pathogenic H5 Influenza Viruses Bearing the Clade 2.3.4.4 Hemagglutinin Gene. Viruses 2023; 15:2249. [PMID: 38005926 PMCID: PMC10674173 DOI: 10.3390/v15112249] [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: 10/21/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
The H5 subtype highly pathogenic avian influenza viruses bearing the clade 2.3.4.4 HA gene have been pervasive among domestic poultry and wild birds worldwide since 2014, presenting substantial risks to human and animal health. Continued circulation of clade 2.3.4.4 viruses has resulted in the emergence of eight subclades (2.3.4.4a-h) and multiple distinct antigenic groups. However, the key antigenic substitutions responsible for the antigenic change of these viruses remain unknown. In this study, we analyzed the HA gene sequences of 5713 clade 2.3.4.4 viruses obtained from a public database and found that 23 amino acid residues were highly variable among these strains. We then generated a series of single-amino-acid mutants based on the H5-Re8 (a vaccine seed virus) background and tested their reactivity with a panel of eight monoclonal antibodies (mAbs). Six mutants bearing amino acid substitutions at positions 120, 126, 141, 156, 185, or 189 (H5 numbering) led to reduced or lost reactivity to these mAbs. Further antigenic cartography analysis revealed that the amino acid residues at positions 126, 156, and 189 acted as immunodominant epitopes of H5 viruses. Collectively, our findings offer valuable guidance for the surveillance and early detection of emerging antigenic variants.
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Affiliation(s)
- Yuancheng Zhang
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Pengfei Cui
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Jianzhong Shi
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
| | - Yuan Chen
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Xianying Zeng
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Yongping Jiang
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Guobin Tian
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Chengjun Li
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Hualan Chen
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
| | - Huihui Kong
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
| | - Guohua Deng
- State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150009, China; (Y.Z.); (P.C.); (J.S.); (Y.C.); (X.Z.); (Y.J.); (G.T.); (C.L.); (H.C.)
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13
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Schommers P, Kim DS, Schlotz M, Kreer C, Eggeling R, Hake A, Stecher M, Park J, Radford CE, Dingens AS, Ercanoglu MS, Gruell H, Odidika S, Dahlhaus M, Gieselmann L, Ahmadov E, Lawong RY, Heger E, Knops E, Wyen C, Kümmerle T, Römer K, Scholten S, Wolf T, Stephan C, Suárez I, Raju N, Adhikari A, Esser S, Streeck H, Duerr R, Nanfack AJ, Zolla-Pazner S, Geldmacher C, Geisenberger O, Kroidl A, William W, Maganga L, Ntinginya NE, Georgiev IS, Vehreschild JJ, Hoelscher M, Fätkenheuer G, Lavinder JJ, Bloom JD, Seaman MS, Lehmann C, Pfeifer N, Georgiou G, Klein F. Dynamics and durability of HIV-1 neutralization are determined by viral replication. Nat Med 2023; 29:2763-2774. [PMID: 37957379 PMCID: PMC10667105 DOI: 10.1038/s41591-023-02582-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/07/2023] [Indexed: 11/15/2023]
Abstract
Human immunodeficiency virus type 1 (HIV-1)-neutralizing antibodies (nAbs) that prevent infection are the main goal of HIV vaccine discovery. But as no nAb-eliciting vaccines are yet available, only data from HIV-1 neutralizers-persons with HIV-1 who naturally develop broad and potent nAbs-can inform about the dynamics and durability of nAb responses in humans, knowledge which is crucial for the design of future HIV-1 vaccine regimens. To address this, we assessed HIV-1-neutralizing immunoglobulin G (IgG) from 2,354 persons with HIV-1 on or off antiretroviral therapy (ART). Infection with non-clade B viruses, CD4+ T cell counts <200 µl-1, being off ART and a longer time off ART were independent predictors of a more potent and broad neutralization. In longitudinal analyses, we found nAb half-lives of 9.3 and 16.9 years in individuals with no- or low-level viremia, respectively, and 4.0 years in persons who newly initiated ART. Finally, in a potent HIV-1 neutralizer, we identified lower fractions of serum nAbs and of nAb-encoding memory B cells after ART initiation, suggesting that a decreasing neutralizing serum activity after antigen withdrawal is due to lower levels of nAbs. These results collectively show that HIV-1-neutralizing responses can persist for several years, even at low antigen levels, suggesting that an HIV-1 vaccine may elicit a durable nAb response.
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Affiliation(s)
- Philipp Schommers
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Dae Sung Kim
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Maike Schlotz
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christoph Kreer
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Anna Hake
- Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken, Germany
- Saarland Informatics Campus, Saarbrücken, Germany
| | - Melanie Stecher
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Juyeon Park
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Caelan E Radford
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Meryem S Ercanoglu
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Henning Gruell
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Stanley Odidika
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
| | - Marten Dahlhaus
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
| | - Lutz Gieselmann
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Elvin Ahmadov
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rene Y Lawong
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Eva Heger
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Elena Knops
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christoph Wyen
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Praxis am Ebertplatz, Cologne, Germany
| | | | - Katja Römer
- Gemeinschaftspraxis Gotenring, Cologne, Germany
| | | | - Timo Wolf
- Infectious Diseases Division, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Christoph Stephan
- Infectious Diseases Division, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Isabelle Suárez
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Nagarajan Raju
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anurag Adhikari
- Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences, Lalitpur, Nepal
| | - Stefan Esser
- Department of Dermatology, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Hendrik Streeck
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
- Institute of Virology, Medical Faculty, University Bonn, Bonn, Germany
| | - Ralf Duerr
- Department of Microbiology, New York University School of Medicine, New York City, NY, USA
- Department of Medicine, NYU Grossman School of Medicine, New York City, NY, USA
- Vaccine Center, NYU Grossman School of Medicine, New York City, NY, USA
| | - Aubin J Nanfack
- Medical Diagnostic Center, Yaoundé, Cameroon
- Chantal Biya International Reference Centre for Research on HIV/AIDS Prevention and Management (CIRCB), Yaoundé, Cameroon
| | - Susan Zolla-Pazner
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Microbiology, Icahn School of Medicine, New York City, NY, USA
| | - Christof Geldmacher
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology, Infection and Pandemic Research, Munich, Germany
| | - Otto Geisenberger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Arne Kroidl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Wiston William
- Mbeya Medical Research Centre, National Institute for Medical Research, Mbeya, Tanzania
| | - Lucas Maganga
- Mbeya Medical Research Centre, National Institute for Medical Research, Mbeya, Tanzania
| | | | - Ivelin S Georgiev
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jörg J Vehreschild
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Michael Hoelscher
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology, Infection and Pandemic Research, Munich, Germany
- Unit Global Health, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Gerd Fätkenheuer
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Jason J Lavinder
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Michael S Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Clara Lehmann
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - George Georgiou
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Florian Klein
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany.
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany.
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Álvarez-Rodríguez B, Buceta J, Geller R. Comprehensive profiling of neutralizing polyclonal sera targeting coxsackievirus B3. Nat Commun 2023; 14:6417. [PMID: 37828013 PMCID: PMC10570382 DOI: 10.1038/s41467-023-42144-2] [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: 01/28/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Despite their fundamental role in resolving viral infections, our understanding of how polyclonal neutralizing antibody responses target non-enveloped viruses remains limited. To define these responses, we obtained the full antigenic profile of multiple human and mouse polyclonal sera targeting the capsid of a prototypical picornavirus, coxsackievirus B3. Our results uncover significant variation in the breadth and strength of neutralization sites targeted by individual human polyclonal responses, which contrasted with homogenous responses observed in experimentally infected mice. We further use these comprehensive antigenic profiles to define key structural and evolutionary parameters that are predictive of escape, assess epitope dominance at the population level, and reveal a need for at least two mutations to achieve significant escape from multiple sera. Overall, our data provide a comprehensive analysis of how polyclonal sera target a non-enveloped viral capsid and help define both immune dominance and escape at the population level.
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Affiliation(s)
- Beatriz Álvarez-Rodríguez
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Valencia, 46980, Spain.
| | - Javier Buceta
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Valencia, 46980, Spain.
| | - Ron Geller
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Valencia, 46980, Spain.
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15
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Busia A, Listgarten J. MBE: model-based enrichment estimation and prediction for differential sequencing data. Genome Biol 2023; 24:218. [PMID: 37784130 PMCID: PMC10544408 DOI: 10.1186/s13059-023-03058-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.
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Affiliation(s)
- Akosua Busia
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
| | - Jennifer Listgarten
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
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16
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Kikawa C, Cartwright-Acar CH, Stuart JB, Contreras M, Levoir LM, Evans MJ, Bloom JD, Goo L. The effect of single mutations in Zika virus envelope on escape from broadly neutralizing antibodies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.557606. [PMID: 37808848 PMCID: PMC10557620 DOI: 10.1101/2023.09.13.557606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Zika virus and dengue virus are co-circulating flaviviruses with a widespread endemic range. Eliciting broad and potent neutralizing antibodies is an attractive goal for developing a vaccine to simultaneously protect against these viruses. However, the capacity of viral mutations to confer escape from broadly neutralizing antibodies remains undescribed, due in part to limited throughput and scope of traditional approaches. Here, we use deep mutational scanning to map how all possible single amino acid mutations in Zika virus envelope protein affect neutralization by antibodies of varying breadth and potency. While all antibodies selected viral escape mutations, the mutations selected by broadly neutralizing antibodies conferred less escape relative to those selected by narrow, virus-specific antibodies. Surprisingly, even for broadly neutralizing antibodies with similar binding footprints, different single mutations led to escape, indicating distinct functional requirements for neutralization not captured by existing structures. Additionally, the antigenic effects of mutations selected by broadly neutralizing antibodies were conserved across divergent, albeit related, flaviviruses. Our approach identifies residues critical for antibody neutralization, thus comprehensively defining the as-yet-unknown functional epitopes of antibodies with clinical potential.
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Affiliation(s)
- Caroline Kikawa
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98109, USA
- Medical Scientist Training Program, University of Washington, Seattle, Washington, 98109, USA
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, 98109, USA
| | | | - Jackson B. Stuart
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, 98109, USA
| | - Maya Contreras
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, 98109, USA
| | - Lisa M. Levoir
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, 98109, USA
| | - Matthew J. Evans
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Jesse D. Bloom
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98109, USA
- Basic Sciences and Computational Biology, Fred Hutch Cancer Center, Seattle Washington, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Leslie Goo
- Vaccine and Infectious Disease Division, Fred Hutch Cancer Center, Seattle, Washington, 98109, USA
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17
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Christensen S, Wernersson C, André I. Facile Method for High-throughput Identification of Stabilizing Mutations. J Mol Biol 2023; 435:168209. [PMID: 37479080 DOI: 10.1016/j.jmb.2023.168209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023]
Abstract
Characterizing the effects of mutations on stability is critical for understanding the function and evolution of proteins and improving their biophysical properties. High throughput folding and abundance assays have been successfully used to characterize missense mutations associated with reduced stability. However, screening for increased thermodynamic stability is more challenging since such mutations are rarer and their impact on assay readout is more subtle. Here, a multiplex assay for high throughput screening of protein folding was developed by combining deep mutational scanning, fluorescence-activated cell sorting, and deep sequencing. By analyzing a library of 2000 variants of Adenylate kinase we demonstrate that the readout of the method correlates with stability and that mutants with up to 13 °C increase in thermal melting temperature could be identified with low false positive rate. The discovery of many stabilizing mutations also enabled the analysis of general substitution patterns associated with increased stability in Adenylate kinase. This high throughput method to identify stabilizing mutations can be combined with functional screens to identify mutations that improve both stability and activity.
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Affiliation(s)
- Signe Christensen
- Department of Biochemistry and Structural Biology, Lund University, Lund, Sweden
| | - Camille Wernersson
- Department of Biochemistry and Structural Biology, Lund University, Lund, Sweden
| | - Ingemar André
- Department of Biochemistry and Structural Biology, Lund University, Lund, Sweden.
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18
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. Bioinformatics 2023; 39:btad446. [PMID: 37478351 PMCID: PMC10477941 DOI: 10.1093/bioinformatics/btad446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
MOTIVATION Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. RESULTS Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AVAILABILITY AND IMPLEMENTATION All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.
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Affiliation(s)
- Matthew D Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Marshall A Case
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Emily K Makowski
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Protein Folding Disease Initiative, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI 48109-2200, United States
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19
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548448. [PMID: 37503142 PMCID: PMC10369870 DOI: 10.1101/2023.07.10.548448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Results Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. Availability All deep sequencing datasets and code to do the analyses presented within are available via GitHub. Contact Peter Tessier, ptessier@umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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20
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Hoskins I, Sun S, Cote A, Roth FP, Cenik C. satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect. Genome Biol 2023; 24:82. [PMID: 37081510 PMCID: PMC10116734 DOI: 10.1186/s13059-023-02922-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.
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Affiliation(s)
- Ian Hoskins
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Song Sun
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Atina Cote
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Frederick P Roth
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
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21
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Sun T, Rosenberg BR, Chung H, Rice CM. Identification of ADAR1 p150 and p110 Associated Edit Sites. Methods Mol Biol 2023; 2651:285-294. [PMID: 36892775 DOI: 10.1007/978-1-0716-3084-6_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Adenosine deaminase acting on RNA 1 (ADAR1) catalyzes adenosine-to-inosine editing on double-stranded RNA molecules and is involved in regulating cellular responses to endogenous and exogenous RNA. ADAR1 is the primary A-to-I editor of RNA in humans, and the majority of edit sites are found in a class of short interspersed nuclear elements called Alu elements, many of which are located in introns and 3' untranslated regions. Two ADAR1 protein isoforms, p110 (110 kDa) and p150 (150 kDa), are known to be coupled in expression, and decoupling the expression of these isoforms has revealed that the p150 isoform edits a broader range of targets compared to p110. Numerous methods for identification of ADAR1-associated edits have been developed, and we present here a specific method for identification of edit sites associated with individual ADAR1 isoforms.
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Affiliation(s)
- Tony Sun
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA.
| | - Brad R Rosenberg
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hachung Chung
- Department of Microbiology and Immunology, Columbia University, New York, NY, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA
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22
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Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable Functional Assays for the Interpretation of Human Genetic Variation. Annu Rev Genet 2022; 56:441-465. [PMID: 36055970 DOI: 10.1146/annurev-genet-072920-032107] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.
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Affiliation(s)
- Daniel Tabet
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Victoria Parikh
- Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Frederick P Roth
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine and Endocrine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA;
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23
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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24
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Ding D, Green AG, Wang B, Lite TLV, Weinstein EN, Marks DS, Laub MT. Co-evolution of interacting proteins through non-contacting and non-specific mutations. Nat Ecol Evol 2022; 6:590-603. [PMID: 35361892 PMCID: PMC9090974 DOI: 10.1038/s41559-022-01688-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 01/31/2022] [Indexed: 01/08/2023]
Abstract
Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact and promote tolerance non-specifically to many different antitoxin mutations, despite covariation in homologues occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.
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Affiliation(s)
- David Ding
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Anna G Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Boyuan Wang
- Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Thuy-Lan Vo Lite
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Michael T Laub
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
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25
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Tareen A, Kooshkbaghi M, Posfai A, Ireland WT, McCandlish DM, Kinney JB. MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect. Genome Biol 2022; 23:98. [PMID: 35428271 PMCID: PMC9011994 DOI: 10.1186/s13059-022-02661-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022] Open
Abstract
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.
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Affiliation(s)
- Ammar Tareen
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, NY, USA
- Present Address: Regeneron Pharmaceuticals, Inc., Tarrytown, 10591, NY, USA
| | - Mahdi Kooshkbaghi
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, NY, USA
| | - Anna Posfai
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, NY, USA
| | - William T Ireland
- Department of Physics, California Institute of Technology, Pasadena, 91125, CA, USA
- Present Address: Department of Applied Physics, Harvard University, Cambridge, 02134, MA, USA
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, NY, USA
| | - Justin B Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, NY, USA.
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26
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The endoplasmic reticulum proteostasis network profoundly shapes the protein sequence space accessible to HIV envelope. PLoS Biol 2022; 20:e3001569. [PMID: 35180219 PMCID: PMC8906867 DOI: 10.1371/journal.pbio.3001569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 03/09/2022] [Accepted: 02/07/2022] [Indexed: 12/27/2022] Open
Abstract
The sequence space accessible to evolving proteins can be enhanced by cellular chaperones that assist biophysically defective clients in navigating complex folding landscapes. It is also possible, at least in theory, for proteostasis mechanisms that promote strict quality control to greatly constrain accessible protein sequence space. Unfortunately, most efforts to understand how proteostasis mechanisms influence evolution rely on artificial inhibition or genetic knockdown of specific chaperones. The few experiments that perturb quality control pathways also generally modulate the levels of only individual quality control factors. Here, we use chemical genetic strategies to tune proteostasis networks via natural stress response pathways that regulate the levels of entire suites of chaperones and quality control mechanisms. Specifically, we upregulate the unfolded protein response (UPR) to test the hypothesis that the host endoplasmic reticulum (ER) proteostasis network shapes the sequence space accessible to human immunodeficiency virus-1 (HIV-1) envelope (Env) protein. Elucidating factors that enhance or constrain Env sequence space is critical because Env evolves extremely rapidly, yielding HIV strains with antibody- and drug-escape mutations. We find that UPR-mediated upregulation of ER proteostasis factors, particularly those controlled by the IRE1-XBP1s UPR arm, globally reduces Env mutational tolerance. Conserved, functionally important Env regions exhibit the largest decreases in mutational tolerance upon XBP1s induction. Our data indicate that this phenomenon likely reflects strict quality control endowed by XBP1s-mediated remodeling of the ER proteostasis environment. Intriguingly, and in contrast, specific regions of Env, including regions targeted by broadly neutralizing antibodies, display enhanced mutational tolerance when XBP1s is induced, hinting at a role for host proteostasis network hijacking in potentiating antibody escape. These observations reveal a key function for proteostasis networks in decreasing instead of expanding the sequence space accessible to client proteins, while also demonstrating that the host ER proteostasis network profoundly shapes the mutational tolerance of Env in ways that could have important consequences for HIV adaptation. The host cell’s endoplasmic reticulum proteostasis network has a profound, constraining impact on the protein sequence space accessible to HIV’s envelope protein, which is a major target of the host’s adaptive immune system; in particular, upregulation of stringent quality control pathways appears to restrict the viability of destabilizing envelope variants.
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27
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Guthmiller JJ, Han J, Utset HA, Li L, Lan LYL, Henry C, Stamper CT, McMahon M, O'Dell G, Fernández-Quintero ML, Freyn AW, Amanat F, Stovicek O, Gentles L, Richey ST, de la Peña AT, Rosado V, Dugan HL, Zheng NY, Tepora ME, Bitar DJ, Changrob S, Strohmeier S, Huang M, García-Sastre A, Liedl KR, Bloom JD, Nachbagauer R, Palese P, Krammer F, Coughlan L, Ward AB, Wilson PC. Broadly neutralizing antibodies target a haemagglutinin anchor epitope. Nature 2022; 602:314-320. [PMID: 34942633 PMCID: PMC8828479 DOI: 10.1038/s41586-021-04356-8] [Citation(s) in RCA: 81] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/15/2021] [Indexed: 11/09/2022]
Abstract
Broadly neutralizing antibodies that target epitopes of haemagglutinin on the influenza virus have the potential to provide near universal protection against influenza virus infection1. However, viral mutants that escape broadly neutralizing antibodies have been reported2,3. The identification of broadly neutralizing antibody classes that can neutralize viral escape mutants is critical for universal influenza virus vaccine design. Here we report a distinct class of broadly neutralizing antibodies that target a discrete membrane-proximal anchor epitope of the haemagglutinin stalk domain. Anchor epitope-targeting antibodies are broadly neutralizing across H1 viruses and can cross-react with H2 and H5 viruses that are a pandemic threat. Antibodies that target this anchor epitope utilize a highly restricted repertoire, which encodes two public binding motifs that make extensive contacts with conserved residues in the fusion peptide. Moreover, anchor epitope-targeting B cells are common in the human memory B cell repertoire and were recalled in humans by an oil-in-water adjuvanted chimeric haemagglutinin vaccine4,5, which is a potential universal influenza virus vaccine. To maximize protection against seasonal and pandemic influenza viruses, vaccines should aim to boost this previously untapped source of broadly neutralizing antibodies that are widespread in the human memory B cell pool.
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Affiliation(s)
- Jenna J Guthmiller
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA.
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Henry A Utset
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Lei Li
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | | | - Carole Henry
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
- Moderna Inc., Cambridge, MA, USA
| | | | - Meagan McMahon
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George O'Dell
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Monica L Fernández-Quintero
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Alec W Freyn
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Moderna Inc., Cambridge, MA, USA
| | - Fatima Amanat
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivia Stovicek
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Lauren Gentles
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Microbiology, University of Washington, Seattle, WA, USA
| | - Sara T Richey
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Alba Torrents de la Peña
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Victoria Rosado
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Haley L Dugan
- Committee on Immunology, University of Chicago, Chicago, IL, USA
| | - Nai-Ying Zheng
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Micah E Tepora
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Dalia J Bitar
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Siriruk Changrob
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Shirin Strohmeier
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Min Huang
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Klaus R Liedl
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Microbiology, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Raffael Nachbagauer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Moderna Inc., Cambridge, MA, USA
| | - Peter Palese
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lynda Coughlan
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
- Center for Vaccine Development and Global Health (CVD), University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew B Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
| | - Patrick C Wilson
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA.
- Committee on Immunology, University of Chicago, Chicago, IL, USA.
- Drukier Institute for Children's Health, Department of Pediatrics, Weill Cornell Medicine, New York, NY, USA.
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28
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Dubé AK, Dandage R, Dibyachintan S, Dionne U, Després PC, Landry CR. Deep Mutational Scanning of Protein-Protein Interactions Between Partners Expressed from Their Endogenous Loci In Vivo. Methods Mol Biol 2022; 2477:237-259. [PMID: 35524121 DOI: 10.1007/978-1-0716-2257-5_14] [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] [Indexed: 06/14/2023]
Abstract
Deep mutational scanning (DMS) generates mutants of a protein of interest in a comprehensive manner. CRISPR-Cas9 technology enables large-scale genome editing with high efficiency. Using both DMS and CRISPR-Cas9 therefore allows us to investigate the effects of thousands of mutations inserted directly in the genome. Combined with protein-fragment complementation assay (PCA), which enables the quantitative measurement of protein-protein interactions (PPIs) in vivo, these methods allow for the systematic assessment of the effects of mutations on PPIs in living cells. Here, we describe a method leveraging DMS, CRISPR-Cas9, and PCA to study the effect of point mutations on PPIs mediated by protein domains in yeast.
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Affiliation(s)
- Alexandre K Dubé
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada.
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
| | - Rohan Dandage
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
| | - Soham Dibyachintan
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- Department of Chemical Engineering, Indian Institute of Technology Bombay (IIT), Powai, Mumbai, Maharashtra, India
| | - Ugo Dionne
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche du Centre Hospitalier Universitaire (CHU) de Québec, Université Laval, Québec, QC, Canada
- Centre de recherche sur le cancer de l'Université Laval, Québec, QC, Canada
| | - Philippe C Després
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada
| | - Christian R Landry
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
- PROTEO, le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, QC, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, Canada.
- Département de Biologie, Faculté de Sciences et Génie, Université Laval, Québec, QC, Canada.
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29
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Hanning KR, Minot M, Warrender AK, Kelton W, Reddy ST. Deep mutational scanning for therapeutic antibody engineering. Trends Pharmacol Sci 2021; 43:123-135. [PMID: 34895944 DOI: 10.1016/j.tips.2021.11.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022]
Abstract
The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.
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Affiliation(s)
- Kyrin R Hanning
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - Mason Minot
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland
| | - Annmaree K Warrender
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - William Kelton
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland.
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30
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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31
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Youssef N, Susko E, Roger AJ, Bielawski JP. Shifts in amino acid preferences as proteins evolve: A synthesis of experimental and theoretical work. Protein Sci 2021; 30:2009-2028. [PMID: 34322924 PMCID: PMC8442975 DOI: 10.1002/pro.4161] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/19/2021] [Accepted: 07/26/2021] [Indexed: 11/08/2022]
Abstract
Amino acid preferences vary across sites and time. While variation across sites is widely accepted, the extent and frequency of temporal shifts are contentious. Our understanding of the drivers of amino acid preference change is incomplete: To what extent are temporal shifts driven by adaptive versus nonadaptive evolutionary processes? We review phenomena that cause preferences to vary (e.g., evolutionary Stokes shift, contingency, and entrenchment) and clarify how they differ. To determine the extent and prevalence of shifted preferences, we review experimental and theoretical studies. Analyses of natural sequence alignments often detect decreases in homoplasy (convergence and reversions) rates, and variation in replacement rates with time-signals that are consistent with temporally changing preferences. While approaches inferring shifts in preferences from patterns in natural alignments are valuable, they are indirect since multiple mechanisms (both adaptive and nonadaptive) could lead to the observed signal. Alternatively, site-directed mutagenesis experiments allow for a more direct assessment of shifted preferences. They corroborate evidence from multiple sequence alignments, revealing that the preference for an amino acid at a site varies depending on the background sequence. However, shifts in preferences are usually minor in magnitude and sites with significantly shifted preferences are low in frequency. The small yet consistent perturbations in preferences could, nevertheless, jeopardize the accuracy of inference procedures, which assume constant preferences. We conclude by discussing if and how such shifts in preferences might influence widely used time-homogenous inference procedures and potential ways to mitigate such effects.
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Affiliation(s)
- Noor Youssef
- Department of BiologyDalhousie UniversityHalifaxNova ScotiaCanada
| | - Edward Susko
- Department of Mathematics and StatisticsDalhousie UniversityHalifaxNova ScotiaCanada
| | - Andrew J. Roger
- Department of Biochemistry and Molecular BiologyDalhousie UniversityHalifaxNova ScotiaCanada
| | - Joseph P. Bielawski
- Department of BiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Mathematics and StatisticsDalhousie UniversityHalifaxNova ScotiaCanada
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32
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Findlay GM. Linking genome variants to disease: scalable approaches to test the functional impact of human mutations. Hum Mol Genet 2021; 30:R187-R197. [PMID: 34338757 PMCID: PMC8490018 DOI: 10.1093/hmg/ddab219] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 07/19/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.
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Affiliation(s)
- Gregory M Findlay
- The Francis Crick Institute, The Genome Function Laboratory, London NW1 1AT, UK
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33
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McDonnell MM, Karvonen SC, Gaba A, Flath B, Chelico L, Emerman M. Highly-potent, synthetic APOBEC3s restrict HIV-1 through deamination-independent mechanisms. PLoS Pathog 2021; 17:e1009523. [PMID: 34170969 PMCID: PMC8266076 DOI: 10.1371/journal.ppat.1009523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/08/2021] [Accepted: 06/07/2021] [Indexed: 02/07/2023] Open
Abstract
The APOBEC3 (A3) genes encode cytidine deaminase proteins with potent antiviral and anti-retroelement activity. This locus is characterized by duplication, recombination, and deletion events that gave rise to the seven A3s found in primates. These include three single deaminase domain A3s (A3A, A3C, and A3H) and four double deaminase domain A3s (A3B, A3D, A3F, and A3G). The most potent of the A3 proteins against HIV-1 is A3G. However, it is not clear if double deaminase domain A3s have a generalized functional advantage to restrict HIV-1. In order to test whether superior restriction factors could be created by genetically linking single A3 domains into synthetic double domains, we linked A3C and A3H single domains in novel combinations. We found that A3C/A3H double domains acquired enhanced antiviral activity that is at least as potent, if not better than, A3G. Although these synthetic double domain A3s package into budding virions more efficiently than their respective single domains, this does not fully explain their gain of antiviral potency. The antiviral activity is conferred both by cytidine-deaminase dependent and independent mechanisms, with the latter correlating to an increase in RNA binding affinity. T cell lines expressing this A3C-A3H super restriction factor are able to control replicating HIV-1ΔVif infection to similar levels as A3G. Together, these data show that novel combinations of A3 domains are capable of gaining potent antiviral activity to levels similar to the most potent genome-encoded A3s, via a primarily non-catalytic mechanism.
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Affiliation(s)
- Mollie M. McDonnell
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, United States of America
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Suzanne C. Karvonen
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Amit Gaba
- Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Ben Flath
- Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Linda Chelico
- Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Michael Emerman
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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34
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Burton TD, Eyre NS. Applications of Deep Mutational Scanning in Virology. Viruses 2021; 13:1020. [PMID: 34071591 PMCID: PMC8227372 DOI: 10.3390/v13061020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022] Open
Abstract
Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.
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Affiliation(s)
| | - Nicholas S. Eyre
- College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia;
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35
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Dingens AS, Pratap P, Malone K, Hilton SK, Ketas T, Cottrell CA, Overbaugh J, Moore JP, Klasse PJ, Ward AB, Bloom JD. High-resolution mapping of the neutralizing and binding specificities of polyclonal sera post-HIV Env trimer vaccination. eLife 2021; 10:e64281. [PMID: 33438580 PMCID: PMC7864656 DOI: 10.7554/elife.64281] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/12/2021] [Indexed: 01/01/2023] Open
Abstract
Mapping polyclonal serum responses is critical to rational vaccine design. However, most high-resolution mapping approaches involve isolating and characterizing individual antibodies, which incompletely defines the polyclonal response. Here we use two complementary approaches to directly map the specificities of the neutralizing and binding antibodies of polyclonal anti-HIV-1 sera from rabbits immunized with BG505 Env SOSIP trimers. We used mutational antigenic profiling to determine how all mutations in Env affected viral neutralization and electron microscopy polyclonal epitope mapping (EMPEM) to directly visualize serum Fabs bound to Env trimers. The dominant neutralizing specificities were generally only a subset of the more diverse binding specificities. Additional differences between binding and neutralization reflected antigenicity differences between virus and soluble Env trimer. Furthermore, we refined residue-level epitope specificity directly from sera, revealing subtle differences across sera. Together, mutational antigenic profiling and EMPEM yield a holistic view of the binding and neutralizing specificity of polyclonal sera.
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Affiliation(s)
- Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Payal Pratap
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Keara Malone
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Sarah K Hilton
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Thomas Ketas
- Department of Microbiology and Immunology, Weill Cornell Medical CollegeNew YorkUnited States
| | - Christopher A Cottrell
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Julie Overbaugh
- Human Biology Division, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - John P Moore
- Department of Microbiology and Immunology, Weill Cornell Medical CollegeNew YorkUnited States
| | - PJ Klasse
- Department of Microbiology and Immunology, Weill Cornell Medical CollegeNew YorkUnited States
| | - Andrew B Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Howard Hughes Medical InstituteSeattleUnited States
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36
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Mattenberger F, Latorre V, Tirosh O, Stern A, Geller R. Globally defining the effects of mutations in a picornavirus capsid. eLife 2021; 10:64256. [PMID: 33432927 PMCID: PMC7861617 DOI: 10.7554/elife.64256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/11/2021] [Indexed: 02/07/2023] Open
Abstract
The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis. Despite their importance, a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking. To address this limitation, we globally define the effects of mutations across the capsid of a human picornavirus. Using this resource, we identify structural and sequence determinants that accurately predict mutational fitness effects, refine evolutionary analyses, and define the sequence specificity of key capsid-encoded motifs. Furthermore, capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites, we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases. Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology, evolution, and host interactions. A virus is made up of genetic material that is encased with a protective protein coat called the capsid. The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material. Inside the cell, the virus hitchhikes the infected cell’s machinery to grow or replicate its own genetic material. Viral capsids are the main target of the host’s defence system, and therefore, continuously change in an attempt to escape the immune system by introducing alterations (known as mutations) into the genes encoding viral capsid proteins. Mutations occur randomly, and so while some changes to the viral capsid might confer an advantage, others may have no effect at all, or even weaken the virus. To better understand the effect of capsid mutations on the virus’ ability to infect host cells, Mattenberger et al. studied the Coxsackievirus B3, which is linked to heart problems and acute heart failure in humans. The researchers analysed around 90% of possible amino acid mutations (over 14,800 mutations) and correlated each mutation to how it influenced the virus’ ability to replicate in human cells grown in the laboratory. Based on these results, Mattenberger et al. developed a computer model to predict how a particular mutation might affect the virus. The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks, such as building the capsid. It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein, which cuts the capsid proteins into the right size from a larger precursor. By looking for similar sequences in human genes, the researchers identified several ones that the virus may attack and inactivate to support its own replication. These findings may help identify potential drug targets to develop new antiviral therapies. For example, proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment. They also highlight new proteins in human cells that could potentially block the virus in cells.
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Affiliation(s)
- Florian Mattenberger
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
| | - Victor Latorre
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
| | - Omer Tirosh
- The Shmunis School of Biomedicine and Cancer Research, Tel-Aviv University, Tel-Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel-Aviv University, Tel-Aviv, Israel
| | - Ron Geller
- Institute for Integrative Systems Biology, I2SysBio (Universitat de València-CSIC), Paterna, Spain
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37
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Song H, Bremer BJ, Hinds EC, Raskutti G, Romero PA. Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning. Cell Syst 2020; 12:92-101.e8. [PMID: 33212013 DOI: 10.1016/j.cels.2020.10.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 08/13/2020] [Accepted: 10/22/2020] [Indexed: 10/22/2022]
Abstract
Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.
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Affiliation(s)
- Hyebin Song
- Department of Statistics, The Pennsylvania State University, State College, PA 16802, USA; Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Bennett J Bremer
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Emily C Hinds
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Garvesh Raskutti
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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38
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Jones EM, Lubock NB, Venkatakrishnan AJ, Wang J, Tseng AM, Paggi JM, Latorraca NR, Cancilla D, Satyadi M, Davis JE, Babu MM, Dror RO, Kosuri S. Structural and functional characterization of G protein-coupled receptors with deep mutational scanning. eLife 2020; 9:54895. [PMID: 33084570 PMCID: PMC7707821 DOI: 10.7554/elife.54895] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 10/16/2020] [Indexed: 01/14/2023] Open
Abstract
The >800 human G protein–coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state- and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here, we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G protein signal transduction. We tested 7800 of 7828 possible single amino acid substitutions to the beta-2 adrenergic receptor (β2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for β2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we identify residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.
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Affiliation(s)
- Eric M Jones
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Nathan B Lubock
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - A J Venkatakrishnan
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.,Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Jeffrey Wang
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Alex M Tseng
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Joseph M Paggi
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Naomi R Latorraca
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Daniel Cancilla
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Megan Satyadi
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - Jessica E Davis
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Ron O Dror
- Department of Computer Science, Stanford University, Department of Computer Science, Institute for Computational and Mathematical Engineering, Stanford University, Department of Computer Science, Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Department of Computer Science, Department of Structural Biology, Stanford University School of Medicine, Stanford, United States
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, Quantitative and Computational Biology Institute, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, and Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, United States
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39
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Identification of Antibodies Targeting the H3N2 Hemagglutinin Receptor Binding Site following Vaccination of Humans. Cell Rep 2020; 29:4460-4470.e8. [PMID: 31875553 PMCID: PMC6953393 DOI: 10.1016/j.celrep.2019.11.084] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/26/2019] [Accepted: 11/20/2019] [Indexed: 11/26/2022] Open
Abstract
Antibodies targeting the receptor binding site (RBS) of the influenza virus hemagglutinin (HA) protein are usually not broadly reactive because their footprints are typically large and extend to nearby variable HA residues. Here, we identify several human H3N2 HA RBS-targeting monoclonal antibodies (mAbs) that are sensitive to substitutions in conventional antigenic sites and are therefore not broadly reactive. However, we also identify an H3N2 HA RBS-targeting mAb that is exceptionally broadly reactive despite being sensitive to substitutions in residues outside of the RBS. We show that similar antibodies are present at measurable levels in the sera of some individuals but that they are inefficiently elicited by conventional vaccines. Our data indicate that HA RBS-targeting antibodies can be effective against variable viral strains even when they are somewhat sensitive to substitutions in HA residues adjacent to the RBS. Zost et al. show that most antibodies targeting the RBS of the H3N2 HAs are not broadly reactive. They identify one broadly reactive H3 HA RBS antibody that is tolerant of substitutions in adjacent antigenic sites but show that these types of antibodies are not efficiently elicited by vaccination.
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40
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DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol 2020; 21:207. [PMID: 32799905 PMCID: PMC7429474 DOI: 10.1186/s13059-020-02091-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 07/05/2020] [Indexed: 12/30/2022] Open
Abstract
Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs, and regulatory elements. Here, we present a customizable pipeline, DiMSum, that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.
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41
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Hilton SK, Huddleston J, Black A, North K, Dingens AS, Bedford T, Bloom JD. dms-view: Interactive visualization tool for deep mutational scanning data. JOURNAL OF OPEN SOURCE SOFTWARE 2020; 5:2353. [PMID: 34189395 PMCID: PMC8237788 DOI: 10.21105/joss.02353] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Sarah K Hilton
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Molecular and Cell Biology, University of Washington, Seattle, WA, USA
| | - Allison Black
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Khrystyna North
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Adam S Dingens
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jesse D Bloom
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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42
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Saad-Roy CM, Arinaminpathy N, Wingreen NS, Levin SA, Akey JM, Grenfell BT. Implications of localized charge for human influenza A H1N1 hemagglutinin evolution: Insights from deep mutational scans. PLoS Comput Biol 2020; 16:e1007892. [PMID: 32584807 PMCID: PMC7316228 DOI: 10.1371/journal.pcbi.1007892] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
Seasonal influenza A viruses of humans evolve rapidly due to strong selection pressures from host immune responses, principally on the hemagglutinin (HA) viral surface protein. Based on mouse transmission experiments, a proposed mechanism for immune evasion consists of increased avidity to host cellular receptors, mediated by electrostatic charge interactions with negatively charged cell surfaces. In support of this, the HA charge of the globally circulating H3N2 has increased over time since its pandemic. However, the same trend was not seen in H1N1 HA sequences. This is counter-intuitive, since immune escape due to increased avidity (due itself to an increase in charge) was determined experimentally. Here, we explore whether patterns of local charge of H1N1 HA can explain this discrepancy and thus further associate electrostatic charge with immune escape and viral evolutionary dynamics. Measures of site-wise functional selection and expected charge computed from deep mutational scan data on an early H1N1 HA yield a striking division of residues into three groups, separated by charge. We then explored evolutionary dynamics of these groups from 1918 to 2008. In particular, one group increases in net charge over time and consists of sites that are evolving the fastest, that are closest to the receptor binding site (RBS), and that are exposed to solvent (i.e., on the surface). By contrast, another group decreases in net charge and consists of sites that are further away from the RBS and evolving slower, but also exposed to solvent. The last group consists of those sites in the HA core, with no change in net charge and that evolve very slowly. Thus, there is a group of residues that follows the same trend as seen for the entire H3N2 HA. It is possible that the H1N1 HA is under other biophysical constraints that result in compensatory decreases in charge elsewhere on the protein. Our results implicate localized charge in HA interactions with host cells, and highlight how deep mutational scan data can inform evolutionary hypotheses. The hemagglutinin (HA) surface protein of influenza A viruses evolves rapidly to evade host immunity, leading to sizable yearly epidemics in human populations. Previous transmission experiments with H1N1 in mice have tied immune escape to an increase in HA avidity for cellular receptors, mediated by electrostatic charge. Furthermore, retrospective sequence analyses from a previous study confirmed that the HA of circulating global H3N2 has increased in net charge, yet surprisingly, that of H1N1 has not varied significantly. How is a stable net charge related to local patterns of H1N1 HA charge in response to selection? To elucidate the role of local electrostatic charge in host-virus interactions, we investigate characteristics of local charge on the H1N1 HA using functional data from deep mutational scan experiments. Combining measures of functional selection and expected charge at each site on DMS data from a 1933 H1N1 HA yields a striking visual pattern that identifies three groups of sites that have different biophysical properties and that prove to have distinct evolutionary patterns in natural human sequences. Essentially, we find evidence for an increase in charge near the receptor binding site and for compensatory changes elsewhere on the protein. Thus, our findings may reconcile disparate results from transmission experiments and natural sequence analyses, and highlight the importance of local properties of the HA protein. Overall, our findings further support the hypothesis of immune escape due to increased HA avidity to cellular receptors mediated by electrostatic charge. More generally, our work illustrates a novel method of leveraging deep mutational scans in conjunction with natural sequences to shed light on virus-host interactions.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail: (CMSR); (BTG)
| | - Nimalan Arinaminpathy
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Ned S. Wingreen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Joshua M. Akey
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (CMSR); (BTG)
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Wu NC, Thompson AJ, Lee JM, Su W, Arlian BM, Xie J, Lerner RA, Yen HL, Bloom JD, Wilson IA. Different genetic barriers for resistance to HA stem antibodies in influenza H3 and H1 viruses. Science 2020; 368:1335-1340. [PMID: 32554590 PMCID: PMC7412937 DOI: 10.1126/science.aaz5143] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 04/14/2020] [Indexed: 12/19/2022]
Abstract
The discovery and characterization of broadly neutralizing human antibodies (bnAbs) to the highly conserved stem region of influenza hemagglutinin (HA) have contributed to considerations of a universal influenza vaccine. However, the potential for resistance to stem bnAbs also needs to be more thoroughly evaluated. Using deep mutational scanning, with a focus on epitope residues, we found that the genetic barrier to resistance to stem bnAbs is low for the H3 subtype but substantially higher for the H1 subtype owing to structural differences in the HA stem. Several strong resistance mutations in H3 can be observed in naturally circulating strains and do not reduce in vitro viral fitness and in vivo pathogenicity. This study highlights a potential challenge for development of a truly universal influenza vaccine.
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Affiliation(s)
- Nicholas C Wu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew J Thompson
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Juhye M Lee
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA
| | - Wen Su
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Britni M Arlian
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jia Xie
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Richard A Lerner
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hui-Ling Yen
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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Klesmith JR, Hackel BJ. Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit. Bioinformatics 2020; 35:2707-2712. [PMID: 30590444 DOI: 10.1093/bioinformatics/bty1042] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Deep mutational scanning experiments have enabled the measurement of the sequence-function relationship for thousands of mutations in a single experiment. The Protein Analysis and Classifier Toolkit (PACT) is a Python software package that marries the fitness metric of a given mutation within these experiments to sequence and structural features enabling downstream analyses. PACT enables the easy development of user sharable protocols for custom deep mutational scanning experiments as all code is modular and reusable between protocols. Protocols for mutational libraries with single or multiple mutations are included. To exemplify its utility, PACT assessed two deep mutational scanning datasets that measured the tradeoff of enzyme activity and enzyme stability. RESULTS PACT efficiently evaluated classifiers that predict protein mutant function tested on deep mutational scanning screens. We found that the classifiers with the lowest false positive and highest true positive rate assesses sequence homology, contact number and if mutation involves proline. AVAILABILITY AND IMPLEMENTATION PACT and the processed datasets are distributed freely under the terms of the GPL-3 license. The source code is available at GitHub (https://github.com/JKlesmith/PACT). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justin R Klesmith
- Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Benjamin J Hackel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
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Abstract
As a part of the innate immune system, humans encode proteins that inhibit viruses such as HIV-1. These broadly acting antiviral proteins do not protect humans from viral infections because viruses encode proteins that antagonize the host antiviral proteins to evade the innate immune system. One such example of a host antiviral protein is APOBEC3C (A3C), which weakly inhibits HIV-1. Here, we show that we can improve the antiviral activity of A3C by duplicating the DNA sequence to create a synthetic tandem domain and, furthermore, that the proteins thus generated are relatively resistant to the viral antagonist Vif. Together, these data give insights about how nature has evolved a defense against viral pathogens such as HIV. Humans encode proteins, called restriction factors, that inhibit replication of viruses such as HIV-1. The members of one family of antiviral proteins, apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3; shortened here to A3), act by deaminating cytidines to uridines during the reverse transcription reaction of HIV-1. The A3 locus encodes seven genes, named A3A to A3H. These genes have either one or two cytidine deaminase domains, and several of these A3s potently restrict HIV-1. A3C, which has only a single cytidine deaminase domain, however, inhibits HIV-1 only very weakly. We tested novel double domain protein combinations by genetically linking two A3C genes to make a synthetic tandem domain protein. This protein created a “super restriction factor” that had more potent antiviral activity than the native A3C protein, which correlated with increased packaging into virions. Furthermore, disabling one of the active sites of the synthetic tandem domain protein resulted in an even greater increase in the antiviral activity—recapitulating a similar evolution seen in A3F and A3G (double domain A3s that use only a single catalytically active deaminase domain). These A3C tandem domain proteins do not have an increase in mutational activity but instead inhibit formation of reverse transcription products, which correlates with their ability to form large higher-order complexes in cells. Finally, the A3C-A3C super restriction factor largely escaped antagonism by the HIV-1 viral protein Vif.
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Kong R, Duan H, Sheng Z, Xu K, Acharya P, Chen X, Cheng C, Dingens AS, Gorman J, Sastry M, Shen CH, Zhang B, Zhou T, Chuang GY, Chao CW, Gu Y, Jafari AJ, Louder MK, O'Dell S, Rowshan AP, Viox EG, Wang Y, Choi CW, Corcoran MM, Corrigan AR, Dandey VP, Eng ET, Geng H, Foulds KE, Guo Y, Kwon YD, Lin B, Liu K, Mason RD, Nason MC, Ohr TY, Ou L, Rawi R, Sarfo EK, Schön A, Todd JP, Wang S, Wei H, Wu W, Mullikin JC, Bailer RT, Doria-Rose NA, Karlsson Hedestam GB, Scorpio DG, Overbaugh J, Bloom JD, Carragher B, Potter CS, Shapiro L, Kwong PD, Mascola JR. Antibody Lineages with Vaccine-Induced Antigen-Binding Hotspots Develop Broad HIV Neutralization. Cell 2020; 178:567-584.e19. [PMID: 31348886 DOI: 10.1016/j.cell.2019.06.030] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 05/03/2019] [Accepted: 06/19/2019] [Indexed: 01/09/2023]
Abstract
The vaccine-mediated elicitation of antibodies (Abs) capable of neutralizing diverse HIV-1 strains has been a long-standing goal. To understand how broadly neutralizing antibodies (bNAbs) can be elicited, we identified, characterized, and tracked five neutralizing Ab lineages targeting the HIV-1-fusion peptide (FP) in vaccinated macaques over time. Genetic and structural analyses revealed two of these lineages to belong to a reproducible class capable of neutralizing up to 59% of 208 diverse viral strains. B cell analysis indicated each of the five lineages to have been initiated and expanded by FP-carrier priming, with envelope (Env)-trimer boosts inducing cross-reactive neutralization. These Abs had binding-energy hotspots focused on FP, whereas several FP-directed Abs induced by immunization with Env trimer-only were less FP-focused and less broadly neutralizing. Priming with a conserved subregion, such as FP, can thus induce Abs with binding-energy hotspots coincident with the target subregion and capable of broad neutralization.
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Affiliation(s)
- Rui Kong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Hongying Duan
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Zizhang Sheng
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Kai Xu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Priyamvada Acharya
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA; National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC 27710, USA
| | - Xuejun Chen
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Cheng Cheng
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98195, USA; Molecular and Cellular Biology PhD Program, University of Washington, Seattle, WA 98195, USA; Division of Human Biology and Epidemiology Program, Seattle, WA 98195, USA
| | - Jason Gorman
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA; National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Mallika Sastry
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Chen-Hsiang Shen
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Baoshan Zhang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Tongqing Zhou
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Cara W Chao
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Ying Gu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Alexander J Jafari
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Mark K Louder
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Sijy O'Dell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Ariana P Rowshan
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Elise G Viox
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Yiran Wang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Chang W Choi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Martin M Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm 17177, Sweden
| | - Angela R Corrigan
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Venkata P Dandey
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Edward T Eng
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Hui Geng
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Kathryn E Foulds
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Yicheng Guo
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Young D Kwon
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Bob Lin
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Kevin Liu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Rosemarie D Mason
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Martha C Nason
- Biostatistics Research Branch, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Tiffany Y Ohr
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Li Ou
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Edward K Sarfo
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Arne Schön
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - John P Todd
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Shuishu Wang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Hui Wei
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Winston Wu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | -
- NIH Intramural Sequencing Center, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - James C Mullikin
- NIH Intramural Sequencing Center, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Robert T Bailer
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Nicole A Doria-Rose
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | | | - Diana G Scorpio
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA
| | - Julie Overbaugh
- Division of Human Biology and Epidemiology Program, Seattle, WA 98195, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98195, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA
| | - Bridget Carragher
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Clinton S Potter
- National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Lawrence Shapiro
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
| | - John R Mascola
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA.
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Identification of HIV-1 Envelope Mutations that Enhance Entry Using Macaque CD4 and CCR5. Viruses 2020; 12:v12020241. [PMID: 32098152 PMCID: PMC7077290 DOI: 10.3390/v12020241] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 02/13/2020] [Accepted: 02/19/2020] [Indexed: 11/24/2022] Open
Abstract
Although Rhesus macaques are an important animal model for HIV-1 vaccine development research, most transmitted HIV-1 strains replicate poorly in macaque cells. A major genetic determinant of this species-specific restriction is a non-synonymous mutation in macaque CD4 that results in reduced HIV-1 Envelope (Env)-mediated viral entry compared to human CD4. Recent research efforts employing either laboratory evolution or structure-guided design strategies have uncovered several mutations in Env’s gp120 subunit that enhance binding of macaque CD4 by transmitted/founder HIV-1 viruses. In order to identify additional Env mutations that promote infection of macaque cells, we utilized deep mutational scanning to screen thousands of Env point mutants for those that enhance HIV-1 entry via macaque receptors. We identified many uncharacterized amino acid mutations in the N-terminal heptad repeat (NHR) and C-terminal heptad repeat (CHR) regions of gp41 that increased entry into cells bearing macaque receptors up to 9-fold. Many of these mutations also modestly increased infection of cells bearing human CD4 and CCR5 (up to 1.5-fold). NHR/CHR mutations identified by deep mutational scanning that enhanced entry also increased sensitivity to neutralizing antibodies targeting the MPER epitope, and to inactivation by cold-incubation, suggesting that they promote sampling of an intermediate trimer conformation between closed and receptor bound states. Identification of this set of mutations can inform future macaque model studies, and also further our understanding of the relationship between Env structure and function.
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Schommers P, Gruell H, Abernathy ME, Tran MK, Dingens AS, Gristick HB, Barnes CO, Schoofs T, Schlotz M, Vanshylla K, Kreer C, Weiland D, Holtick U, Scheid C, Valter MM, van Gils MJ, Sanders RW, Vehreschild JJ, Cornely OA, Lehmann C, Fätkenheuer G, Seaman MS, Bloom JD, Bjorkman PJ, Klein F. Restriction of HIV-1 Escape by a Highly Broad and Potent Neutralizing Antibody. Cell 2020; 180:471-489.e22. [PMID: 32004464 PMCID: PMC7042716 DOI: 10.1016/j.cell.2020.01.010] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/05/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Broadly neutralizing antibodies (bNAbs) represent a promising approach to prevent and treat HIV-1 infection. However, viral escape through mutation of the HIV-1 envelope glycoprotein (Env) limits clinical applications. Here we describe 1-18, a new VH1-46-encoded CD4 binding site (CD4bs) bNAb with outstanding breadth (97%) and potency (GeoMean IC50 = 0.048 μg/mL). Notably, 1-18 is not susceptible to typical CD4bs escape mutations and effectively overcomes HIV-1 resistance to other CD4bs bNAbs. Moreover, mutational antigenic profiling uncovered restricted pathways of HIV-1 escape. Of most promise for therapeutic use, even 1-18 alone fully suppressed viremia in HIV-1-infected humanized mice without selecting for resistant viral variants. A 2.5-Å cryo-EM structure of a 1-18-BG505SOSIP.664 Env complex revealed that these characteristics are likely facilitated by a heavy-chain insertion and increased inter-protomer contacts. The ability of 1-18 to effectively restrict HIV-1 escape pathways provides a new option to successfully prevent and treat HIV-1 infection.
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Affiliation(s)
- Philipp Schommers
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany
| | - Henning Gruell
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany
| | - Morgan E Abernathy
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - My-Kim Tran
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Harry B Gristick
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christopher O Barnes
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Till Schoofs
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Maike Schlotz
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Kanika Vanshylla
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Christoph Kreer
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Daniela Weiland
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Udo Holtick
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Christof Scheid
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Markus M Valter
- Department of Gynecology and Obstetrics, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Marit J van Gils
- Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Rogier W Sanders
- Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands; Department of Microbiology and Immunology, Weill Medical College of Cornell University, New York, NY 10021, USA
| | - Jörg J Vehreschild
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany; Medical Department 2, University Hospital of Frankfurt, 60590 Frankfurt, Germany
| | - Oliver A Cornely
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany; Clinical Trials Centre Cologne (ZKS Köln), University of Cologne, 50935 Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Clara Lehmann
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Gerd Fätkenheuer
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany
| | - Michael S Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - Pamela J Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Florian Klein
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne, 50931 Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany.
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Atkinson JT, Jones AM, Nanda V, Silberg JJ. Protein tolerance to random circular permutation correlates with thermostability and local energetics of residue-residue contacts. Protein Eng Des Sel 2019; 32:489-501. [PMID: 32626892 PMCID: PMC7462040 DOI: 10.1093/protein/gzaa012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/13/2020] [Accepted: 04/15/2020] [Indexed: 01/08/2023] Open
Abstract
Adenylate kinase (AK) orthologs with a range of thermostabilities were subjected to random circular permutation, and deep mutational scanning was used to evaluate where new protein termini were nondisruptive to activity. The fraction of circularly permuted variants that retained function in each library correlated with AK thermostability. In addition, analysis of the positional tolerance to new termini, which increase local conformational flexibility, showed that bonds were either functionally sensitive to cleavage across all homologs, differentially sensitive, or uniformly tolerant. The mobile AMP-binding domain, which displays the highest calculated contact energies, presented the greatest tolerance to new termini across all AKs. In contrast, retention of function in the lid and core domains was more dependent upon AK melting temperature. These results show that family permutation profiling identifies primary structure that has been selected by evolution for dynamics that are critical to activity within an enzyme family. These findings also illustrate how deep mutational scanning can be applied to protein homologs in parallel to differentiate how topology, stability, and local energetics govern mutational tolerance.
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Affiliation(s)
- Joshua T Atkinson
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, 6100 Main Street, MS-180, Houston, TX 77005, USA
- Department of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA
| | - Alicia M Jones
- Biochemistry and Cell Biology Graduate Program, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jonathan J Silberg
- Department of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA
- Department of Bioengineering, Rice University, 6100 Main Street, MS-142, Houston, TX 77005, USA
- Department of Chemical and Biomolecular Engineering, Rice University, 6100 Main Street, MS-362, Houston, TX 77005, USA
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