1
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Posfai A, Zhou J, McCandlish DM, Kinney JB. Gauge fixing for sequence-function relationships. PLoS Comput Biol 2025; 21:e1012818. [PMID: 40111986 PMCID: PMC11957564 DOI: 10.1371/journal.pcbi.1012818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 01/22/2025] [Indexed: 03/22/2025] Open
Abstract
Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.
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Affiliation(s)
- Anna Posfai
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Juannan Zhou
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Justin B. Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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2
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Monge M, Giovanetti SM, Ravishankar A, Sadhu MJ. Highly replicated experiments studying complex genotypes using nested DNA barcodes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.18.643964. [PMID: 40166312 PMCID: PMC11956976 DOI: 10.1101/2025.03.18.643964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Many biological experiments involve studying the differences caused by genetic modifications, including genotypes composed of modifications at more than one locus. However, as the number and complexity of the genotypes increases, independently generating and tracking the necessary number of biological replicate samples becomes a major challenge. We developed a barcode-based method to track large numbers of independent replicates of combinatorial genotypes in a pooled format, enabling robust detection of subtle phenotypic differences. To construct a plasmid library of combinatorial genotypes, we utilized a nested serial cloning process to combine gene variants of interest that have associated DNA barcodes. The final plasmids each contain variants of multiple genes of interest, and a combined barcode that specifies the genotype of all the genes while also encoding a random sequence for tracking individual replicates. Sequencing of the pool of barcodes by next-generation sequencing allows the whole population to be studied in a single flask, enabling a high degree of replication even for complex genotypes. Using this approach, we tested the functionality of combinations of yeast, human, and null orthologs of the nucleotide excision repair factor I (NEF-1) complex and found that cells expressing all three yeast NEF-1 subunits had superior growth in DNA-damaging conditions. We also assessed the sensitivity of our method by simulating downsampling of barcodes across different degrees of phenotypic differentiation. Our results demonstrate the utility of NICR barcodes for high-throughput combinatorial genetic screens and provide a scalable framework for exploring complex genotype-phenotype relationships.
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Affiliation(s)
- Molly Monge
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Present address: Weill Medical College, Cornell University, New York, NY 10065, USA
| | - Simone M Giovanetti
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Apoorva Ravishankar
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Present address: MelioLabs Inc., 4655 Old Ironsides Dr Ste. 480, Santa Clara, CA 95054
| | - Meru J Sadhu
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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3
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Fryer T, Wolff DS, Overath MD, Schäfer E, Laustsen AH, Jenkins TP, Andersen C. Post-assembly Plasmid Amplification for Increased Transformation Yields in E. coli and S. cerevisiae. CHEM & BIO ENGINEERING 2025; 2:87-96. [PMID: 40041006 PMCID: PMC11873849 DOI: 10.1021/cbe.4c00115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 10/30/2024] [Accepted: 11/03/2024] [Indexed: 03/06/2025]
Abstract
Many biological disciplines rely upon the transformation of host cells with heterologous DNA to edit, engineer, or examine biological phenotypes. Transformation of model cell strains (Escherichia coli) under model conditions (electroporation of circular supercoiled plasmid DNA; typically pUC19) can achieve >1010 transformants/μg DNA. Yet outside of these conditions, e.g., work with relaxed plasmid DNA from in vitro assembly reactions (cloned DNA) or nonmodel organisms, the efficiency of transformation can drop by multiple orders of magnitude. Overcoming these inefficiencies requires cost- and time-intensive processes, such as generating large quantities of appropriately formatted input DNA or transforming many aliquots of cells in parallel. We sought to simplify the generation of large quantities of appropriately formatted input cloned DNA by using rolling circle amplification (RCA) and treatment with specific endonucleases to generate an efficiently transformable linear DNA product for in vivo circularization in host cells. We achieved an over 6500-fold increase in the yield of input DNA, and demonstrate that the use of a nicking endonuclease to generate homologous single-stranded ends increases the efficiency of E. coli chemical transformation compared to both linear DNA with double-stranded homologous ends and circular Golden-Gate assembly products. Meanwhile, the use of a restriction endonuclease to generate linear DNA with double-stranded homologous ends increases the efficiency of chemical and electrotransformation of Saccharomyces cerevisiae. Importantly, we also optimized the process such that both RCA and endonuclease treatment occur efficiently in the same buffer, streamlining the workflow and reducing product loss through purification steps. We expect that our approach could have utility beyond E. coli and S. cerevisiae and be applicable to areas such as directed evolution, genome engineering, and the manipulation of alternative organisms with even poorer transformation efficiencies.
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Affiliation(s)
- Thomas Fryer
- Department
of Biotechnology and Biomedicine, Technical
University of Denmark, Søltofts Plads 239, Lyngby, Hovedstaden DK 2800, Denmark
- Department
of Molecular Discovery, R&D, Novozymes
A/S, Bagsvaerd, Hovedstaden DK 2880, Denmark
| | - Darian S. Wolff
- Department
of Biotechnology and Biomedicine, Technical
University of Denmark, Søltofts Plads 239, Lyngby, Hovedstaden DK 2800, Denmark
- Department
of Molecular Discovery, R&D, Novozymes
A/S, Bagsvaerd, Hovedstaden DK 2880, Denmark
| | - Max D. Overath
- Department
of Biotechnology and Biomedicine, Technical
University of Denmark, Søltofts Plads 239, Lyngby, Hovedstaden DK 2800, Denmark
| | - Elena Schäfer
- Department
of Biochemistry, University of Cambridge, Cambridge CB2 1GA, United Kingdom
| | - Andreas H. Laustsen
- Department
of Biotechnology and Biomedicine, Technical
University of Denmark, Søltofts Plads 239, Lyngby, Hovedstaden DK 2800, Denmark
| | - Timothy P. Jenkins
- Department
of Biotechnology and Biomedicine, Technical
University of Denmark, Søltofts Plads 239, Lyngby, Hovedstaden DK 2800, Denmark
| | - Carsten Andersen
- Department
of Molecular Discovery, R&D, Novozymes
A/S, Bagsvaerd, Hovedstaden DK 2880, Denmark
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4
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Kirby MB, Petersen BM, Faris JG, Kells SP, Sprenger KG, Whitehead TA. Retrospective SARS-CoV-2 human antibody development trajectories are largely sparse and permissive. Proc Natl Acad Sci U S A 2025; 122:e2412787122. [PMID: 39841142 PMCID: PMC11789010 DOI: 10.1073/pnas.2412787122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 11/27/2024] [Indexed: 01/23/2025] Open
Abstract
Immunological interventions, like vaccinations, are enabled by the predictive control of humoral responses to novel antigens. While the development trajectories for many broadly neutralizing antibodies (bnAbs) have been measured, it is less established how human subtype-specific antibodies develop from their precursors. In this work, we evaluated the retrospective development trajectories for eight anti-SARS-CoV-2 Spike human antibodies (Abs). To mimic the immunological process of BCR selection during affinity maturation in germinal centers (GCs), we performed deep mutational scanning on anti-S1 molecular Fabs using yeast display coupled to fluorescence-activated cell sorting. Focusing only on changes in affinity upon mutation, we found that human Ab development pathways have few mutations which impart changes in monovalent binding dissociation constants and that these mutations can occur in nearly any order. Maturation pathways of two bnAbs showed that while they are only slightly less permissible than subtype-specific Abs, more development steps on average are needed to reach the same level of affinity. Many of the subtype-specific Abs had inherent affinity for antigen, and these results were robust against different potential inferred precursor sequences. To evaluate the effect of differential affinity for precursors on GC outcomes, we adapted a coarse-grained affinity maturation model. This model showed that antibody precursors with minimal affinity advantages rapidly outcompete competitors to become the dominant clonotype.
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Affiliation(s)
- Monica B. Kirby
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
| | - Brian M. Petersen
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
| | - Jonathan G. Faris
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
| | - Siobhan P. Kells
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
| | - Kayla G. Sprenger
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
| | - Timothy A. Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO80305
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5
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Keen MM, Keith AD, Ortlund EA. Epitope mapping via in vitro deep mutational scanning methods and its applications. J Biol Chem 2025; 301:108072. [PMID: 39674321 PMCID: PMC11783119 DOI: 10.1016/j.jbc.2024.108072] [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: 09/30/2024] [Revised: 12/04/2024] [Accepted: 12/09/2024] [Indexed: 12/16/2024] Open
Abstract
Epitope mapping is a technique employed to define the region of an antigen that elicits an immune response, providing crucial insight into the structural architecture of the antigen as well as epitope-paratope interactions. With this breadth of knowledge, immunotherapies, diagnostics, and vaccines are being developed with a rational and data-supported design. Traditional epitope mapping methods are laborious, time-intensive, and often lack the ability to screen proteins in a high-throughput manner or provide high resolution. Deep mutational scanning (DMS), however, is revolutionizing the field as it can screen all possible single amino acid mutations and provide an efficient and high-throughput way to infer the structures of both linear and three-dimensional epitopes with high resolution. Currently, more than 50 publications take this approach to efficiently identify enhancing or escaping mutations, with many then employing this information to rapidly develop broadly neutralizing antibodies, T-cell immunotherapies, vaccine platforms, or diagnostics. We provide a comprehensive review of the approaches to accomplish epitope mapping while also providing a summation of the development of DMS technology and its impactful applications.
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Affiliation(s)
- Meredith M Keen
- Department of Biochemistry, Emory School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Alasdair D Keith
- Department of Biochemistry, Emory School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Eric A Ortlund
- Department of Biochemistry, Emory School of Medicine, Emory University, Atlanta, Georgia, USA.
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6
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Alpay BA, Desai MM. Effects of selection stringency on the outcomes of directed evolution. PLoS One 2024; 19:e0311438. [PMID: 39401192 PMCID: PMC11472920 DOI: 10.1371/journal.pone.0311438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 09/18/2024] [Indexed: 10/17/2024] Open
Abstract
Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.
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Affiliation(s)
- Berk A. Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
| | - Michael M. Desai
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
- Department of Physics, Harvard University, Cambridge, MA, United States of America
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7
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Yu C, Lin X, Cheng Y, Xu J, Wang H, Yan Y, Huang Y, Liu L, Zhao W, Zhao Q, Wang J, Zhang L. AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge. Proteins 2024; 92:1147-1160. [PMID: 38441337 DOI: 10.1002/prot.26676] [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: 09/25/2023] [Revised: 02/08/2024] [Accepted: 02/08/2024] [Indexed: 10/26/2024]
Abstract
Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitations including inefficiency and a restricted exploration of the immense space of potential antibodies. To overcome these limitations, we propose a novel method for generating antibody sequences using deep learning algorithms called AbDPP (target-oriented antibody design with pretraining and prior biological knowledge). AbDPP integrates a pretrained model for antibodies with biological region information, enabling the effective use of vast antibody sequence data and intricate biological system understanding to generate sequences. To target specific antigens, AbDPP incorporates an antibody property evaluation model, which is further optimized based on evaluation results to generate more focused sequences. The efficacy of AbDPP was assessed through multiple experiments, evaluating its ability to generate amino acids, improve neutralization and binding, maintain sequence consistency, and improve sequence diversity. Results demonstrated that AbDPP outperformed other methods in terms of the performance and quality of generated sequences, showcasing its potential to enhance antibody design and screening efficiency. In summary, this study contributes to the field by offering an innovative deep learning-based method for antibody generation, addressing some limitations of traditional approaches, and underscoring the importance of integrating a specific antibody pretrained model and the biological properties of antibodies in generating novel sequences. The code and documentation underlying this article are freely available at https://github.com/zlfyj/AbDPP.
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Affiliation(s)
- Chenglei Yu
- Department of Computer Science and Technology, Shanghai Normal University, Shanghai, China
| | - Xiangtian Lin
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Yuxuan Cheng
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Jiahong Xu
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Hao Wang
- Biologicals Innovation and Discovery, WuXi Biologics, Shanghai, China
| | - Yuyao Yan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Yanting Huang
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Lanxuan Liu
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Wei Zhao
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Qin Zhao
- Department of Computer Science and Technology, Shanghai Normal University, Shanghai, China
- Key Laboratory of the Ministry of Education for Embedded System and Service Computing, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Education and Big Data, Shanghai Normal University, Shanghai, China
| | - John Wang
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
| | - Lei Zhang
- Digital Innovation of AI, WuXi Biologics, Shanghai, China
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8
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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. Virus Evol 2024; 10:veae067. [PMID: 39310091 PMCID: PMC11414647 DOI: 10.1093/ve/veae067] [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: 07/24/2024] [Revised: 08/20/2024] [Accepted: 08/28/2024] [Indexed: 09/25/2024] Open
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. 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, 15 N Medical Dr E, Salt Lake City, UT 84112, USA
| | - Tyler N Starr
- Department of Biochemistry, University of Utah School of Medicine, 15 N Medical Dr E, Salt Lake City, UT 84112, USA
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9
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Son A, Park J, Kim W, Lee W, Yoon Y, Ji J, Kim H. Integrating Computational Design and Experimental Approaches for Next-Generation Biologics. Biomolecules 2024; 14:1073. [PMID: 39334841 PMCID: PMC11430650 DOI: 10.3390/biom14091073] [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: 07/23/2024] [Revised: 08/13/2024] [Accepted: 08/26/2024] [Indexed: 09/30/2024] Open
Abstract
Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.
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Affiliation(s)
- Ahrum Son
- Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA;
| | - Jongham Park
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; (J.P.); (W.K.); (W.L.); (Y.Y.)
| | - Woojin Kim
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; (J.P.); (W.K.); (W.L.); (Y.Y.)
| | - Wonseok Lee
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; (J.P.); (W.K.); (W.L.); (Y.Y.)
| | - Yoonki Yoon
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; (J.P.); (W.K.); (W.L.); (Y.Y.)
| | - Jaeho Ji
- Department of Convergent Bioscience and Informatics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea;
| | - Hyunsoo Kim
- Department of Bio-AI Convergence, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea; (J.P.); (W.K.); (W.L.); (Y.Y.)
- Department of Convergent Bioscience and Informatics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea;
- Protein AI Design Institute, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
- SCICS (Sciences for Panomics), 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
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10
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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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11
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Posfai A, Zhou J, McCandlish DM, Kinney JB. Gauge fixing for sequence-function relationships. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593772. [PMID: 38798671 PMCID: PMC11118547 DOI: 10.1101/2024.05.12.593772] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.
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Affiliation(s)
- Anna Posfai
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Juannan Zhou
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
- Department of Biology, University of Florida, Gainesville, FL, 32611
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Justin B. Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
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12
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Ornelas MY, Ouyang WO, Wu NC. A library-on-library screen reveals the breadth expansion landscape of a broadly neutralizing betacoronavirus antibody. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597810. [PMID: 38915656 PMCID: PMC11195093 DOI: 10.1101/2024.06.06.597810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Broadly neutralizing antibodies (bnAbs) typically evolve cross-reactivity breadth through acquiring somatic hypermutations. While evolution of breadth requires improvement of binding to multiple antigenic variants, most experimental evolution platforms select against only one antigenic variant at a time. In this study, a yeast display library-on-library approach was applied to delineate the affinity maturation of a betacoronavirus bnAb, S2P6, against 27 spike stem helix peptides in a single experiment. Our results revealed that the binding affinity landscape of S2P6 varies among different stem helix peptides. However, somatic hypermutations that confer general improvement in binding affinity across different stem helix peptides could also be identified. We further showed that a key somatic hypermutation for breadth expansion involves long-range interaction. Overall, our work not only provides a proof-of-concept for using a library-on-library approach to analyze the evolution of antibody breadth, but also has important implications for the development of broadly protective vaccines.
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Affiliation(s)
- Marya Y. Ornelas
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Wenhao O. Ouyang
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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13
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Alpay BA, Desai MM. Effects of selection stringency on the outcomes of directed evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.09.598029. [PMID: 38895455 PMCID: PMC11185767 DOI: 10.1101/2024.06.09.598029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.
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Affiliation(s)
- Berk A. Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Michael M. Desai
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
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14
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Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. Nat Commun 2024; 15:3974. [PMID: 38730230 PMCID: PMC11087541 DOI: 10.1038/s41467-024-48072-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: 09/29/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Affiliation(s)
- Brian M Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Monica B Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Karson M Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Isabell K Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Cyrus M Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alexis M Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sophia A Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Emily R Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Jenna J Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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15
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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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16
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Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci 2024; 45:255-267. [PMID: 38378385 DOI: 10.1016/j.tips.2024.01.003] [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: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
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Affiliation(s)
- Marissa Mock
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | | | - Peter Grandsard
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Suzanne Edavettal
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Alan Russell
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
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17
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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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18
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Feng Y, Yi J, Yang L, Wang Y, Wen J, Zhao W, Kim P, Zhou X. COV2Var, a function annotation database of SARS-CoV-2 genetic variation. Nucleic Acids Res 2024; 52:D701-D713. [PMID: 37897356 PMCID: PMC10767816 DOI: 10.1093/nar/gkad958] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.
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Affiliation(s)
- Yuzhou Feng
- Department of Laboratory Medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu 610041, China
| | - Jiahao Yi
- School of Big Health, Guizhou Medical University, Guiyang 550025, China
| | - Lin Yang
- Department of Cardiology and Laboratory of Gene Therapy for Heart Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China
| | - Yanfei Wang
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jianguo Wen
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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19
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Taylor AL, Starr TN. Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic drift during SARS-CoV-2 evolution. PLoS Pathog 2023; 19:e1011901. [PMID: 38157379 PMCID: PMC10783747 DOI: 10.1371/journal.ppat.1011901] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/11/2024] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron, but whether such epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including F456L that defines the XBB.1.5-derived EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.
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Affiliation(s)
- Ashley L. Taylor
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Tyler N. Starr
- Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
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20
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Teo QW, Wang Y, Lv H, Tan TJC, Lei R, Mao KJ, Wu NC. Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem. Cell Rep 2023; 42:113410. [PMID: 37976161 PMCID: PMC10872586 DOI: 10.1016/j.celrep.2023.113410] [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: 07/27/2023] [Revised: 09/29/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023] Open
Abstract
IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study reveals the importance of light-chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrate that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to the HA stem, are incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light-chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.
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Affiliation(s)
- Qi Wen Teo
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Yiquan Wang
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Huibin Lv
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Timothy J C Tan
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ruipeng Lei
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Kevin J Mao
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
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21
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Banach BB, Pletnev S, Olia AS, Xu K, Zhang B, Rawi R, Bylund T, Doria-Rose NA, Nguyen TD, Fahad AS, Lee M, Lin BC, Liu T, Louder MK, Madan B, McKee K, O'Dell S, Sastry M, Schön A, Bui N, Shen CH, Wolfe JR, Chuang GY, Mascola JR, Kwong PD, DeKosky BJ. Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site. Nat Commun 2023; 14:7593. [PMID: 37989731 PMCID: PMC10663459 DOI: 10.1038/s41467-023-42098-5] [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: 12/23/2022] [Accepted: 09/25/2023] [Indexed: 11/23/2023] Open
Abstract
The HIV-1 fusion peptide (FP) represents a promising vaccine target, but global FP sequence diversity among circulating strains has limited anti-FP antibodies to ~60% neutralization breadth. Here we evolve the FP-targeting antibody VRC34.01 in vitro to enhance FP-neutralization using site saturation mutagenesis and yeast display. Successive rounds of directed evolution by iterative selection of antibodies for binding to resistant HIV-1 strains establish a variant, VRC34.01_mm28, as a best-in-class antibody with 10-fold enhanced potency compared to the template antibody and ~80% breadth on a cross-clade 208-strain neutralization panel. Structural analyses demonstrate that the improved paratope expands the FP binding groove to accommodate diverse FP sequences of different lengths while also recognizing the HIV-1 Env backbone. These data reveal critical antibody features for enhanced neutralization breadth and potency against the FP site of vulnerability and accelerate clinical development of broad HIV-1 FP-targeting vaccines and therapeutics.
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Affiliation(s)
- Bailey B Banach
- Bioengineering Graduate Program, The University of Kansas, Lawrence, KS, 66045, USA
| | - Sergei Pletnev
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Adam S Olia
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Kai Xu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
- Department of Veterinary Biosciences, The Ohio State University, Columbus, OH, 43210, USA
| | - Baoshan Zhang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Tatsiana Bylund
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Nicole A Doria-Rose
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Thuy Duong Nguyen
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA
| | - Ahmed S Fahad
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA
| | - Myungjin Lee
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Bob C Lin
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Tracy Liu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Mark K Louder
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Bharat Madan
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA
| | - Krisha McKee
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Sijy O'Dell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Mallika Sastry
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Arne Schön
- Department of Biology, John Hopkins University, Baltimore, MD, 21218, USA
| | - Natalie Bui
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA
| | - Chen-Hsiang Shen
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Jacy R Wolfe
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA
| | - Gwo-Yu Chuang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - John R Mascola
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20814, USA.
| | - Brandon J DeKosky
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, 66045, USA.
- Department of Chemical Engineering, The University of Kansas, Lawrence, KS, 66045, USA.
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- The Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA.
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22
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Taylor AL, Starr TN. Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic drift during SARS-CoV-2 evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557279. [PMID: 37745441 PMCID: PMC10515859 DOI: 10.1101/2023.09.11.557279] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron variants, but whether such large epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous RBD deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe many tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including mutations like F456L that define the newly emerging EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.
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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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23
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Xie X, Sun X, Wang Y, Lehner B, Li X. Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles. Nat Commun 2023; 14:5551. [PMID: 37689712 PMCID: PMC10492795 DOI: 10.1038/s41467-023-41188-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/25/2023] [Indexed: 09/11/2023] Open
Abstract
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
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Affiliation(s)
- Xuan Xie
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
| | - Xia Sun
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Yuheng Wang
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain.
- ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
| | - Xianghua Li
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Biomedical and Health Translational Centre of Zhejiang Province, Haizhou East Road 718, Haining, 314400, P. R. China.
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24
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. Bioinformatics 2023; 39:btad446. [PMID: 37478351 PMCID: PMC10477941 DOI: 10.1093/bioinformatics/btad446] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
MOTIVATION Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. RESULTS Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AVAILABILITY AND IMPLEMENTATION All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.
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Affiliation(s)
- Matthew D Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Marshall A Case
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Emily K Makowski
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Protein Folding Disease Initiative, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI 48109-2200, United States
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25
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Teo QW, Wang Y, Lv H, Tan TJ, Lei R, Mao KJ, Wu NC. Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.547908. [PMID: 37461670 PMCID: PMC10350038 DOI: 10.1101/2023.07.06.547908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study revealed the importance of light chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrated that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to HA stem, were incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.
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Affiliation(s)
- Qi Wen Teo
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Yiquan Wang
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Huibin Lv
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Timothy J.C. Tan
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ruipeng Lei
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Kevin J. Mao
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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26
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Pan X, López Acevedo SN, Cuziol C, De Tavernier E, Fahad AS, Longjam PS, Rao SP, Aguilera-Rodríguez D, Rezé M, Bricault CA, Gutiérrez-González MF, de Souza MO, DiNapoli JM, Vigne E, Shahsavarian MA, DeKosky BJ. Large-scale antibody immune response mapping of splenic B cells and bone marrow plasma cells in a transgenic mouse model. Front Immunol 2023; 14:1137069. [PMID: 37346047 PMCID: PMC10280637 DOI: 10.3389/fimmu.2023.1137069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/30/2023] [Indexed: 06/23/2023] Open
Abstract
Molecular characterization of antibody immunity and human antibody discovery is mainly carried out using peripheral memory B cells, and occasionally plasmablasts, that express B cell receptors (BCRs) on their cell surface. Despite the importance of plasma cells (PCs) as the dominant source of circulating antibodies in serum, PCs are rarely utilized because they do not express surface BCRs and cannot be analyzed using antigen-based fluorescence-activated cell sorting. Here, we studied the antibodies encoded by the entire mature B cell populations, including PCs, and compared the antibody repertoires of bone marrow and spleen compartments elicited by immunization in a human immunoglobulin transgenic mouse strain. To circumvent prior technical limitations for analysis of plasma cells, we applied single-cell antibody heavy and light chain gene capture from the entire mature B cell repertoires followed by yeast display functional analysis using a cytokine as a model immunogen. We performed affinity-based sorting of antibody yeast display libraries and large-scale next-generation sequencing analyses to follow antibody lineage performance, with experimental validation of 76 monoclonal antibodies against the cytokine antigen that identified three antibodies with exquisite double-digit picomolar binding affinity. We observed that spleen B cell populations generated higher affinity antibodies compared to bone marrow PCs and that antigen-specific splenic B cells had higher average levels of somatic hypermutation. A degree of clonal overlap was also observed between bone marrow and spleen antibody repertoires, indicating common origins of certain clones across lymphoid compartments. These data demonstrate a new capacity to functionally analyze antigen-specific B cell populations of different lymphoid organs, including PCs, for high-affinity antibody discovery and detailed fundamental studies of antibody immunity.
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Affiliation(s)
- Xiaoli Pan
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, United States
- Ragon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sheila N. López Acevedo
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, United States
| | - Camille Cuziol
- Large Molecule Research, Sanofi, Vitry sur Seine, France
| | | | - Ahmed S. Fahad
- Ragon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | | | - Mathilde Rezé
- Large Molecule Research, Sanofi, Vitry sur Seine, France
| | | | - Matías F. Gutiérrez-González
- Ragon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Matheus Oliveira de Souza
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, United States
- Ragon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | | | - Brandon J. DeKosky
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, United States
- Ragon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Chemical Engineering, The University of Kansas, Lawrence, KS, United States
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27
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Moulana A, Dupic T, Phillips AM, Desai MM. Genotype-phenotype landscapes for immune-pathogen coevolution. Trends Immunol 2023; 44:384-396. [PMID: 37024340 PMCID: PMC10147585 DOI: 10.1016/j.it.2023.03.006] [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: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023]
Abstract
Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA; Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA.
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28
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Chandra S, Manjunath K, Asok A, Varadarajan R. Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA. Protein Sci 2023; 32:e4580. [PMID: 36714997 PMCID: PMC9951195 DOI: 10.1002/pro.4580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/02/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.
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Affiliation(s)
| | | | - Aparna Asok
- Molecular Biophysics Unit, Indian Institute of ScienceBangaloreIndia
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29
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Moulana A, Dupic T, Phillips AM, Chang J, Roffler AA, Greaney AJ, Starr TN, Bloom JD, Desai MM. The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution. eLife 2023; 12:e83442. [PMID: 36803543 PMCID: PMC9949795 DOI: 10.7554/elife.83442] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
The Omicron BA.1 variant of SARS-CoV-2 escapes convalescent sera and monoclonal antibodies that are effective against earlier strains of the virus. This immune evasion is largely a consequence of mutations in the BA.1 receptor binding domain (RBD), the major antigenic target of SARS-CoV-2. Previous studies have identified several key RBD mutations leading to escape from most antibodies. However, little is known about how these escape mutations interact with each other and with other mutations in the RBD. Here, we systematically map these interactions by measuring the binding affinity of all possible combinations of these 15 RBD mutations (215=32,768 genotypes) to 4 monoclonal antibodies (LY-CoV016, LY-CoV555, REGN10987, and S309) with distinct epitopes. We find that BA.1 can lose affinity to diverse antibodies by acquiring a few large-effect mutations and can reduce affinity to others through several small-effect mutations. However, our results also reveal alternative pathways to antibody escape that does not include every large-effect mutation. Moreover, epistatic interactions are shown to constrain affinity decline in S309 but only modestly shape the affinity landscapes of other antibodies. Together with previous work on the ACE2 affinity landscape, our results suggest that the escape of each antibody is mediated by distinct groups of mutations, whose deleterious effects on ACE2 affinity are compensated by another distinct group of mutations (most notably Q498R and N501Y).
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Jeffrey Chang
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Anne A Roffler
- Biological and Biomedical Sciences, Harvard Medical SchoolBostonUnited States
| | - Allison J Greaney
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome Sciences, University of WashingtonSeattleUnited States
- Medical Scientist Training Program, University of WashingtonSeattleUnited States
| | - Tyler N Starr
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome Sciences, University of WashingtonSeattleUnited States
- Howard Hughes Medical InstituteSeattleUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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30
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. CELL REPORTS METHODS 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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31
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Phillips AM, Maurer DP, Brooks C, Dupic T, Schmidt AG, Desai MM. Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody. eLife 2023; 12:83628. [PMID: 36625542 PMCID: PMC9995116 DOI: 10.7554/elife.83628] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Caelan Brooks
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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32
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Chattopadhyay G, Ahmed S, Srilatha NS, Asok A, Varadarajan R. Ter-Seq: A high-throughput method to stabilize transient ternary complexes and measure associated kinetics. Protein Sci 2023; 32:e4514. [PMID: 36382921 PMCID: PMC9793979 DOI: 10.1002/pro.4514] [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: 09/09/2022] [Revised: 11/01/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022]
Abstract
Regulation of biological processes by proteins often involves the formation of transient, multimeric complexes whose characterization is mechanistically important but challenging. The bacterial toxin CcdB binds and poisons DNA Gyrase. The corresponding antitoxin CcdA extracts CcdB from its complex with Gyrase through the formation of a transient ternary complex, thus rejuvenating Gyrase. We describe a high throughput methodology called Ter-Seq to stabilize probable ternary complexes and measure associated kinetics using the CcdA-CcdB-GyrA14 ternary complex as a model system. The method involves screening a yeast surface display (YSD) saturation mutagenesis library of one partner (CcdB) for mutants that show enhanced ternary complex formation. We also isolated CcdB mutants that were either resistant or sensitive to rejuvenation, and used surface plasmon resonance (SPR) with purified proteins to validate the kinetics measured using the surface display. Positions, where CcdB mutations lead to slower rejuvenation rates, are largely involved in CcdA-binding, though there were several notable exceptions suggesting allostery. Mutations at these positions reduce the affinity towards CcdA, thereby slowing down the rejuvenation process. Mutations at GyrA14-interacting positions significantly enhanced rejuvenation rates, either due to reduced affinity or complete loss of CcdB binding to GyrA14. We examined the effect of different parameters (CcdA affinity, GyrA14 affinity, surface accessibilities, evolutionary conservation) on the rate of rejuvenation. Finally, we further validated the Ter-Seq results by monitoring the kinetics of ternary complex formation for individual CcdB mutants in solution by fluorescence resonance energy transfer (FRET) studies.
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Affiliation(s)
- Gopinath Chattopadhyay
- Molecular Biophysics UnitIndian Institute of ScienceBangaloreIndia
- Institute for Evolutionary Biology and Environmental SciencesUniversity of ZurichZurichSwitzerland
| | - Shahbaz Ahmed
- Molecular Biophysics UnitIndian Institute of ScienceBangaloreIndia
- St. Jude Children's Research HospitalTennesseeUSA
| | | | - Aparna Asok
- Molecular Biophysics UnitIndian Institute of ScienceBangaloreIndia
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33
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Zambo B, Morlet B, Negroni L, Trave G, Gogl G. Native holdup (nHU) to measure binding affinities from cell extracts. SCIENCE ADVANCES 2022; 8:eade3828. [PMID: 36542723 PMCID: PMC9770967 DOI: 10.1126/sciadv.ade3828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.
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Affiliation(s)
- Boglarka Zambo
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Bastien Morlet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Luc Negroni
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Gilles Trave
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
| | - Gergo Gogl
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
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Moulana A, Dupic T, Phillips AM, Chang J, Nieves S, Roffler AA, Greaney AJ, Starr TN, Bloom JD, Desai MM. Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat Commun 2022; 13:7011. [PMID: 36384919 PMCID: PMC9668218 DOI: 10.1038/s41467-022-34506-z] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
The Omicron BA.1 variant emerged in late 2021 and quickly spread across the world. Compared to the earlier SARS-CoV-2 variants, BA.1 has many mutations, some of which are known to enable antibody escape. Many of these antibody-escape mutations individually decrease the spike receptor-binding domain (RBD) affinity for ACE2, but BA.1 still binds ACE2 with high affinity. The fitness and evolution of the BA.1 lineage is therefore driven by the combined effects of numerous mutations. Here, we systematically map the epistatic interactions between the 15 mutations in the RBD of BA.1 relative to the Wuhan Hu-1 strain. Specifically, we measure the ACE2 affinity of all possible combinations of these 15 mutations (215 = 32,768 genotypes), spanning all possible evolutionary intermediates from the ancestral Wuhan Hu-1 strain to BA.1. We find that immune escape mutations in BA.1 individually reduce ACE2 affinity but are compensated by epistatic interactions with other affinity-enhancing mutations, including Q498R and N501Y. Thus, the ability of BA.1 to evade immunity while maintaining ACE2 affinity is contingent on acquiring multiple interacting mutations. Our results implicate compensatory epistasis as a key factor driving substantial evolutionary change for SARS-CoV-2 and are consistent with Omicron BA.1 arising from a chronic infection.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
| | - Jeffrey Chang
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA
| | - Serafina Nieves
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Anne A Roffler
- Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, 02115, USA
| | - Allison J Greaney
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, 98195, USA
| | - Tyler N Starr
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA.
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA, 02138, USA.
- Quantitative Biology Initiative, Harvard University, Cambridge, MA, 02138, USA.
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35
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Starr TN, Greaney AJ, Stewart CM, Walls AC, Hannon WW, Veesler D, Bloom JD. Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains. PLoS Pathog 2022; 18:e1010951. [PMID: 36399443 PMCID: PMC9674177 DOI: 10.1371/journal.ppat.1010951] [Citation(s) in RCA: 115] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
SARS-CoV-2 continues to acquire mutations in the spike receptor-binding domain (RBD) that impact ACE2 receptor binding, folding stability, and antibody recognition. Deep mutational scanning prospectively characterizes the impacts of mutations on these biochemical properties, enabling rapid assessment of new mutations seen during viral surveillance. However, the effects of mutations can change as the virus evolves, requiring updated deep mutational scans. We determined the impacts of all single amino acid mutations in the Omicron BA.1 and BA.2 RBDs on ACE2-binding affinity, RBD folding, and escape from binding by the LY-CoV1404 (bebtelovimab) monoclonal antibody. The effects of some mutations in Omicron RBDs differ from those measured in the ancestral Wuhan-Hu-1 background. These epistatic shifts largely resemble those previously seen in the Alpha variant due to the convergent epistatically modifying N501Y substitution. However, Omicron variants show additional lineage-specific shifts, including examples of the epistatic phenomenon of entrenchment that causes the Q498R and N501Y substitutions present in Omicron to be more favorable in that background than in earlier viral strains. In contrast, the Omicron substitution Q493R exhibits no sign of entrenchment, with the derived state, R493, being as unfavorable for ACE2 binding in Omicron RBDs as in Wuhan-Hu-1. Likely for this reason, the R493Q reversion has occurred in Omicron sub-variants including BA.4/BA.5 and BA.2.75, where the affinity buffer from R493Q reversion may potentiate concurrent antigenic change. Consistent with prior studies, we find that Omicron RBDs have reduced expression, and identify candidate stabilizing mutations that ameliorate this deficit. Last, our maps highlight a broadening of the sites of escape from LY-CoV1404 antibody binding in BA.1 and BA.2 compared to the ancestral Wuhan-Hu-1 background. These BA.1 and BA.2 deep mutational scanning datasets identify shifts in the RBD mutational landscape and inform ongoing efforts in viral surveillance.
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Affiliation(s)
- Tyler N. Starr
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Biochemistry, University of Utah, Salt Lake City, Utah, United States of America
| | - Allison J. Greaney
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America
| | - Cameron M. Stewart
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Alexandra C. Walls
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - William W. Hannon
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, Washington, United States of America
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
| | - Jesse D. Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
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36
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Trippe BL, Huang B, DeBenedictis EA, Coventry B, Bhattacharya N, Yang KK, Baker D, Crawford L. Randomized gates eliminate bias in sort‐seq assays. Protein Sci 2022. [PMCID: PMC9601873 DOI: 10.1002/pro.4401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi‐bin sorters.
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Affiliation(s)
- Brian L. Trippe
- Massachusetts Institute of Technology Cambridge Massachusetts USA
- Microsoft Research New England Cambridge Massachusetts USA
- Institute for Protein Design, University of Washington Seattle Washington USA
| | - Buwei Huang
- Institute for Protein Design, University of Washington Seattle Washington USA
- Department of Biochemistry University of Washington Seattle Washington USA
| | - Erika A. DeBenedictis
- Institute for Protein Design, University of Washington Seattle Washington USA
- Department of Biochemistry University of Washington Seattle Washington USA
| | - Brian Coventry
- Institute for Protein Design, University of Washington Seattle Washington USA
- Department of Biochemistry University of Washington Seattle Washington USA
| | - Nicholas Bhattacharya
- Microsoft Research New England Cambridge Massachusetts USA
- Department of Mathematics University of California Berkeley Berkeley California USA
| | - Kevin K. Yang
- Microsoft Research New England Cambridge Massachusetts USA
| | - David Baker
- Institute for Protein Design, University of Washington Seattle Washington USA
- Department of Biochemistry University of Washington Seattle Washington USA
- Howard Hughes Medical Institute, University of Washington Seattle Washington USA
| | - Lorin Crawford
- Microsoft Research New England Cambridge Massachusetts USA
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37
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Banach BB, Tripathi P, Da Silva Pereira L, Gorman J, Nguyen TD, Dillon M, Fahad AS, Kiyuka PK, Madan B, Wolfe JR, Bonilla B, Flynn B, Francica JR, Hurlburt NK, Kisalu NK, Liu T, Ou L, Rawi R, Schön A, Shen CH, Teng IT, Zhang B, Pancera M, Idris AH, Seder RA, Kwong PD, DeKosky BJ. Highly protective antimalarial antibodies via precision library generation and yeast display screening. J Exp Med 2022; 219:e20220323. [PMID: 35736810 PMCID: PMC9242090 DOI: 10.1084/jem.20220323] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/19/2022] [Accepted: 05/23/2022] [Indexed: 02/03/2023] Open
Abstract
The monoclonal antibody CIS43 targets the Plasmodium falciparum circumsporozoite protein (PfCSP) and prevents malaria infection in humans for up to 9 mo following a single intravenous administration. To enhance the potency and clinical utility of CIS43, we used iterative site-saturation mutagenesis and DNA shuffling to screen precise gene-variant yeast display libraries for improved PfCSP antigen recognition. We identified several mutations that improved recognition, predominately in framework regions, and combined these to produce a panel of antibody variants. The most improved antibody, CIS43_Var10, had three mutations and showed approximately sixfold enhanced protective potency in vivo compared to CIS43. Co-crystal and cryo-electron microscopy structures of CIS43_Var10 with the peptide epitope or with PfCSP, respectively, revealed functional roles for each of these mutations. The unbiased site-directed mutagenesis and screening pipeline described here represent a powerful approach to enhance protective potency and to enable broader clinical use of antimalarial antibodies.
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Affiliation(s)
- Bailey B. Banach
- Bioengineering Graduate Program, The University of Kansas, Lawrence, KS
| | - Prabhanshu Tripathi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Lais Da Silva Pereira
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Jason Gorman
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Thuy Duong Nguyen
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS
| | - Marlon Dillon
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Ahmed S. Fahad
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS
| | - Patience K. Kiyuka
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Bharat Madan
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS
| | - Jacy R. Wolfe
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS
| | - Brian Bonilla
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Barbara Flynn
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Joseph R. Francica
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Nicholas K. Hurlburt
- Fred Hutchinson Cancer Research Center, Vaccines and Infectious Diseases Division, Seattle, WA
| | - Neville K. Kisalu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Tracy Liu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Li Ou
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Arne Schön
- Department of Biology, Johns Hopkins University, Baltimore, MD
| | - Chen-Hsiang Shen
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - I-Ting Teng
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Baoshan Zhang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Marie Pancera
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
- Fred Hutchinson Cancer Research Center, Vaccines and Infectious Diseases Division, Seattle, WA
| | - Azza H. Idris
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Robert A. Seder
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Peter D. Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD
| | - Brandon J. DeKosky
- Bioengineering Graduate Program, The University of Kansas, Lawrence, KS
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS
- Department of Chemical Engineering, The University of Kansas, Lawrence, KS
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA
- The Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA
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38
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Starr TN, Greaney AJ, Hannon WW, Loes AN, Hauser K, Dillen JR, Ferri E, Farrell AG, Dadonaite B, McCallum M, Matreyek KA, Corti D, Veesler D, Snell G, Bloom JD. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science 2022; 377:420-424. [PMID: 35762884 PMCID: PMC9273037 DOI: 10.1126/science.abo7896] [Citation(s) in RCA: 167] [Impact Index Per Article: 55.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/23/2022] [Indexed: 12/30/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved variants with substitutions in the spike receptor-binding domain (RBD) that affect its affinity for angiotensin-converting enzyme 2 (ACE2) receptor and recognition by antibodies. These substitutions could also shape future evolution by modulating the effects of mutations at other sites-a phenomenon called epistasis. To investigate this possibility, we performed deep mutational scans to measure the effects on ACE2 binding of all single-amino acid mutations in the Wuhan-Hu-1, Alpha, Beta, Delta, and Eta variant RBDs. Some substitutions, most prominently Asn501→Tyr (N501Y), cause epistatic shifts in the effects of mutations at other sites. These epistatic shifts shape subsequent evolutionary change-for example, enabling many of the antibody-escape substitutions in the Omicron RBD. These epistatic shifts occur despite high conservation of the overall RBD structure. Our data shed light on RBD sequence-function relationships and facilitate interpretation of ongoing SARS-CoV-2 evolution.
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Affiliation(s)
- Tyler N. Starr
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Allison J. Greaney
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA 98109, USA
| | - William W. Hannon
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA 98109, USA
| | - Andrea N. Loes
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | | | | | - Elena Ferri
- Vir Biotechnology, San Francisco, CA 94158, USA
| | - Ariana Ghez Farrell
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Bernadeta Dadonaite
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Matthew McCallum
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Kenneth A. Matreyek
- Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Davide Corti
- Humabs BioMed SA, a subsidiary of Vir Biotechnology, 6500 Bellinzona, Switzerland
| | - David Veesler
- Howard Hughes Medical Institute, Seattle, WA 98109, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | | | - Jesse D. Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
- Howard Hughes Medical Institute, Seattle, WA 98109, USA
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39
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Makowski EK, Kinnunen PC, Huang J, Wu L, Smith MD, Wang T, Desai AA, Streu CN, Zhang Y, Zupancic JM, Schardt JS, Linderman JJ, Tessier PM. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space. Nat Commun 2022; 13:3788. [PMID: 35778381 PMCID: PMC9249733 DOI: 10.1038/s41467-022-31457-3] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/20/2022] [Indexed: 11/08/2022] Open
Abstract
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.
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Affiliation(s)
- Emily K Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jie Huang
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matthew D Smith
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Tiexin Wang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alec A Desai
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Craig N Streu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemistry, Albion College, Albion, MI, 49224, USA
| | - Yulei Zhang
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer M Zupancic
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - John S Schardt
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Peter M Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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40
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Chattopadhyay G, Bhasin M, Ahmed S, Gosain TP, Ganesan S, Das S, Thakur C, Chandra N, Singh R, Varadarajan R. Functional and Biochemical Characterization of the MazEF6 Toxin-Antitoxin System of Mycobacterium tuberculosis. J Bacteriol 2022; 204:e0005822. [PMID: 35357163 PMCID: PMC9053165 DOI: 10.1128/jb.00058-22] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/07/2022] [Indexed: 12/15/2022] Open
Abstract
The Mycobacterium tuberculosis genome harbors nine toxin-antitoxin (TA) systems that are members of the mazEF family, unlike other prokaryotes, which have only one or two. Although the overall tertiary folds of MazF toxins are predicted to be similar, it is unclear how they recognize structurally different RNAs and antitoxins with divergent sequence specificity. Here, we have expressed and purified the individual components and complex of the MazEF6 TA system from M. tuberculosis. Size exclusion chromatography-multiangle light scattering (SEC-MALS) was performed to determine the oligomerization status of the toxin, antitoxin, and the complex in different stoichiometric ratios. The relative stabilities of the proteins were determined by nano-differential scanning fluorimetry (nano-DSF). Microscale thermophoresis (MST) and yeast surface display (YSD) were performed to measure the relative affinities between the cognate toxin-antitoxin partners. The interaction between MazEF6 complexes and cognate promoter DNA was also studied using MST. Analysis of paired-end RNA sequencing data revealed that the overexpression of MazF6 resulted in differential expression of 323 transcripts in M. tuberculosis. Network analysis was performed to identify the nodes from the top-response network. The analysis of mRNA protection ratios resulted in identification of putative MazF6 cleavage site in its native host, M. tuberculosis. IMPORTANCE M. tuberculosis harbors a large number of type II toxin-antitoxin (TA) systems, the exact roles for most of which are unclear. Prior studies have reported that overexpression of several of these type II toxins inhibits bacterial growth and contributes to the formation of drug-tolerant populations in vitro. To obtain insights into M. tuberculosis MazEF6 type II TA system function, we determined stability, oligomeric states, and binding affinities of cognate partners with each other and with their promoter operator DNA. Using RNA-seq data obtained from M. tuberculosis overexpression strains, we have identified putative MazF6 cleavage sites and targets in its native, cellular context.
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Affiliation(s)
| | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Tannu Priya Gosain
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Srivarshini Ganesan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Sayan Das
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
| | - Ramandeep Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
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41
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Faure AJ, Domingo J, Schmiedel JM, Hidalgo-Carcedo C, Diss G, Lehner B. Mapping the energetic and allosteric landscapes of protein binding domains. Nature 2022; 604:175-183. [PMID: 35388192 DOI: 10.1038/s41586-022-04586-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/25/2022] [Indexed: 11/09/2022]
Abstract
Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.
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Affiliation(s)
- Andre J Faure
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Júlia Domingo
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,New York Genome Center (NYGC), New York, NY, USA
| | - Jörn M Schmiedel
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Cristina Hidalgo-Carcedo
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Guillaume Diss
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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42
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Starr TN, Zepeda SK, Walls AC, Greaney AJ, Alkhovsky S, Veesler D, Bloom JD. ACE2 binding is an ancestral and evolvable trait of sarbecoviruses. Nature 2022; 603:913-918. [PMID: 35114688 PMCID: PMC8967715 DOI: 10.1038/s41586-022-04464-z] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 01/25/2022] [Indexed: 11/08/2022]
Abstract
Two different sarbecoviruses have caused major human outbreaks in the past two decades1,2. Both of these sarbecoviruses, SARS-CoV-1 and SARS-CoV-2, engage ACE2 through the spike receptor-binding domain2-6. However, binding to ACE2 orthologues of humans, bats and other species has been observed only sporadically among the broader diversity of bat sarbecoviruses7-11. Here we use high-throughput assays12 to trace the evolutionary history of ACE2 binding across a diverse range of sarbecoviruses and ACE2 orthologues. We find that ACE2 binding is an ancestral trait of sarbecovirus receptor-binding domains that has subsequently been lost in some clades. Furthermore, we reveal that bat sarbecoviruses from outside Asia can bind to ACE2. Moreover, ACE2 binding is highly evolvable-for many sarbecovirus receptor-binding domains, there are single amino-acid mutations that enable binding to new ACE2 orthologues. However, the effects of individual mutations can differ considerably between viruses, as shown by the N501Y mutation, which enhances the human ACE2-binding affinity of several SARS-CoV-2 variants of concern12 but substantially decreases it for SARS-CoV-1. Our results point to the deep ancestral origin and evolutionary plasticity of ACE2 binding, broadening the range of sarbecoviruses that should be considered to have spillover potential.
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Affiliation(s)
- Tyler N Starr
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
| | - Samantha K Zepeda
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Alexandra C Walls
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Allison J Greaney
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sergey Alkhovsky
- N.F. Gamleya National Center of Epidemiology and Microbiology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - David Veesler
- Howard Hughes Medical Institute, Seattle, WA, USA.
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
| | - Jesse D Bloom
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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43
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Zhou Y, Yuan Y, Wu Y, Li L, Jameel A, Xing XH, Zhang C. Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast. ACS Synth Biol 2022; 11:977-989. [PMID: 35089702 DOI: 10.1021/acssynbio.1c00595] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.
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Affiliation(s)
- Yikang Zhou
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yaomeng Yuan
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Yinan Wu
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Lu Li
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Aysha Jameel
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xin-Hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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44
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Hwang T, Parker SS, Hill SM, Grant RA, Ilunga MW, Sivaraman V, Mouneimne G, Keating AE. Native proline-rich motifs exploit sequence context to target actin-remodeling Ena/VASP protein ENAH. eLife 2022; 11:70680. [PMID: 35076015 PMCID: PMC8789275 DOI: 10.7554/elife.70680] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 01/06/2022] [Indexed: 12/13/2022] Open
Abstract
The human proteome is replete with short linear motifs (SLiMs) of four to six residues that are critical for protein-protein interactions, yet the importance of the sequence surrounding such motifs is underexplored. We devised a proteomic screen to examine the influence of SLiM sequence context on protein-protein interactions. Focusing on the EVH1 domain of human ENAH, an actin regulator that is highly expressed in invasive cancers, we screened 36-residue proteome-derived peptides and discovered new interaction partners of ENAH and diverse mechanisms by which context influences binding. A pocket on the ENAH EVH1 domain that has diverged from other Ena/VASP paralogs recognizes extended SLiMs and favors motif-flanking proline residues. Many high-affinity ENAH binders that contain two proline-rich SLiMs use a noncanonical site on the EVH1 domain for binding and display a thermodynamic signature consistent with the two-motif chain engaging a single domain. We also found that photoreceptor cilium actin regulator (PCARE) uses an extended 23-residue region to obtain a higher affinity than any known ENAH EVH1-binding motif. Our screen provides a way to uncover the effects of proteomic context on motif-mediated binding, revealing diverse mechanisms of control over EVH1 interactions and establishing that SLiMs can’t be fully understood outside of their native context.
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Affiliation(s)
- Theresa Hwang
- Department of Biology, Massachusetts Institute of Technology
| | - Sara S Parker
- Department of Cellular & Molecular Medicine, University of Arizona
| | - Samantha M Hill
- Department of Cellular & Molecular Medicine, University of Arizona
| | - Robert A Grant
- Department of Biology, Massachusetts Institute of Technology
| | - Meucci W Ilunga
- Department of Biology, Massachusetts Institute of Technology
| | | | | | - Amy E Keating
- Department of Biology, Massachusetts Institute of Technology
- Department of Biological Engineering and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
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45
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Javanmardi K, Chou CW, Terrace CI, Annapareddy A, Kaoud TS, Guo Q, Lutgens J, Zorkic H, Horton AP, Gardner EC, Nguyen G, Boutz DR, Goike J, Voss WN, Kuo HC, Dalby KN, Gollihar JD, Finkelstein IJ. Rapid characterization of spike variants via mammalian cell surface display. Mol Cell 2021; 81:5099-5111.e8. [PMID: 34919820 PMCID: PMC8675084 DOI: 10.1016/j.molcel.2021.11.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/16/2021] [Accepted: 11/19/2021] [Indexed: 12/21/2022]
Abstract
The SARS-CoV-2 spike protein is a critical component of vaccines and a target for neutralizing monoclonal antibodies (nAbs). Spike is also undergoing immunogenic selection with variants that increase infectivity and partially escape convalescent plasma. Here, we describe Spike Display, a high-throughput platform to rapidly characterize glycosylated spike ectodomains across multiple coronavirus-family proteins. We assayed ∼200 variant SARS-CoV-2 spikes for their expression, ACE2 binding, and recognition by 13 nAbs. An alanine scan of all five N-terminal domain (NTD) loops highlights a public epitope in the N1, N3, and N5 loops recognized by most NTD-binding nAbs. NTD mutations in variants of concern B.1.1.7 (alpha), B.1.351 (beta), B.1.1.28 (gamma), B.1.427/B.1.429 (epsilon), and B.1.617.2 (delta) impact spike expression and escape most NTD-targeting nAbs. Finally, B.1.351 and B.1.1.28 completely escape a potent ACE2 mimic. We anticipate that Spike Display will accelerate antigen design, deep scanning mutagenesis, and antibody epitope mapping for SARS-CoV-2 and other emerging viral threats.
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Affiliation(s)
- Kamyab Javanmardi
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Chia-Wei Chou
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | | | - Ankur Annapareddy
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tamer S Kaoud
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, The University of Texas at Austin, Austin, TX, USA
| | - Qingqing Guo
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Josh Lutgens
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Hayley Zorkic
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Andrew P Horton
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Elizabeth C Gardner
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Giaochau Nguyen
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | | | - Jule Goike
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - William N Voss
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Hung-Che Kuo
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kevin N Dalby
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, The University of Texas at Austin, Austin, TX, USA
| | - Jimmy D Gollihar
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA; CCDC Army Research Laboratory-South, Austin, TX, USA; Center for Molecular and Translational Human Infectious Diseases Research, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA; Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA
| | - Ilya J Finkelstein
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA; Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX, USA.
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46
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Hanning KR, Minot M, Warrender AK, Kelton W, Reddy ST. Deep mutational scanning for therapeutic antibody engineering. Trends Pharmacol Sci 2021; 43:123-135. [PMID: 34895944 DOI: 10.1016/j.tips.2021.11.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022]
Abstract
The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.
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Affiliation(s)
- Kyrin R Hanning
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - Mason Minot
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland
| | - Annmaree K Warrender
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand
| | - William Kelton
- Te Huataki Waiora School of Health, University of Waikato, Hamilton 3240, New Zealand.
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zurich, Basel 4058, Switzerland.
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47
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Francino-Urdaniz IM, Whitehead TA. An overview of methods for the structural and functional mapping of epitopes recognized by anti-SARS-CoV-2 antibodies. RSC Chem Biol 2021; 2:1580-1589. [PMID: 34977572 PMCID: PMC8637828 DOI: 10.1039/d1cb00169h] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/25/2021] [Indexed: 12/20/2022] Open
Abstract
This mini-review presents a critical survey of techniques used for epitope mapping on the SARS-CoV-2 Spike protein. The sequence and structures for common neutralizing and non-neutralizing epitopes on the Spike protein are described as determined by X-ray crystallography, electron microscopy and linear peptide epitope mapping, among other methods. An additional focus of this mini-review is an analytical appraisal of different deep mutational scanning workflows for conformational epitope mapping and identification of mutants on the Spike protein which escape antibody neutralization. Such a focus is necessary as a critical review of deep mutational scanning for conformational epitope mapping has not been published. A perspective is presented on the use of different epitope determination methods for development of broadly potent antibody therapies and vaccines against SARS-CoV-2.
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Affiliation(s)
- Irene M Francino-Urdaniz
- Department of Chemical and Biological Engineering, University of Colorado JSC Biotechnology Building, 3415 Colorado Avenue Boulder CO 80305 USA +1 303-735-2145
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado JSC Biotechnology Building, 3415 Colorado Avenue Boulder CO 80305 USA +1 303-735-2145
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48
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Neutralizing Monoclonal Antibodies That Target the Spike Receptor Binding Domain Confer Fc Receptor-Independent Protection against SARS-CoV-2 Infection in Syrian Hamsters. mBio 2021; 12:e0239521. [PMID: 34517754 PMCID: PMC8546861 DOI: 10.1128/mbio.02395-21] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein is the main target for neutralizing antibodies. These antibodies can be elicited through immunization or passively transferred as therapeutics in the form of convalescent-phase sera or monoclonal antibodies (MAbs). Potently neutralizing antibodies are expected to confer protection; however, it is unclear whether weakly neutralizing antibodies contribute to protection. Also, their mechanism of action in vivo is incompletely understood. Here, we demonstrate that 2B04, an antibody with an ultrapotent neutralizing activity (50% inhibitory concentration [IC50] of 0.04 μg/ml), protects hamsters against SARS-CoV-2 in a prophylactic and therapeutic infection model. Protection is associated with reduced weight loss and viral loads in nasal turbinates and lungs after challenge. MAb 2B04 also blocked aerosol transmission of the virus to naive contacts. We next examined three additional MAbs (2C02, 2C03, and 2E06), recognizing distinct epitopes within the receptor binding domain of spike protein that possess either minimal (2C02 and 2E06, IC50 > 20 μg/ml) or weak (2C03, IC50 of 5 μg/ml) virus neutralization capacity in vitro. Only 2C03 protected Syrian hamsters from weight loss and reduced lung viral load after SARS-CoV-2 infection. Finally, we demonstrated that Fc-Fc receptor interactions were not required for protection when 2B04 and 2C03 were administered prophylactically. These findings inform the mechanism of protection and support the rational development of antibody-mediated protection against SARS-CoV-2 infections.
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49
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Phillips AM, Lawrence KR, Moulana A, Dupic T, Chang J, Johnson MS, Cvijovic I, Mora T, Walczak AM, Desai MM. Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 2021; 10:71393. [PMID: 34491198 PMCID: PMC8476123 DOI: 10.7554/elife.71393] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/05/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past two decades, several broadly neutralizing antibodies (bnAbs) that confer protection against diverse influenza strains have been isolated. Structural and biochemical characterization of these bnAbs has provided molecular insight into how they bind distinct antigens. However, our understanding of the evolutionary pathways leading to bnAbs, and thus how best to elicit them, remains limited. Here, we measure equilibrium dissociation constants of combinatorially complete mutational libraries for two naturally isolated influenza bnAbs (CR9114, 16 heavy-chain mutations; CR6261, 11 heavy-chain mutations), reconstructing all possible evolutionary intermediates back to the unmutated germline sequences. We find that these two libraries exhibit strikingly different patterns of breadth: while many variants of CR6261 display moderate affinity to diverse antigens, those of CR9114 display appreciable affinity only in specific, nested combinations. By examining the extensive pairwise and higher order epistasis between mutations, we find key sites with strong synergistic interactions that are highly similar across antigens for CR6261 and different for CR9114. Together, these features of the binding affinity landscapes strongly favor sequential acquisition of affinity to diverse antigens for CR9114, while the acquisition of breadth to more similar antigens for CR6261 is less constrained. These results, if generalizable to other bnAbs, may explain the molecular basis for the widespread observation that sequential exposure favors greater breadth, and such mechanistic insight will be essential for predicting and eliciting broadly protective immune responses.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Katherine R Lawrence
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Massachusetts Institute of Technology, Cambridge, United States
| | - Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Jeffrey Chang
- Department of Physics, Harvard University, Cambridge, United States
| | - Milo S Johnson
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Ivana Cvijovic
- Department of Applied Physics, Stanford University, Stanford, United States
| | - Thierry Mora
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de ÍÉcole Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.,Quantitative Biology Initiative, Harvard University, Cambridge, United States.,Department of Physics, Harvard University, Cambridge, United States
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50
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Coyote-Maestas W, Fraser JS. ORACLE reveals a bright future to fight bacteria. eLife 2021; 10:68277. [PMID: 33856343 PMCID: PMC8049741 DOI: 10.7554/elife.68277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
A new way to alter the genome of bacteriophages helps produce large libraries of variants, allowing these bacteria-killing viruses to be designed to target species harmful to human health.
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Affiliation(s)
- Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, United States.,Quantitative Biosciences Institute, UCSF, San Francisco, United States
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, United States.,Quantitative Biosciences Institute, UCSF, San Francisco, United States
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