1
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Yan Q, Gao X, Liu B, Hou R, He P, Ma Y, Zhang Y, Zhang Y, Li Z, Chen Q, Wang J, Huang X, Liang H, Zheng H, Yao Y, Chen X, Niu X, He J, Chen L, Zhao J, Xiong X. Antibodies utilizing VL6-57 light chains target a convergent cryptic epitope on SARS-CoV-2 spike protein and potentially drive the genesis of Omicron variants. Nat Commun 2024; 15:7585. [PMID: 39217172 PMCID: PMC11366018 DOI: 10.1038/s41467-024-51770-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 08/17/2024] [Indexed: 09/04/2024] Open
Abstract
Continued evolution of SARS-CoV-2 generates variants to challenge antibody immunity established by infection and vaccination. A connection between population immunity and genesis of virus variants has long been suggested but its molecular basis remains poorly understood. Here, we identify a class of SARS-CoV-2 neutralizing public antibodies defined by their shared usage of VL6-57 light chains. Although heavy chains of diverse genotypes are utilized, convergent HCDR3 rearrangements have been observed among these public antibodies to cooperate with germline VL6-57 LCDRs to target a convergent epitope defined by RBD residues S371-S373-S375. Antibody repertoire analysis identifies that this class of VL6-57 antibodies is present in SARS-CoV-2-naive individuals and is clonally expanded in most COVID-19 patients. We confirm that Omicron-specific substitutions at S371, S373 and S375 mediate escape of antibodies of the VL6-57 class. These findings support that this class of public antibodies constitutes a potential immune pressure promoting the introduction of S371L/F-S373P-S375F in Omicron variants. The results provide further molecular evidence to support that antigenic evolution of SARS-CoV-2 is driven by antibody mediated population immunity.
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Affiliation(s)
- Qihong Yan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xijie Gao
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Banghui Liu
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ruitian Hou
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Ping He
- Guangzhou National Laboratory, Guangzhou, China
| | - Yong Ma
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yudi Zhang
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yanjun Zhang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zimu Li
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Qiuluan Chen
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health - Guangdong Laboratory), Guangzhou, China
| | - Jingjing Wang
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xiaohan Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huan Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huiran Zheng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yichen Yao
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xianying Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xuefeng Niu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jun He
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
| | - Ling Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
- Guangzhou National Laboratory, Guangzhou, China.
| | - Jincun Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Guangzhou National Laboratory, Guangzhou, China.
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Xiaoli Xiong
- State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
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2
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Cohen AA, Keeffe JR, Schiepers A, Dross SE, Greaney AJ, Rorick AV, Gao H, Gnanapragasam PNP, Fan C, West AP, Ramsingh AI, Erasmus JH, Pata JD, Muramatsu H, Pardi N, Lin PJC, Baxter S, Cruz R, Quintanar-Audelo M, Robb E, Serrano-Amatriain C, Magneschi L, Fotheringham IG, Fuller DH, Victora GD, Bjorkman PJ. Mosaic sarbecovirus nanoparticles elicit cross-reactive responses in pre-vaccinated animals. Cell 2024:S0092-8674(24)00846-8. [PMID: 39197450 DOI: 10.1016/j.cell.2024.07.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/15/2024] [Accepted: 07/27/2024] [Indexed: 09/01/2024]
Abstract
Immunization with mosaic-8b (nanoparticles presenting 8 SARS-like betacoronavirus [sarbecovirus] receptor-binding domains [RBDs]) elicits more broadly cross-reactive antibodies than homotypic SARS-CoV-2 RBD-only nanoparticles and protects against sarbecoviruses. To investigate original antigenic sin (OAS) effects on mosaic-8b efficacy, we evaluated the effects of prior COVID-19 vaccinations in non-human primates and mice on anti-sarbecovirus responses elicited by mosaic-8b, admix-8b (8 homotypics), or homotypic SARS-CoV-2 immunizations, finding the greatest cross-reactivity for mosaic-8b. As demonstrated by molecular fate mapping, in which antibodies from specific cohorts of B cells are differentially detected, B cells primed by WA1 spike mRNA-LNP dominated antibody responses after RBD-nanoparticle boosting. While mosaic-8b- and homotypic-nanoparticles boosted cross-reactive antibodies, de novo antibodies were predominantly induced by mosaic-8b, and these were specific for variant RBDs with increased identity to RBDs on mosaic-8b. These results inform OAS mechanisms and support using mosaic-8b to protect COVID-19-vaccinated/infected humans against as-yet-unknown SARS-CoV-2 variants and animal sarbecoviruses with human spillover potential.
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Affiliation(s)
- Alexander A Cohen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Jennifer R Keeffe
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ariën Schiepers
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY 10065, USA
| | - Sandra E Dross
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA; National Primate Research Center, Seattle, WA 98121, USA
| | - Allison J Greaney
- Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA
| | - Annie V Rorick
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Han Gao
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Chengcheng Fan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Anthony P West
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | | | - Janice D Pata
- Wadsworth Center, New York State Department of Health and Department of Biomedical Sciences, University at Albany, Albany, NY 12201, USA
| | - Hiromi Muramatsu
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Norbert Pardi
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Scott Baxter
- Ingenza Ltd., Roslin Innovation Centre, Charnock Bradley Building, Roslin EH25 9RG, UK
| | - Rita Cruz
- Ingenza Ltd., Roslin Innovation Centre, Charnock Bradley Building, Roslin EH25 9RG, UK
| | | | - Ellis Robb
- Ingenza Ltd., Roslin Innovation Centre, Charnock Bradley Building, Roslin EH25 9RG, UK
| | | | - Leonardo Magneschi
- Ingenza Ltd., Roslin Innovation Centre, Charnock Bradley Building, Roslin EH25 9RG, UK
| | - Ian G Fotheringham
- Ingenza Ltd., Roslin Innovation Centre, Charnock Bradley Building, Roslin EH25 9RG, UK
| | - Deborah H Fuller
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA; National Primate Research Center, Seattle, WA 98121, USA
| | - Gabriel D Victora
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY 10065, USA
| | - Pamela J Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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3
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Innocenti G, Obara M, Costa B, Jacobsen H, Katzmarzyk M, Cicin-Sain L, Kalinke U, Galardini M. Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections. Genome Biol 2024; 25:228. [PMID: 39175058 PMCID: PMC11342480 DOI: 10.1186/s13059-024-03355-y] [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: 05/02/2024] [Accepted: 07/26/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND The emergence of the SARS-CoV-2 virus has highlighted the importance of genomic epidemiology in understanding the evolution of pathogens and guiding public health interventions. The Omicron variant in particular has underscored the role of epistasis in the evolution of lineages with both higher infectivity and immune escape, and therefore the necessity to update surveillance pipelines to detect them early on. RESULTS In this study, we apply a method based on mutual information between positions in a multiple sequence alignment, which is capable of scaling up to millions of samples. We show how it can reliably predict known experimentally validated epistatic interactions, even when using as little as 10,000 sequences, which opens the possibility of making it a near real-time prediction system. We test this possibility by modifying the method to account for the sample collection date and apply it retrospectively to multiple sequence alignments for each month between March 2020 and March 2023. We detected a cornerstone epistatic interaction in the Spike protein between codons 498 and 501 as soon as seven samples with a double mutation were present in the dataset, thus demonstrating the method's sensitivity. We test the ability of the method to make inferences about emerging interactions by testing candidates predicted after March 2023, which we validate experimentally. CONCLUSIONS We show how known epistatic interaction in SARS-CoV-2 can be detected with high sensitivity, and how emerging ones can be quickly prioritized for experimental validation, an approach that could be implemented downstream of pandemic genome sequencing efforts.
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Affiliation(s)
- Gabriel Innocenti
- Institute for Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School (MHH), Hannover, Germany
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Maureen Obara
- Institute for Experimental Infection Research, TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany
| | - Bibiana Costa
- Institute for Experimental Infection Research, TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany
| | - Henning Jacobsen
- Helmholtz Centre for Infection Research, Department of Viral Immunology (VIRI), Brunswick, Germany
- Centre for Individualized Infection Medicine (CiiM) a Joint Venture of Helmholtz Centre for Infection Research and Hannover Medical School, Hannover, Germany
| | - Maeva Katzmarzyk
- Helmholtz Centre for Infection Research, Department of Viral Immunology (VIRI), Brunswick, Germany
- Centre for Individualized Infection Medicine (CiiM) a Joint Venture of Helmholtz Centre for Infection Research and Hannover Medical School, Hannover, Germany
| | - Luka Cicin-Sain
- Helmholtz Centre for Infection Research, Department of Viral Immunology (VIRI), Brunswick, Germany
- Centre for Individualized Infection Medicine (CiiM) a Joint Venture of Helmholtz Centre for Infection Research and Hannover Medical School, Hannover, Germany
| | - Ulrich Kalinke
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School (MHH), Hannover, Germany
- Institute for Experimental Infection Research, TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany
| | - Marco Galardini
- Institute for Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany.
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School (MHH), Hannover, Germany.
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4
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Focosi D, Spezia PG, Maggi F. Subsequent Waves of Convergent Evolution in SARS-CoV-2 Genes and Proteins. Vaccines (Basel) 2024; 12:887. [PMID: 39204013 PMCID: PMC11358953 DOI: 10.3390/vaccines12080887] [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: 07/20/2024] [Revised: 08/02/2024] [Accepted: 08/03/2024] [Indexed: 09/03/2024] Open
Abstract
Beginning in 2022, following widespread infection and vaccination among the global population, the SARS-CoV-2 virus mainly evolved to evade immunity derived from vaccines and past infections. This review covers the convergent evolution of structural, nonstructural, and accessory proteins in SARS-CoV-2, with a specific look at common mutations found in long-lasting infections that hint at the virus potentially reverting to an enteric sarbecovirus type.
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Affiliation(s)
- Daniele Focosi
- North-Western Tuscany Blood Bank, Pisa University Hospital, 56124 Pisa, Italy;
| | - Pietro Giorgio Spezia
- Laboratory of Virology and Laboratory of Biosecurity, National Institute of Infectious Diseases Lazzaro Spallanzani—IRCCS, 00149 Rome, Italy;
| | - Fabrizio Maggi
- Laboratory of Virology and Laboratory of Biosecurity, National Institute of Infectious Diseases Lazzaro Spallanzani—IRCCS, 00149 Rome, Italy;
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5
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Dadonaite B, Ahn JJ, Ort JT, Yu J, Furey C, Dosey A, Hannon WW, Baker AV, Webby RJ, King NP, Liu Y, Hensley SE, Peacock TP, Moncla LH, Bloom JD. Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595634. [PMID: 38826368 PMCID: PMC11142178 DOI: 10.1101/2024.05.23.595634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
H5 influenza is a potential pandemic threat. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic risk, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. Here we use pseudovirus deep mutational scanning to measure how all mutations to a clade 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind a2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also identify recent viral strains with reduced neutralization to sera elicited by candidate vaccine virus. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive characterization of mutations to inform surveillance of H5 influenza.
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6
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Taylor AL, Starr TN. Deep mutational scanning of SARS-CoV-2 Omicron BA.2.86 and epistatic emergence of the KP.3 variant. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604853. [PMID: 39091888 PMCID: PMC11291116 DOI: 10.1101/2024.07.23.604853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Deep mutational scanning experiments aid in the surveillance and forecasting of viral evolution by providing prospective measurements of mutational effects on viral traits, but epistatic shifts in the impacts of mutations can hinder viral forecasting when measurements were made in outdated strain backgrounds. Here, we report measurements of the impact of all single amino acid mutations on ACE2-binding affinity and protein folding and expression in the SARS-CoV-2 Omicron BA.2.86 spike receptor-binding domain (RBD). As with other SARS-CoV-2 variants, we find a plastic and evolvable basis for receptor binding, with many mutations at the ACE2 interface maintaining or even improving ACE2-binding affinity. Despite its large genetic divergence, mutational effects in BA.2.86 have not diverged greatly from those measured in its Omicron BA.2 ancestor. However, we do identify strong positive epistasis among subsequent mutations that have accrued in BA.2.86 descendants. Specifically, the Q493E mutation that decreased ACE2-binding affinity in all previous SARS-CoV-2 backgrounds is reversed in sign to enhance human ACE2-binding affinity when coupled with L455S and F456L in the currently emerging KP.3 variant. Our results point to a modest degree of epistatic drift in mutational effects during recent SARS-CoV-2 evolution but highlight how these small epistatic shifts can have important consequences for the emergence of new SARS-CoV-2 variants.
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Affiliation(s)
- Ashley L. Taylor
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Tyler N. Starr
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
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7
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Li P, Liu ZP. MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402918. [PMID: 38995072 DOI: 10.1002/advs.202402918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/04/2024] [Indexed: 07/13/2024]
Abstract
Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.
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Affiliation(s)
- Pengpai Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
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8
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Raisinghani N, Alshahrani M, Gupta G, Verkhivker G. Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602810. [PMID: 39026832 PMCID: PMC11257589 DOI: 10.1101/2024.07.09.602810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.
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9
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Shanker VR, Bruun TUJ, Hie BL, Kim PS. Unsupervised evolution of protein and antibody complexes with a structure-informed language model. Science 2024; 385:46-53. [PMID: 38963838 DOI: 10.1126/science.adk8946] [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: 09/18/2023] [Accepted: 05/29/2024] [Indexed: 07/06/2024]
Abstract
Large language models trained on sequence information alone can learn high-level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here, we show that a general protein language model augmented with protein structure backbone coordinates can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.
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Affiliation(s)
- Varun R Shanker
- Stanford Biophysics Program, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Theodora U J Bruun
- Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Brian L Hie
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peter S Kim
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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10
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Dadonaite B, Brown J, McMahon TE, Farrell AG, Figgins MD, Asarnow D, Stewart C, Lee J, Logue J, Bedford T, Murrell B, Chu HY, Veesler D, Bloom JD. Spike deep mutational scanning helps predict success of SARS-CoV-2 clades. Nature 2024; 631:617-626. [PMID: 38961298 PMCID: PMC11254757 DOI: 10.1038/s41586-024-07636-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 05/31/2024] [Indexed: 07/05/2024]
Abstract
SARS-CoV-2 variants acquire mutations in the spike protein that promote immune evasion1 and affect other properties that contribute to viral fitness, such as ACE2 receptor binding and cell entry2,3. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning4 to measure how more than 9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully affected ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456 and 473; however, the antigenic effects of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however, many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.
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Affiliation(s)
- Bernadeta Dadonaite
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jack Brown
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Teagan E McMahon
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ariana G Farrell
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Marlin D Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Daniel Asarnow
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Cameron Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Jimin Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Jenni Logue
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Ben Murrell
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Helen Y Chu
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, 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.
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11
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Yu G, Zhao Q, Bi X, Wang J. DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure. Bioinformatics 2024; 40:i418-i427. [PMID: 38940145 PMCID: PMC11211828 DOI: 10.1093/bioinformatics/btae232] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein-protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations. RESULTS To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity. AVAILABILITY AND IMPLEMENTATION DDAffinity is available at https://github.com/ak422/DDAffinity.
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Affiliation(s)
- Guanglei Yu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi 830017, China
| | - Qichang Zhao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Xuehua Bi
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi 830017, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
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12
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations. Phys Chem Chem Phys 2024; 26:17720-17744. [PMID: 38869513 DOI: 10.1039/d4cp01372g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas, 75275, USA
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
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13
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Ives CM, Nguyen L, Fogarty CA, Harbison AM, Durocher Y, Klassen J, Fadda E. Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern. eLife 2024; 13:RP95708. [PMID: 38864493 PMCID: PMC11168744 DOI: 10.7554/elife.95708] [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: 06/13/2024] Open
Abstract
Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan's structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.
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Affiliation(s)
- Callum M Ives
- Department of Chemistry, Maynooth UniversityMaynoothIreland
| | - Linh Nguyen
- Department of Chemistry, University of AlbertaEdmontonCanada
| | - Carl A Fogarty
- Department of Chemistry, Maynooth UniversityMaynoothIreland
| | | | - Yves Durocher
- Human Health Therapeutics Research Centre, Life Sciences Division, National Research Council CanadaQuébecCanada
- Département de Biochimie et Médecine Moléculaire, Université de MontréalQuébecCanada
| | - John Klassen
- Department of Chemistry, University of AlbertaEdmontonCanada
| | - Elisa Fadda
- School of Biological Sciences, University of SouthamptonSouthamptonUnited Kingdom
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14
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Rojas Chávez RA, Fili M, Han C, Rahman SA, Bicar IGL, Gregory S, Helverson A, Hu G, Darbro BW, Das J, Brown GD, Haim H. Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics. PLoS Comput Biol 2024; 20:e1012215. [PMID: 38857308 PMCID: PMC11192331 DOI: 10.1371/journal.pcbi.1012215] [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: 07/06/2023] [Revised: 06/21/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024] Open
Abstract
New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.
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Affiliation(s)
| | - Mohammad Fili
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa, United States of America
| | - Changze Han
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, Iowa, United States of America
| | - Syed A. Rahman
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Isaiah G. L. Bicar
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, Iowa, United States of America
| | - Sullivan Gregory
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, Iowa, United States of America
| | - Annika Helverson
- Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, Iowa, United States of America
| | - Guiping Hu
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa, United States of America
| | - Benjamin W. Darbro
- Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States of America
| | - Jishnu Das
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Grant D. Brown
- Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, Iowa, United States of America
| | - Hillel Haim
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, Iowa, United States of America
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15
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Xue S, Han Y, Wu F, Wang Q. Mutations in the SARS-CoV-2 spike receptor binding domain and their delicate balance between ACE2 affinity and antibody evasion. Protein Cell 2024; 15:403-418. [PMID: 38442025 PMCID: PMC11131022 DOI: 10.1093/procel/pwae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Abstract
Intensive selection pressure constrains the evolutionary trajectory of SARS-CoV-2 genomes and results in various novel variants with distinct mutation profiles. Point mutations, particularly those within the receptor binding domain (RBD) of SARS-CoV-2 spike (S) protein, lead to the functional alteration in both receptor engagement and monoclonal antibody (mAb) recognition. Here, we review the data of the RBD point mutations possessed by major SARS-CoV-2 variants and discuss their individual effects on ACE2 affinity and immune evasion. Many single amino acid substitutions within RBD epitopes crucial for the antibody evasion capacity may conversely weaken ACE2 binding affinity. However, this weakened effect could be largely compensated by specific epistatic mutations, such as N501Y, thus maintaining the overall ACE2 affinity for the spike protein of all major variants. The predominant direction of SARS-CoV-2 evolution lies neither in promoting ACE2 affinity nor evading mAb neutralization but in maintaining a delicate balance between these two dimensions. Together, this review interprets how RBD mutations efficiently resist antibody neutralization and meanwhile how the affinity between ACE2 and spike protein is maintained, emphasizing the significance of comprehensive assessment of spike mutations.
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Affiliation(s)
- Song Xue
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yuru Han
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Fan Wu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Qiao Wang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
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16
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Gonzales J, Kim I, Hwang W, Cho JH. Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of influenza A virus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595776. [PMID: 38826371 PMCID: PMC11142230 DOI: 10.1101/2024.05.24.595776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Viral proteins frequently mutate to evade or antagonize host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present the deep analysis for the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveal how the mutations rewired inter-residue communications which underlies long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects. Notably, these mutations also exhibited long-range epistatic effects. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, enhancing its affinity to p85β. Complementary MD simulations and graph-based network analysis uncover how these mutations rewire dynamic residue interaction networks, which underlies the long-range epistasis and allosteric effects on p85β-binding affinity. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provides insight into their role in the molecular evolution of NS1.
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17
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Cohen AA, Keeffe JR, Schiepers A, Dross SE, Greaney AJ, Rorick AV, Gao H, Gnanapragasam PN, Fan C, West AP, Ramsingh AI, Erasmus JH, Pata JD, Muramatsu H, Pardi N, Lin PJ, Baxter S, Cruz R, Quintanar-Audelo M, Robb E, Serrano-Amatriain C, Magneschi L, Fotheringham IG, Fuller DH, Victora GD, Bjorkman PJ. Mosaic sarbecovirus nanoparticles elicit cross-reactive responses in pre-vaccinated animals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.576722. [PMID: 38370696 PMCID: PMC10871317 DOI: 10.1101/2024.02.08.576722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Immunization with mosaic-8b [60-mer nanoparticles presenting 8 SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs)] elicits more broadly cross-reactive antibodies than homotypic SARS-CoV-2 RBD-only nanoparticles and protects against sarbecoviruses. To investigate original antigenic sin (OAS) effects on mosaic-8b efficacy, we evaluated effects of prior COVID-19 vaccinations in non-human primates and mice on anti-sarbecovirus responses elicited by mosaic-8b, admix-8b (8 homotypics), or homotypic SARS-CoV-2 immunizations, finding greatest cross-reactivity for mosaic-8b. As demonstrated by molecular fate-mapping in which antibodies from specific cohorts of B cells are differentially detected, B cells primed by WA1 spike mRNA-LNP dominated antibody responses after RBD-nanoparticle boosting. While mosaic-8b- and homotypic-nanoparticles boosted cross-reactive antibodies, de novo antibodies were predominantly induced by mosaic-8b, and these were specific for variant RBDs with increased identity to RBDs on mosaic-8b. These results inform OAS mechanisms and support using mosaic-8b to protect COVID-19 vaccinated/infected humans against as-yet-unknown SARS-CoV-2 variants and animal sarbecoviruses with human spillover potential.
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Affiliation(s)
- Alexander A. Cohen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- These authors contributed equally
| | - Jennifer R. Keeffe
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- These authors contributed equally
| | - Ariën Schiepers
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, 10065, USA
| | - Sandra E. Dross
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA
- National Primate Research Center, Seattle, WA 98121, USA
| | - Allison J. Greaney
- Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA
| | - Annie V. Rorick
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Han Gao
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Chengcheng Fan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Anthony P. West
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | | | - Janice D. Pata
- Wadsworth Center, New York State Department of Health and Department of Biomedical Sciences, University at Albany, Albany, NY, 12201, USA
| | - Hiromi Muramatsu
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Norbert Pardi
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Scott Baxter
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
| | - Rita Cruz
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
| | - Martina Quintanar-Audelo
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
- Present address: Centre for Inflammation Research and Institute of Regeneration and Repair, The University of Edinburgh, Edinburgh, EH16 4UU, UK
| | - Ellis Robb
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
| | | | - Leonardo Magneschi
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
| | - Ian G. Fotheringham
- Ingenza Ltd, Roslin Innovation Centre, Charnock Bradley Building, Roslin, EH25 9RG, UK
| | - Deborah H. Fuller
- Department of Microbiology, University of Washington, Seattle, WA 98195, USA
- National Primate Research Center, Seattle, WA 98121, USA
| | - Gabriel D. Victora
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, 10065, USA
| | - Pamela J. Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Lead contact
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18
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity. J Phys Chem B 2024; 128:4696-4715. [PMID: 38696745 DOI: 10.1021/acs.jpcb.4c01341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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19
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Ketaren NE, Mast FD, Fridy PC, Olivier JP, Sanyal T, Sali A, Chait BT, Rout MP, Aitchison JD. Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus. eLife 2024; 12:RP89423. [PMID: 38712823 PMCID: PMC11076045 DOI: 10.7554/elife.89423] [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: 05/08/2024] Open
Abstract
To date, all major modes of monoclonal antibody therapy targeting SARS-CoV-2 have lost significant efficacy against the latest circulating variants. As SARS-CoV-2 omicron sublineages account for over 90% of COVID-19 infections, evasion of immune responses generated by vaccination or exposure to previous variants poses a significant challenge. A compelling new therapeutic strategy against SARS-CoV-2 is that of single-domain antibodies, termed nanobodies, which address certain limitations of monoclonal antibodies. Here, we demonstrate that our high-affinity nanobody repertoire, generated against wild-type SARS-CoV-2 spike protein (Mast et al., 2021), remains effective against variants of concern, including omicron BA.4/BA.5; a subset is predicted to counter resistance in emerging XBB and BQ.1.1 sublineages. Furthermore, we reveal the synergistic potential of nanobody cocktails in neutralizing emerging variants. Our study highlights the power of nanobody technology as a versatile therapeutic and diagnostic tool to combat rapidly evolving infectious diseases such as SARS-CoV-2.
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Affiliation(s)
- Natalia E Ketaren
- Laboratory of Cellular and Structural Biology, The Rockefeller UniversityNew YorkUnited States
| | - Fred D Mast
- Center for Global Infectious Disease Research, Seattle Children's Research InstituteSeattleUnited States
| | - Peter C Fridy
- Laboratory of Cellular and Structural Biology, The Rockefeller UniversityNew YorkUnited States
| | - Jean Paul Olivier
- Center for Global Infectious Disease Research, Seattle Children's Research InstituteSeattleUnited States
| | - Tanmoy Sanyal
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, University of California, San FranciscoSan FranciscoUnited States
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, University of California, San FranciscoSan FranciscoUnited States
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller UniversityNew YorkUnited States
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller UniversityNew YorkUnited States
| | - John D Aitchison
- Center for Global Infectious Disease Research, Seattle Children's Research InstituteSeattleUnited States
- Department of Pediatrics, University of WashingtonSeattleUnited States
- Department of Biochemistry, University of WashingtonSeattleUnited States
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20
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Ose NJ, Campitelli P, Modi T, Kazan IC, Kumar S, Ozkan SB. Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics. eLife 2024; 12:RP92063. [PMID: 38713502 PMCID: PMC11076047 DOI: 10.7554/elife.92063] [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: 05/08/2024] Open
Abstract
We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.
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Affiliation(s)
- Nicholas James Ose
- Department of Physics and Center for Biological Physics, Arizona State UniversityTempeUnited States
| | - Paul Campitelli
- Department of Physics and Center for Biological Physics, Arizona State UniversityTempeUnited States
| | - Tushar Modi
- Department of Physics and Center for Biological Physics, Arizona State UniversityTempeUnited States
| | - I Can Kazan
- Department of Physics and Center for Biological Physics, Arizona State UniversityTempeUnited States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple UniversityPhiladelphiaUnited States
- Department of Biology, Temple UniversityPhiladelphiaUnited States
- Center for Genomic Medicine Research, King Abdulaziz UniversityJeddahSaudi Arabia
| | - Sefika Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State UniversityTempeUnited States
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21
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Shukla N, Roelle SM, Snell JC, DelSignore O, Bruchez AM, Matreyek KA. Pseudotyped virus infection of multiplexed ACE2 libraries reveals SARS-CoV-2 variant shifts in receptor usage. PLoS Pathog 2024; 20:e1012044. [PMID: 38768238 PMCID: PMC11142672 DOI: 10.1371/journal.ppat.1012044] [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: 02/12/2024] [Revised: 05/31/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024] Open
Abstract
Pairwise compatibility between virus and host proteins can dictate the outcome of infection. During transmission, both inter- and intraspecies variabilities in receptor protein sequences can impact cell susceptibility. Many viruses possess mutable viral entry proteins and the patterns of host compatibility can shift as the viral protein sequence changes. This combinatorial sequence space between virus and host is poorly understood, as traditional experimental approaches lack the throughput to simultaneously test all possible combinations of protein sequences. Here, we created a pseudotyped virus infection assay where a multiplexed target-cell library of host receptor variants can be assayed simultaneously using a DNA barcode sequencing readout. We applied this assay to test a panel of 30 ACE2 orthologs or human sequence mutants for infectability by the original SARS-CoV-2 spike protein or the Alpha, Beta, Gamma, Delta, and Omicron BA1 variant spikes. We compared these results to an analysis of the structural shifts that occurred for each variant spike's interface with human ACE2. Mutated residues were directly involved in the largest shifts, although there were also widespread indirect effects altering interface structure. The N501Y substitution in spike conferred a large structural shift for interaction with ACE2, which was partially recreated by indirect distal substitutions in Delta, which does not harbor N501Y. The structural shifts from N501Y greatly influenced the set of animal orthologs the variant spike was capable of interacting with. Out of the thirteen non-human orthologs, ten exhibited unique patterns of variant-specific compatibility, demonstrating that spike sequence changes during human transmission can toggle ACE2 compatibility and potential susceptibility of other animal species, and cumulatively increase overall compatibilities as new variants emerge. These experiments provide a blueprint for similar large-scale assessments of protein compatibility during entry by diverse viruses. This dataset demonstrates the complex compatibility relationships that occur between variable interacting host and virus proteins.
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Affiliation(s)
- Nidhi Shukla
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Sarah M. Roelle
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - John C. Snell
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Olivia DelSignore
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Anna M. Bruchez
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Kenneth A. Matreyek
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
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22
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Iketani S, Ho DD. SARS-CoV-2 resistance to monoclonal antibodies and small-molecule drugs. Cell Chem Biol 2024; 31:632-657. [PMID: 38640902 PMCID: PMC11084874 DOI: 10.1016/j.chembiol.2024.03.008] [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: 09/07/2023] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
Over four years have passed since the beginning of the COVID-19 pandemic. The scientific response has been rapid and effective, with many therapeutic monoclonal antibodies and small molecules developed for clinical use. However, given the ability for viruses to become resistant to antivirals, it is perhaps no surprise that the field has identified resistance to nearly all of these compounds. Here, we provide a comprehensive review of the resistance profile for each of these therapeutics. We hope that this resource provides an atlas for mutations to be aware of for each agent, particularly as a springboard for considerations for the next generation of antivirals. Finally, we discuss the outlook and thoughts for moving forward in how we continue to manage this, and the next, pandemic.
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Affiliation(s)
- Sho Iketani
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; Division of Infectious Diseases, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - David D Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; Division of Infectious Diseases, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
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23
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Wagner C, Kistler KE, Perchetti GA, Baker N, Frisbie LA, Torres LM, Aragona F, Yun C, Figgins M, Greninger AL, Cox A, Oltean HN, Roychoudhury P, Bedford T. Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution. Nat Commun 2024; 15:3207. [PMID: 38615031 PMCID: PMC11016114 DOI: 10.1038/s41467-024-47599-5] [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: 09/27/2023] [Accepted: 04/04/2024] [Indexed: 04/15/2024] Open
Abstract
Knockout of the ORF8 protein has repeatedly spread through the global viral population during SARS-CoV-2 evolution. Here we use both regional and global pathogen sequencing to explore the selection pressures underlying its loss. In Washington State, we identified transmission clusters with ORF8 knockout throughout SARS-CoV-2 evolution, not just on novel, high fitness viral backbones. Indeed, ORF8 is truncated more frequently and knockouts circulate for longer than for any other gene. Using a global phylogeny, we find evidence of positive selection to explain this phenomenon: nonsense mutations resulting in shortened protein products occur more frequently and are associated with faster clade growth rates than synonymous mutations in ORF8. Loss of ORF8 is also associated with reduced clinical severity, highlighting the diverse clinical impacts of SARS-CoV-2 evolution.
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Affiliation(s)
- Cassia Wagner
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Kathryn E Kistler
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Garrett A Perchetti
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Noah Baker
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | | | - Frank Aragona
- Washington State Department of Health, Shoreline, WA, USA
| | - Cory Yun
- Washington State Department of Health, Shoreline, WA, USA
| | - Marlin Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Alexander L Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Alex Cox
- Washington State Department of Health, Shoreline, WA, USA
| | - Hanna N Oltean
- Washington State Department of Health, Shoreline, WA, USA
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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24
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Raisinghani N, Alshahrani M, Gupta G, Verkhivker G. Ensemble-Based Mutational Profiling and Network Analysis of the SARS-CoV-2 Spike Omicron XBB Lineages for Interactions with the ACE2 Receptor and Antibodies: Cooperation of Binding Hotspots in Mediating Epistatic Couplings Underlies Binding Mechanism and Immune Escape. Int J Mol Sci 2024; 25:4281. [PMID: 38673865 PMCID: PMC11049863 DOI: 10.3390/ijms25084281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (N.R.); (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
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25
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587850. [PMID: 38617283 PMCID: PMC11014522 DOI: 10.1101/2024.04.02.587850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles and binding mechanisms of convergent evolution for the SARS-CoV-2 Spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron Spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamic simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron Spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and molecular dynamics simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and molecular dynamics simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.
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26
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Li W, Xu Z, Niu T, Xie Y, Zhao Z, Li D, He Q, Sun W, Shi K, Guo W, Chang Z, Liu K, Fan Z, Qi J, Gao GF. Key mechanistic features of the trade-off between antibody escape and host cell binding in the SARS-CoV-2 Omicron variant spike proteins. EMBO J 2024; 43:1484-1498. [PMID: 38467833 PMCID: PMC11021471 DOI: 10.1038/s44318-024-00062-z] [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: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 03/13/2024] Open
Abstract
Since SARS-CoV-2 Omicron variant emerged, it is constantly evolving into multiple sub-variants, including BF.7, BQ.1, BQ.1.1, XBB, XBB.1.5 and the recently emerged BA.2.86 and JN.1. Receptor binding and immune evasion are recognized as two major drivers for evolution of the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) protein. However, the underlying mechanism of interplay between two factors remains incompletely understood. Herein, we determined the structures of human ACE2 complexed with BF.7, BQ.1, BQ.1.1, XBB and XBB.1.5 RBDs. Based on the ACE2/RBD structures of these sub-variants and a comparison with the known complex structures, we found that R346T substitution in the RBD enhanced ACE2 binding upon an interaction with the residue R493, but not Q493, via a mechanism involving long-range conformation changes. Furthermore, we found that R493Q and F486V exert a balanced impact, through which immune evasion capability was somewhat compromised to achieve an optimal receptor binding. We propose a "two-steps-forward and one-step-backward" model to describe such a compromise between receptor binding affinity and immune evasion during RBD evolution of Omicron sub-variants.
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Affiliation(s)
- Weiwei Li
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zepeng Xu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
- Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Tianhui Niu
- Air Force Medical University, Air Force Medical center, PLA, Beijing, China
| | - Yufeng Xie
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Zhennan Zhao
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Dedong Li
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Qingwen He
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Wenqiao Sun
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Kaiyuan Shi
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Wenjing Guo
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhen Chang
- Department of Pathogen Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Kefang Liu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China
| | - Zheng Fan
- Institutional Core Facility, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China.
| | - Jianxun Qi
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - George F Gao
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, China.
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27
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Zhang Q, Pavlinov I, Ye Y, Zheng W. Therapeutic development targeting host heparan sulfate proteoglycan in SARS-CoV-2 infection. Front Med (Lausanne) 2024; 11:1364657. [PMID: 38618194 PMCID: PMC11014733 DOI: 10.3389/fmed.2024.1364657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/18/2024] [Indexed: 04/16/2024] Open
Abstract
The global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an urgent need for effective therapeutic options. SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic that has resulted in significant morbidity and mortality worldwide. The virus is known to enter host cells by binding to the angiotensin-converting enzyme 2 (ACE2) receptor, and emerging evidence suggests that heparan sulfate proteoglycans (HSPGs) play a crucial role in facilitating this process. HSPGs are abundant cell surface proteoglycan present in many tissues, including the lung, and have been shown to interact directly with the spike protein of SARS-CoV-2. This review aims to summarize the current understanding of the role of HSPGs in SARS-CoV-2 infection and the potential of developing new therapies targeting HSPGs.
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Affiliation(s)
- Qi Zhang
- Therapeutic Development Branch, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
| | - Ivan Pavlinov
- Therapeutic Development Branch, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
| | - Yihong Ye
- Laboratory of Molecular Biology, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Wei Zheng
- Therapeutic Development Branch, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
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28
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Maurer DP, Vu M, Schmidt AG. Antigenic drift expands viral escape pathways from imprinted host humoral immunity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585891. [PMID: 38562862 PMCID: PMC10983950 DOI: 10.1101/2024.03.20.585891] [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
An initial virus exposure can imprint antibodies such that future responses to antigenically drifted strains are dependent on the identity of the imprinting strain. Subsequent exposure to antigenically distinct strains followed by affinity maturation can guide immune responses toward generation of cross-reactive antibodies. How viruses evolve in turn to escape these imprinted broad antibody responses is unclear. Here, we used clonal antibody lineages from two human donors recognizing conserved influenza virus hemagglutinin (HA) epitopes to assess viral escape potential using deep mutational scanning. We show that even though antibody affinity maturation does restrict the number of potential escape routes in the imprinting strain through repositioning the antibody variable domains, escape is still readily observed in drifted strains and attributed to epistatic networks within HA. These data explain how influenza virus continues to evolve in the human population by escaping even broad antibody responses.
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29
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Sohail MS, Ahmed SF, Quadeer AA, McKay MR. Cross-Reactivity Assessment of Vaccine-Derived SARS-CoV-2 T Cell Responses against BA.2.86 and JN.1. Viruses 2024; 16:473. [PMID: 38543838 PMCID: PMC10975570 DOI: 10.3390/v16030473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/15/2024] [Indexed: 05/23/2024] Open
Abstract
The SARS-CoV-2 Omicron sub-variants BA.2.86 and JN.1 contain multiple mutations in the spike protein that were not present in previous variants of concern and Omicron sub-variants. Preliminary research suggests that these variants reduce the neutralizing capability of antibodies induced by vaccines, which is particularly significant for JN.1. This raises concern as many widely deployed COVID-19 vaccines are based on the spike protein of the ancestral Wuhan strain of SARS-CoV-2. While T cell responses have been shown to be robust against previous SARS-CoV-2 variants, less is known about the impact of mutations in BA.2.86 and JN.1 on T cell responses. We evaluate the effect of mutations specific to BA.2.86 and JN.1 on experimentally determined T cell epitopes derived from the spike protein of the ancestral Wuhan strain and the spike protein of the XBB.1.5 strain that has been recommended as a booster vaccine. Our data suggest that BA.2.86 and JN.1 affect numerous T cell epitopes in spike compared to previous variants; however, the widespread loss of T cell recognition against these variants is unlikely.
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Affiliation(s)
- Muhammad Saqib Sohail
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China;
| | - Syed Faraz Ahmed
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia;
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
| | - Ahmed Abdul Quadeer
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Matthew R. McKay
- Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia;
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3000, Australia
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30
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Case M, Smith M, Vinh J, Thurber G. Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space. Proc Natl Acad Sci U S A 2024; 121:e2311726121. [PMID: 38451939 PMCID: PMC10945751 DOI: 10.1073/pnas.2311726121] [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: 07/24/2023] [Accepted: 12/27/2023] [Indexed: 03/09/2024] Open
Abstract
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.
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Affiliation(s)
- Marshall Case
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
| | - Matthew Smith
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI48109
| | - Jordan Vinh
- Biomedical Engineering, University of Michigan, Ann Arbor, MI48109
| | - Greg Thurber
- Chemical Engineering, University of Michigan, Ann Arbor, MI48109
- Biomedical Engineering, University of Michigan, Ann Arbor, MI48109
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31
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape. J Chem Inf Model 2024; 64:1657-1681. [PMID: 38373700 DOI: 10.1021/acs.jcim.3c01857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States of America
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32
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Lv JX, Liu X, Pei YY, Song ZG, Chen X, Hu SJ, She JL, Liu Y, Chen YM, Zhang YZ. Evolutionary trajectory of diverse SARS-CoV-2 variants at the beginning of COVID-19 outbreak. Virus Evol 2024; 10:veae020. [PMID: 38562953 PMCID: PMC10984623 DOI: 10.1093/ve/veae020] [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: 09/01/2023] [Revised: 01/24/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Despite extensive scientific efforts directed toward the evolutionary trajectory of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans at the beginning of the COVID-19 epidemic, it remains unclear how the virus jumped into and evolved in humans so far. Herein, we recruited almost all adult coronavirus disease 2019 (COVID-19) cases appeared locally or imported from abroad during the first 8 months of the outbreak in Shanghai. From these patients, SARS-CoV-2 genomes occupying the important phylogenetic positions in the virus phylogeny were recovered. Phylogenetic and mutational landscape analyses of viral genomes recovered here and those collected in and outside of China revealed that all known SARS-CoV-2 variants exhibited the evolutionary continuity despite the co-circulation of multiple lineages during the early period of the epidemic. Various mutations have driven the rapid SARS-CoV-2 diversification, and some of them favor its better adaptation and circulation in humans, which may have determined the waxing and waning of various lineages.
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Affiliation(s)
- Jia-Xin Lv
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Xiang Liu
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Yuan-Yuan Pei
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
- Shanghai Public Health Clinical Center, No. 2901 Canglang Road, Jinshan District, Shanghai 210508, China
| | - Zhi-Gang Song
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
- Shanghai Public Health Clinical Center, No. 2901 Canglang Road, Jinshan District, Shanghai 210508, China
| | - Xiao Chen
- College of Marine Sciences, South China Agricultural University, No. 483 Wushan Road, Tianhe District, Guangzhou, Guangdong 510642, China
| | - Shu-Jian Hu
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Jia-Lei She
- Shanghai Public Health Clinical Center, No. 2901 Canglang Road, Jinshan District, Shanghai 210508, China
| | - Yi Liu
- Shanghai Public Health Clinical Center, No. 2901 Canglang Road, Jinshan District, Shanghai 210508, China
| | - Yan-Mei Chen
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
| | - Yong-Zhen Zhang
- State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Human Phenome Institute, Fudan University, No. 2005 Songhu Road, Yangpu District, Shanghai 200438, China
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33
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Yao Z, Zhang L, Duan Y, Tang X, Lu J. Molecular insights into the adaptive evolution of SARS-CoV-2 spike protein. J Infect 2024; 88:106121. [PMID: 38367704 DOI: 10.1016/j.jinf.2024.106121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/02/2024] [Accepted: 02/10/2024] [Indexed: 02/19/2024]
Abstract
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially damaged the global economy and human health. The spike (S) protein of coronaviruses plays a pivotal role in viral entry by binding to host cell receptors. Additionally, it acts as the primary target for neutralizing antibodies in those infected and is the central focus for currently utilized or researched vaccines. During the virus's adaptation to the human host, the S protein of SARS-CoV-2 has undergone significant evolution. As the COVID-19 pandemic has unfolded, new mutations have arisen and vanished, giving rise to distinctive amino acid profiles within variant of concern strains of SARS-CoV-2. Notably, many of these changes in the S protein have been positively selected, leading to substantial alterations in viral characteristics, such as heightened transmissibility and immune evasion capabilities. This review aims to provide an overview of our current understanding of the structural implications associated with key amino acid changes in the S protein of SARS-CoV-2. These research findings shed light on the intricate and dynamic nature of viral evolution, underscoring the importance of continuous monitoring and analysis of viral genomes. Through these molecular-level investigations, we can attain deeper insights into the virus's adaptive evolution, offering valuable guidance for designing vaccines and developing antiviral drugs to combat the ever-evolving viral threats.
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Affiliation(s)
- Zhuocheng Yao
- College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Lin Zhang
- College of Fishery, Ocean University of China, Qingdao 266003, China
| | - Yuange Duan
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xiaolu Tang
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Jian Lu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China.
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Chan CWF, Wang B, Nan L, Huang X, Mao T, Chu HY, Luo C, Chu H, Choi GCG, Shum HC, Wong ASL. High-throughput screening of genetic and cellular drivers of syncytium formation induced by the spike protein of SARS-CoV-2. Nat Biomed Eng 2024; 8:291-309. [PMID: 37996617 DOI: 10.1038/s41551-023-01140-z] [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: 11/21/2022] [Accepted: 10/18/2023] [Indexed: 11/25/2023]
Abstract
Mapping mutations and discovering cellular determinants that cause the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to induce infected cells to form syncytia would facilitate the development of strategies for blocking the formation of such cell-cell fusion. Here we describe high-throughput screening methods based on droplet microfluidics and the size-exclusion selection of syncytia, coupled with large-scale mutagenesis and genome-wide knockout screening via clustered regularly interspaced short palindromic repeats (CRISPR), for the large-scale identification of determinants of cell-cell fusion. We used the methods to perform deep mutational scans in spike-presenting cells to pinpoint mutable syncytium-enhancing substitutions in two regions of the spike protein (the fusion peptide proximal region and the furin-cleavage site). We also used a genome-wide CRISPR screen in cells expressing the receptor angiotensin-converting enzyme 2 to identify inhibitors of clathrin-mediated endocytosis that impede syncytium formation, which we validated in hamsters infected with SARS-CoV-2. Finding genetic and cellular determinants of the formation of syncytia may reveal insights into the physiological and pathological consequences of cell-cell fusion.
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Affiliation(s)
- Charles W F Chan
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre for Oncology and Immunology, Hong Kong Science Park, Shatin, Hong Kong SAR, China
| | - Bei Wang
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre for Oncology and Immunology, Hong Kong Science Park, Shatin, Hong Kong SAR, China
| | - Lang Nan
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, Hong Kong SAR, China
| | - Xiner Huang
- State Key Laboratory of Emerging Infectious Diseases, Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Tianjiao Mao
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, Hong Kong SAR, China
| | - Hoi Yee Chu
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre for Oncology and Immunology, Hong Kong Science Park, Shatin, Hong Kong SAR, China
| | - Cuiting Luo
- State Key Laboratory of Emerging Infectious Diseases, Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Hin Chu
- State Key Laboratory of Emerging Infectious Diseases, Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Centre for Virology, Vaccinology and Therapeutics, Hong Kong Science Park, Shatin, Hong Kong SAR, China.
- Department of Infectious Disease and Microbiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People's Republic of China.
| | - Gigi C G Choi
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Centre for Oncology and Immunology, Hong Kong Science Park, Shatin, Hong Kong SAR, China.
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
| | - Ho Cheung Shum
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, Hong Kong SAR, China.
| | - Alan S L Wong
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Centre for Oncology and Immunology, Hong Kong Science Park, Shatin, Hong Kong SAR, China.
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35
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Shukla N, Roelle SM, Snell JC, DelSignore O, Bruchez AM, Matreyek KA. Pseudotyped virus infection of multiplexed ACE2 libraries reveals SARS-CoV-2 variant shifts in receptor usage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580056. [PMID: 38405739 PMCID: PMC10888787 DOI: 10.1101/2024.02.13.580056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Pairwise compatibility between virus and host proteins can dictate the outcome of infection. During transmission, both inter- and intraspecies variabilities in receptor protein sequences can impact cell susceptibility. Many viruses possess mutable viral entry proteins and the patterns of host compatibility can shift as the viral protein sequence changes. This combinatorial sequence space between virus and host is poorly understood, as traditional experimental approaches lack the throughput to simultaneously test all possible combinations of protein sequences. Here, we created a pseudotyped virus infection assay where a multiplexed target-cell library of host receptor variants can be assayed simultaneously using a DNA barcode sequencing readout. We applied this assay to test a panel of 30 ACE2 orthologs or human sequence mutants for infectability by the original SARS-CoV-2 spike protein or the Alpha, Beta, Gamma, Delta, and Omicron BA1 variant spikes. We compared these results to an analysis of the structural shifts that occurred for each variant spike's interface with human ACE2. Mutated residues were directly involved in the largest shifts, although there were also widespread indirect effects altering interface structure. The N501Y substitution in spike conferred a large structural shift for interaction with ACE2, which was partially recreated by indirect distal substitutions in Delta, which does not harbor N501Y. The structural shifts from N501Y greatly influenced the set of animal orthologs the variant spike was capable of interacting with. Out of the thirteen non-human orthologs, ten exhibited unique patterns of variant-specific compatibility, demonstrating that spike sequence changes during human transmission can toggle ACE2 compatibility and potential susceptibility of other animal species, and cumulatively increase overall compatibilities as new variants emerge. These experiments provide a blueprint for similar large-scale assessments of protein compatibility during entry by diverse viruses. This dataset demonstrates the complex compatibility relationships that occur between variable interacting host and virus proteins.
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Affiliation(s)
- Nidhi Shukla
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Sarah M Roelle
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - John C Snell
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Olivia DelSignore
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Anna M Bruchez
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Kenneth A Matreyek
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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36
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Lan PD, Nissley DA, O’Brien EP, Nguyen TT, Li MS. Deciphering the free energy landscapes of SARS-CoV-2 wild type and Omicron variant interacting with human ACE2. J Chem Phys 2024; 160:055101. [PMID: 38310477 PMCID: PMC11223169 DOI: 10.1063/5.0188053] [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: 11/18/2023] [Accepted: 01/08/2024] [Indexed: 02/05/2024] Open
Abstract
The binding of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein to the host cell receptor angiotensin-converting enzyme 2 (ACE2) is the first step in human viral infection. Therefore, understanding the mechanism of interaction between RBD and ACE2 at the molecular level is critical for the prevention of COVID-19, as more variants of concern, such as Omicron, appear. Recently, atomic force microscopy has been applied to characterize the free energy landscape of the RBD-ACE2 complex, including estimation of the distance between the transition state and the bound state, xu. Here, using a coarse-grained model and replica-exchange umbrella sampling, we studied the free energy landscape of both the wild type and Omicron subvariants BA.1 and XBB.1.5 interacting with ACE2. In agreement with experiment, we find that the wild type and Omicron subvariants have similar xu values, but Omicron binds ACE2 more strongly than the wild type, having a lower dissociation constant KD.
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Affiliation(s)
| | - Daniel A. Nissley
- Department of Statistics, University of Oxford, Oxford Protein Bioinformatics Group, Oxford OX1 2JD, United Kingdom
| | | | - Toan T. Nguyen
- Key Laboratory for Multiscale Simulation of Complex Systems and Department of Theoretical Physics, Faculty of Physics, University of Science, Vietnam National University - Hanoi, 334 Nguyen Trai Street, Thanh Xuan District, Hanoi 11400, Vietnam
| | - Mai Suan Li
- Institute of Physics, Polish Academy of Sciences, al. Lotnikow 32/46, 02-668 Warsaw, Poland
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37
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Pun MN, Ivanov A, Bellamy Q, Montague Z, LaMont C, Bradley P, Otwinowski J, Nourmohammad A. Learning the shape of protein microenvironments with a holographic convolutional neural network. Proc Natl Acad Sci U S A 2024; 121:e2300838121. [PMID: 38300863 PMCID: PMC10861886 DOI: 10.1073/pnas.2300838121] [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: 01/18/2023] [Accepted: 11/29/2023] [Indexed: 02/03/2024] Open
Abstract
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.
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Affiliation(s)
- Michael N. Pun
- Department of Physics, University of Washington, Seattle, WA98195
- The Department for Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, Göttingen37077, Germany
| | - Andrew Ivanov
- Department of Physics, University of Washington, Seattle, WA98195
| | - Quinn Bellamy
- Department of Physics, University of Washington, Seattle, WA98195
| | - Zachary Montague
- Department of Physics, University of Washington, Seattle, WA98195
- The Department for Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, Göttingen37077, Germany
| | - Colin LaMont
- The Department for Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, Göttingen37077, Germany
| | - Philip Bradley
- Fred Hutchinson Cancer Center, Seattle, WA98102
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Jakub Otwinowski
- The Department for Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, Göttingen37077, Germany
- Dyno Therapeutics, Watertown, MA02472
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA98195
- The Department for Statistical Physics of Evolving Systems, Max Planck Institute for Dynamics and Self-Organization, Göttingen37077, Germany
- Fred Hutchinson Cancer Center, Seattle, WA98102
- Department of Applied Mathematics, University of Washington, Seattle, WA98105
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA98195
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38
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Ose NJ, Campitelli P, Modi T, Can Kazan I, Kumar S, Banu Ozkan S. Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.14.557827. [PMID: 37745560 PMCID: PMC10515954 DOI: 10.1101/2023.09.14.557827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.
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Affiliation(s)
- Nicholas J. Ose
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Paul Campitelli
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Tushar Modi
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - I. Can Kazan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Biology, Temple University, Philadelphia, Pennsylvania, United States of America
- Center for Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - S. Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
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Ketaren NE, Mast FD, Fridy PC, Olivier JP, Sanyal T, Sali A, Chait BT, Rout MP, Aitchison JD. Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.14.549041. [PMID: 37503298 PMCID: PMC10369967 DOI: 10.1101/2023.07.14.549041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
To date, all major modes of monoclonal antibody therapy targeting SARS-CoV-2 have lost significant efficacy against the latest circulating variants. As SARS-CoV-2 omicron sublineages account for over 90% of COVID-19 infections, evasion of immune responses generated by vaccination or exposure to previous variants poses a significant challenge. A compelling new therapeutic strategy against SARS-CoV-2 is that of single domain antibodies, termed nanobodies, which address certain limitations of monoclonal antibodies. Here we demonstrate that our high-affinity nanobody repertoire, generated against wild-type SARS-CoV-2 spike protein (Mast, Fridy et al. 2021), remains effective against variants of concern, including omicron BA.4/BA.5; a subset is predicted to counter resistance in emerging XBB and BQ.1.1 sublineages. Furthermore, we reveal the synergistic potential of nanobody cocktails in neutralizing emerging variants. Our study highlights the power of nanobody technology as a versatile therapeutic and diagnostic tool to combat rapidly evolving infectious diseases such as SARS-CoV-2.
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Affiliation(s)
- Natalia E. Ketaren
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065, USA
| | - Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington 98109, USA
| | - Peter C. Fridy
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065, USA
| | - Jean Paul Olivier
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington 98109, USA
| | - Tanmoy Sanyal
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94143, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, California 94143, USA
| | - Brian T. Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York 10065, USA
| | - Michael P. Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York 10065, USA
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington 98109, USA
- Department of Pediatrics, University of Washington, Seattle, Washington 98195, USA
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
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40
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Harari S, Miller D, Fleishon S, Burstein D, Stern A. Using big sequencing data to identify chronic SARS-Coronavirus-2 infections. Nat Commun 2024; 15:648. [PMID: 38245511 PMCID: PMC10799923 DOI: 10.1038/s41467-024-44803-4] [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: 09/04/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.
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Affiliation(s)
- Sheri Harari
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Danielle Miller
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Shay Fleishon
- Israeli Health Intelligence Agency, Public Health Division, Ministry of Health, Jerusalem, Israel
| | - David Burstein
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel.
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Irvine EB, Reddy ST. Advancing Antibody Engineering through Synthetic Evolution and Machine Learning. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:235-243. [PMID: 38166249 DOI: 10.4049/jimmunol.2300492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 01/04/2024]
Abstract
Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.
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Affiliation(s)
- Edward B Irvine
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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42
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Anand A, Long C, Chandran K. NYC metropolitan wastewater reveals links between SARS-CoV-2 amino acid mutations and disease outcomes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:167971. [PMID: 37914132 DOI: 10.1016/j.scitotenv.2023.167971] [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: 02/17/2023] [Revised: 10/01/2023] [Accepted: 10/18/2023] [Indexed: 11/03/2023]
Abstract
Since late 2020, diverse SARS-CoV-2 variants with enhanced infectivity and transmissibility have emerged. In contrast to the focus on amino acid mutations in the spike protein, mutations in non-spike proteins and their associated impacts remain relatively understudied. New York City metropolitan wastewater revealed over 60 % of the most frequently occurring amino acid mutations in regions outside the spike protein. Strikingly, ~50 % of the mutations detected herein remain uncharacterized for functional impacts. Our results suggest that there are several understudied mutations within non-spike proteins N, ORF1a, ORF1b, ORF9b, and ORF9c, that could increase transmissibility, and infectivity among human populations. We also demonstrate significant correlations of P314L, D614G, T95I, G50E, G50R, G204R, R203K, G662S, P10S, and P13L with documented mortality rates, hospitalization rates, and percent positivity suggesting that amino acid mutations are likely to be indicators of COVID-19 infection outcomes.
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Affiliation(s)
- Archana Anand
- Department of Earth and Environmental Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, United States of America
| | - Chenghua Long
- Department of Earth and Environmental Engineering, Columbia University, 500 W. 120th Street, New York, NY 10027, United States of America
| | - Kartik Chandran
- Department of Earth and Environmental Engineering, Columbia University, 500 W. 120th Street, New York, NY 10027, United States of America.
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Pérez-Massón B, Quintana-Pérez Y, Tundidor Y, Pérez-Martínez D, Castro-Martínez C, Pupo-Meriño M, Orosa I, Relova-Hernández E, Villegas R, Guirola O, Rojas G. Studying SARS-CoV-2 interactions using phage-displayed receptor binding domain as a model protein. Sci Rep 2024; 14:712. [PMID: 38184672 PMCID: PMC10771503 DOI: 10.1038/s41598-023-50450-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/20/2023] [Indexed: 01/08/2024] Open
Abstract
SARS-CoV-2 receptor binding domain (RBD) mediates viral entry into human cells through its interaction with angiotensin converting enzyme 2 (ACE2). Most neutralizing antibodies elicited by infection or vaccination target this domain. Such a functional relevance, together with large RBD sequence variability arising during viral spreading, point to the need of exploring the complex landscape of interactions between RBD-derived variants, ACE2 and antibodies. The current work was aimed at developing a simple platform to do so. Biologically active and antigenic Wuhan-Hu-1 RBD, as well as mutated RBD variants found in nature, were successfully displayed on filamentous phages. Mutational scanning confirmed the global plasticity of the receptor binding motif within RBD, highlighted residues playing a critical role in receptor binding, and identified mutations strengthening the interaction. The ability of vaccine-induced antibodies to inhibit ACE2 binding of many mutated RBD variants, albeit at different extents, was shown. Amino acid replacements which could compromise such inhibitory potential were underscored. The expansion of our approach could be the starting point for a large-scale phage-based exploration of diversity within RBD of SARS-CoV-2 and related coronaviruses, useful to understand structure-function relationships, to engineer RBD proteins, and to anticipate changes to watch during viral evolution.
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Affiliation(s)
- Beatriz Pérez-Massón
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Yazmina Quintana-Pérez
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Yaima Tundidor
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Dayana Pérez-Martínez
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Camila Castro-Martínez
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Mario Pupo-Meriño
- Universidad de Ciencias Informáticas, Carretera a San Antonio de los Baños, km 2 1/2, Torrens, Boyeros, CP 19370, Havana, Cuba
| | - Ivette Orosa
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Ernesto Relova-Hernández
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba
| | - Rosmery Villegas
- Universidad de Ciencias Informáticas, Carretera a San Antonio de los Baños, km 2 1/2, Torrens, Boyeros, CP 19370, Havana, Cuba
| | - Osmany Guirola
- Center for Genetic Engineering and Biotechnology, Ave 31 E/158 y 190, Cubanacán, Playa, CP 11300, Havana, Cuba
| | - Gertrudis Rojas
- Center of Molecular Immunology, Calle 216 esq 15, apartado 16040, Atabey, Playa, CP 11300, Havana, Cuba.
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Zhao J, Kang M, Wu H, Sun B, Baele G, He WT, Lu M, Suchard MA, Ji X, He N, Su S, Veit M. Risk assessment of SARS-CoV-2 replicating and evolving in animals. Trends Microbiol 2024; 32:79-92. [PMID: 37541811 DOI: 10.1016/j.tim.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 08/06/2023]
Abstract
The retransmissions of SARS-CoV-2 from several mammals - primarily mink and white-tailed deer - to humans have raised concerns for the emergence of a new animal-derived SARS-CoV-2 variant to worsen the pandemic. Here, we discuss animal species that are susceptible to natural or experimental infection with SARS-CoV-2 and can transmit the virus to mates or humans. We describe cutting-edge techniques to assess the impact of a mutation in the viral spike (S) protein on its receptor and on antibody binding. Our review of spike sequences of animal-derived viruses identified nine unique amino acid exchanges in the receptor-binding domain (RBD) that are not present in any variant of concern (VOC). These mutations are present in SARS-CoV-2 found in companion animals such as dogs and cats, and they exhibit a higher frequency in SARS-CoV-2 found in mink and white-tailed deer, suggesting that sustained transmissions may contribute to maintaining novel mutations. Four of these exchanges, such as Leu452Met, could undermine acquired immune protection in humans while maintaining high affinity for the human angiotensin-converting enzyme 2 (ACE2) receptor. Finally, we discuss important avenues of future research into animal-derived viruses with public health risks.
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Affiliation(s)
- Jin Zhao
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Mei Kang
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China; Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongyan Wu
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Bowen Sun
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Guy Baele
- Department of Microbiology, Immunology, and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Wan-Ting He
- School of Pharmacy, China Pharmaceutical University, Nanjing, China.
| | - Meng Lu
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA; Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA
| | - Na He
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Shuo Su
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.
| | - Michael Veit
- Institute for Virology, Center for Infection Medicine, Veterinary Faculty, Free University Berlin, Berlin, Germany.
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45
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Nemoto T, Ocari T, Planul A, Tekinsoy M, Zin EA, Dalkara D, Ferrari U. ACIDES: on-line monitoring of forward genetic screens for protein engineering. Nat Commun 2023; 14:8504. [PMID: 38148337 PMCID: PMC10751290 DOI: 10.1038/s41467-023-43967-9] [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: 04/07/2023] [Accepted: 11/24/2023] [Indexed: 12/28/2023] Open
Abstract
Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.
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Affiliation(s)
- Takahiro Nemoto
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
- Graduate School of Informatics, Kyoto University, Yoshida Hon-machi, Sakyo-ku, Kyoto, 606-8501, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka, 565-0871, Japan.
| | - Tommaso Ocari
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France
| | - Arthur Planul
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France
| | - Muge Tekinsoy
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France
| | - Emilia A Zin
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France
| | - Deniz Dalkara
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
| | - Ulisse Ferrari
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
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46
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Shanker VR, Bruun TU, Hie BL, Kim PS. Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572475. [PMID: 38187780 PMCID: PMC10769282 DOI: 10.1101/2023.12.19.572475] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Large language models trained on sequence information alone are capable of learning high level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here we show that a general protein language model augmented with protein structure backbone coordinates and trained on the inverse folding problem can guide evolution for diverse proteins without needing to explicitly model individual functional tasks. We demonstrate inverse folding to be an effective unsupervised, structure-based sequence optimization strategy that also generalizes to multimeric complexes by implicitly learning features of binding and amino acid epistasis. Using this approach, we screened ~30 variants of two therapeutic clinical antibodies used to treat SARS-CoV-2 infection and achieved up to 26-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants-of-concern BQ.1.1 and XBB.1.5, respectively. In addition to substantial overall improvements in protein function, we find inverse folding performs with leading experimental success rates among other reported machine learning-guided directed evolution methods, without requiring any task-specific training data.
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Affiliation(s)
- Varun R. Shanker
- Stanford Biophysics Program, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford CA 94305, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Theodora U.J. Bruun
- Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford CA 94305, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Brian L. Hie
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peter S. Kim
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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47
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Lee J, Zepeda SK, Park YJ, Taylor AL, Quispe J, Stewart C, Leaf EM, Treichel C, Corti D, King NP, Starr TN, Veesler D. Broad receptor tropism and immunogenicity of a clade 3 sarbecovirus. Cell Host Microbe 2023; 31:1961-1973.e11. [PMID: 37989312 PMCID: PMC10913562 DOI: 10.1016/j.chom.2023.10.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023]
Abstract
Although Rhinolophus bats harbor diverse clade 3 sarbecoviruses, the structural determinants of receptor tropism along with the antigenicity of their spike (S) glycoproteins remain uncharacterized. Here, we show that the African Rhinolophus bat clade 3 sarbecovirus PRD-0038 S has a broad angiotensin-converting enzyme 2 (ACE2) usage and that receptor-binding domain (RBD) mutations further expand receptor promiscuity and enable human ACE2 utilization. We determine a cryo-EM structure of the PRD-0038 RBD bound to Rhinolophus alcyone ACE2, explaining receptor tropism and highlighting differences with SARS-CoV-1 and SARS-CoV-2. Characterization of PRD-0038 S using cryo-EM and monoclonal antibody reactivity reveals its distinct antigenicity relative to SARS-CoV-2 and identifies PRD-0038 cross-neutralizing antibodies for pandemic preparedness. PRD-0038 S vaccination elicits greater titers of antibodies cross-reacting with vaccine-mismatched clade 2 and clade 1a sarbecoviruses compared with SARS-CoV-2 S due to broader antigenic targeting, motivating the inclusion of clade 3 antigens in next-generation vaccines for enhanced resilience to viral evolution.
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Affiliation(s)
- Jimin Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Samantha K Zepeda
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Young-Jun Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA
| | - Ashley L Taylor
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Joel Quispe
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Cameron Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Elizabeth M Leaf
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Catherine Treichel
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Davide Corti
- Humabs Biomed SA, a Subsidiary of Vir. Biotechnology, 6500 Bellinzona, Switzerland
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Tyler N Starr
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA.
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48
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Raisinghani N, Alshahrani M, Gupta G, Xiao S, Tao P, Verkhivker G. AlphaFold2-Enabled Atomistic Modeling of Epistatic Binding Mechanisms for the SARS-CoV-2 Spike Omicron XBB.1.5, EG.5 and FLip Variants: Convergent Evolution Hotspots Cooperate to Control Stability and Conformational Adaptability in Balancing ACE2 Binding and Antibody Resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.11.571185. [PMID: 38168257 PMCID: PMC10760024 DOI: 10.1101/2023.12.11.571185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
In this study, we combined AI-based atomistic structural modeling and microsecond molecular simulations of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for XBB.1.5+L455F, XBB.1.5+F456L(EG.5) and XBB.1.5+L455F/F456L (FLip) lineages to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and physics-based rigorous computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of Q493 hotspot in synchronization of epistatic couplings between L455F and F456L mutations providing a quantitative insight into the mechanism underlying differences between XBB lineages. Mutational profiling is combined with network-based model of epistatic couplings showing that the Q493, L455 and F456 sites mediate stable communities at the binding interface with ACE2 and can serve as stable mediators of non-additive couplings. Structure-based mutational analysis of Spike protein binding with the class 1 antibodies quantified the critical role of F456L and F486P mutations in eliciting strong immune evasion response. The results of this analysis support a mechanism in which the emergence of EG.5 and FLip variants may have been dictated by leveraging strong epistatic effects between several convergent revolutionary hotspots that provide synergy between the improved ACE2 binding and broad neutralization resistance. This interpretation is consistent with the notion that functionally balanced substitutions which simultaneously optimize immune evasion and high ACE2 affinity may continue to emerge through lineages with beneficial pair or triplet combinations of RBD mutations involving mediators of epistatic couplings and sites in highly adaptable RBD regions.
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49
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Yao W, Li Y, Ma D, Hou X, Wang H, Tang X, Cheng D, Zhang H, Du C, Pan H, Li C, Lin H, Sun M, Ding Q, Wang Y, Gao J, Zhong G. Evolution of SARS-CoV-2 Spikes shapes their binding affinities to animal ACE2 orthologs. Microbiol Spectr 2023; 11:e0267623. [PMID: 37943512 PMCID: PMC10715038 DOI: 10.1128/spectrum.02676-23] [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: 07/10/2023] [Accepted: 10/08/2023] [Indexed: 11/10/2023] Open
Abstract
IMPORTANCE Spike-receptor interaction is a critical determinant for the host range of coronaviruses. In this study, we investigated the SARS-CoV-2 WHU01 strain and five WHO-designated SARS-CoV-2 variants of concern (VOCs), including Alpha, Beta, Gamma, Delta, and the early Omicron variant, for their Spike interactions with ACE2 proteins of 18 animal species. First, the receptor-binding domains (RBDs) of Alpha, Beta, Gamma, and Omicron were found to display progressive gain of affinity to mouse ACE2. More interestingly, these RBDs were also found with progressive loss of affinities to multiple ACE2 orthologs. The Omicron RBD showed decreased or complete loss of affinity to eight tested animal ACE2 orthologs, including that of some livestock animals (horse, donkey, and pig), pet animals (dog and cat), and wild animals (pangolin, American pika, and Rhinolophus sinicus bat). These findings shed light on potential host range shift of SARS-CoV-2 VOCs, especially that of the Omicron variant.
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Affiliation(s)
- Weitong Yao
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
- Hubei JiangXia Laboratory, Wuhan, Hubei, China
| | - Yujun Li
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Danting Ma
- Shenzhen Bay Laboratory, Shenzhen, China
- NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China
| | - Xudong Hou
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Haimin Wang
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Xiaojuan Tang
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Dechun Cheng
- Shenzhen Bay Laboratory, Shenzhen, China
- Heilongjiang Academy of Medical Sciences, Harbin, China
| | - He Zhang
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Chengzhi Du
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Hong Pan
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Chao Li
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Hua Lin
- Biomedical Research Center of South China, Fujian Normal University, Fuzhou, China
| | - Mengsi Sun
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Qiang Ding
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China
| | | | - Jiali Gao
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Guocai Zhong
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
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50
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Jian F, Feng L, Yang S, Yu Y, Wang L, Song W, Yisimayi A, Chen X, Xu Y, Wang P, Yu L, Wang J, Liu L, Niu X, Wang J, Xiao T, An R, Wang Y, Gu Q, Shao F, Jin R, Shen Z, Wang Y, Wang X, Cao Y. Convergent evolution of SARS-CoV-2 XBB lineages on receptor-binding domain 455-456 synergistically enhances antibody evasion and ACE2 binding. PLoS Pathog 2023; 19:e1011868. [PMID: 38117863 PMCID: PMC10766189 DOI: 10.1371/journal.ppat.1011868] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/04/2024] [Accepted: 11/28/2023] [Indexed: 12/22/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) XBB lineages have achieved dominance worldwide and keep on evolving. Convergent evolution of XBB lineages on the receptor-binding domain (RBD) L455F and F456L is observed, resulting in variants with substantial growth advantages, such as EG.5, FL.1.5.1, XBB.1.5.70, and HK.3. Here, we show that neutralizing antibody (NAb) evasion drives the convergent evolution of F456L, while the epistatic shift caused by F456L enables the subsequent convergence of L455F through ACE2 binding enhancement and further immune evasion. L455F and F456L evade RBD-targeting Class 1 public NAbs, reducing the neutralization efficacy of XBB breakthrough infection (BTI) and reinfection convalescent plasma. Importantly, L455F single substitution significantly dampens receptor binding; however, the combination of L455F and F456L forms an adjacent residue flipping, which leads to enhanced NAbs resistance and ACE2 binding affinity. The perturbed receptor-binding mode leads to the exceptional ACE2 binding and NAb evasion, as revealed by structural analyses. Our results indicate the evolution flexibility contributed by epistasis cannot be underestimated, and the evolution potential of SARS-CoV-2 RBD remains high.
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Affiliation(s)
- Fanchong Jian
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Changping Laboratory, Beijing, People’s Republic of China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, People’s Republic of China
| | - Leilei Feng
- CAS Key Laboratory of Infection and Immunity, National Laboratory of Macromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Sijie Yang
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, People’s Republic of China
| | - Yuanling Yu
- Changping Laboratory, Beijing, People’s Republic of China
| | - Lei Wang
- CAS Key Laboratory of Infection and Immunity, National Laboratory of Macromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Weiliang Song
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Changping Laboratory, Beijing, People’s Republic of China
- School of Life Sciences, Peking University, Beijing, People’s Republic of China
| | - Ayijiang Yisimayi
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Changping Laboratory, Beijing, People’s Republic of China
- School of Life Sciences, Peking University, Beijing, People’s Republic of China
| | - Xiaosu Chen
- Institute for Immunology, College of Life Sciences, Nankai University, Tianjin, People’s Republic of China
| | - Yanli Xu
- Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Peng Wang
- Changping Laboratory, Beijing, People’s Republic of China
| | - Lingling Yu
- Changping Laboratory, Beijing, People’s Republic of China
| | - Jing Wang
- Changping Laboratory, Beijing, People’s Republic of China
| | - Lu Liu
- Changping Laboratory, Beijing, People’s Republic of China
| | - Xiao Niu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, People’s Republic of China
| | - Jing Wang
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Changping Laboratory, Beijing, People’s Republic of China
- School of Life Sciences, Peking University, Beijing, People’s Republic of China
| | - Tianhe Xiao
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Joint Graduate Program of Peking-Tsinghua-NIBS, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People’s Republic of China
| | - Ran An
- Changping Laboratory, Beijing, People’s Republic of China
| | - Yao Wang
- Changping Laboratory, Beijing, People’s Republic of China
| | - Qingqing Gu
- Changping Laboratory, Beijing, People’s Republic of China
| | - Fei Shao
- Changping Laboratory, Beijing, People’s Republic of China
| | - Ronghua Jin
- Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Zhongyang Shen
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, People’s Republic of China
| | - Youchun Wang
- Changping Laboratory, Beijing, People’s Republic of China
- Institute of Medical Biology, Chinese Academy of Medical Science & Peking Union Medical College, Kunming, People’s Republic of China
| | - Xiangxi Wang
- CAS Key Laboratory of Infection and Immunity, National Laboratory of Macromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Yunlong Cao
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, People’s Republic of China
- Changping Laboratory, Beijing, People’s Republic of China
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