1
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Parkinson J, Hard R, Ko YS, Wang W. RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605700. [PMID: 39131296 PMCID: PMC11312550 DOI: 10.1101/2024.07.30.605700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Discovery of therapeutic antibodies against infectious disease pathogens presents distinct challenges. Ideal candidates must possess not only the properties required for any therapeutic antibody (e.g. specificity, low immunogenicity) but also high affinity to many mutants of the target antigen. Here we present RESP2, an enhanced version of our RESP pipeline, designed for the discovery of antibodies against diverse antigens with simultaneously optimized developability properties. RESP2 provides a suite of methods to estimate the uncertainty of predictions including a new model combining neural network and Gaussian process with great flexibility to model protein engineering data, which accelerates in silico directed evolution to identify tight binders even those not present in the original screening library. An interpretable model is then exploited to assess antibody humanness to minimize immunogenicity risk of the selected candidates. To demonstrate the power of this pipeline, we use the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discover a highly human antibody with broad (mid to high-affinity) binding to at least 8 different variants of the RBD. These results illustrate the advantages of this pipeline for antibody discovery against a challenging target. The code needed to reproduce the experiments in this paper is available at https://github.com/Wang-lab-UCSD/RESP2.
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Affiliation(s)
- Jonathan Parkinson
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359
- MAP Bioscience, La Jolla, CA 92093
| | - Ryan Hard
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359
| | - Young Su Ko
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093-0359
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2
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Thalén NB, Karlander M, Lundqvist M, Persson H, Hofström C, Turunen SP, Godzwon M, Volk AL, Malm M, Ohlin M, Rockberg J. Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping. Commun Biol 2024; 7:805. [PMID: 38961245 PMCID: PMC11222437 DOI: 10.1038/s42003-024-06508-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.
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Affiliation(s)
- Niklas Berndt Thalén
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Maximilian Karlander
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Magnus Lundqvist
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Helena Persson
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Camilla Hofström
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - S Pauliina Turunen
- Science for Life Laboratory, Drug Discovery and Development Platform & School of Biotechnology, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Anna-Luisa Volk
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Magdalena Malm
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Mats Ohlin
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Johan Rockberg
- Department Protein science, KTH-Royal Institute of Technology, Stockholm, SE-106 91, Sweden.
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3
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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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4
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Mondal A, Singh B, Felkner RH, De Falco A, Swapna G, Montelione GT, Roth MJ, Perez A. A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries. Angew Chem Int Ed Engl 2024; 63:e202405767. [PMID: 38588243 DOI: 10.1002/anie.202405767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/10/2024]
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.
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Affiliation(s)
- Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
| | - Bhumika Singh
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
| | - Roland H Felkner
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854, USA
| | - Anna De Falco
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Gvt Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Monica J Roth
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854, USA
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
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5
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Chambers MJ, Scobell SB, Sadhu MJ. Systematic genetic characterization of the human PKR kinase domain highlights its functional malleability to escape a viral substrate mimic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596416. [PMID: 38903081 PMCID: PMC11188142 DOI: 10.1101/2024.05.29.596416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Evolutionary arms races can arise at the contact surfaces between host and viral proteins, producing dynamic spaces in which genetic variants are continually pursued. However, the sampling of genetic variation must be balanced with the need to maintain protein function. A striking case is given by protein kinase R (PKR), a member of the mammalian innate immune system. PKR detects viral replication within the host cell and halts protein synthesis to prevent viral replication by phosphorylating eIF2α, a component of the translation initiation machinery. PKR is targeted by many viral antagonists, including poxvirus pseudosubstrate antagonists that inhibit PKR by interacting with the same binding surface as eIF2α. Remarkably, PKR has several rapidly evolving residues at this interface, suggesting it is engaging in an evolutionary arms race, despite the surface's critical role in phosphorylating eIF2α. To systematically explore the evolutionary opportunities available at this dynamic interface, we generated and characterized a library of 426 SNP-accessible nonsynonymous variants of human PKR for their ability to escape inhibition by the model pseudosubstrate inhibitor K3 from vaccinia virus. We identified key sites in the PKR kinase domain that harbor K3-resistant variants, as well as critical sites where variation leads to loss of function. We find K3-resistant variants are readily available throughout the interface and are enriched at sites under positive selection. Moreover, variants beneficial against K3 were also beneficial against an enhanced variant of K3, indicating resilience to viral adaptation. Overall, we find that the eIF2α-binding surface of PKR is highly malleable, potentiating its evolutionary ability to combat viral inhibition.
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Affiliation(s)
- Michael J Chambers
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Department of Microbiology & Immunology, Georgetown University, Washington DC, USA
| | - Sophia B Scobell
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Meru J Sadhu
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
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6
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Deichmann M, Hansson FG, Jensen ED. Yeast-based screening platforms to understand and improve human health. Trends Biotechnol 2024:S0167-7799(24)00095-7. [PMID: 38677901 DOI: 10.1016/j.tibtech.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/29/2024]
Abstract
Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.
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Affiliation(s)
- Marcus Deichmann
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Frederik G Hansson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Emil D Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.
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7
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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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8
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Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
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Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
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9
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Mondal A, Singh B, Felkner RH, De Falco A, Swapna GVT, Montelione GT, Roth MJ, Perez A. Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576374. [PMID: 38328039 PMCID: PMC10849530 DOI: 10.1101/2024.01.20.576374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.
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Affiliation(s)
- Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Bhumika Singh
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Roland H. Felkner
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Anna De Falco
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - GVT Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Monica J. Roth
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
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10
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Jansson JK, McClure R, Egbert RG. Soil microbiome engineering for sustainability in a changing environment. Nat Biotechnol 2023; 41:1716-1728. [PMID: 37903921 DOI: 10.1038/s41587-023-01932-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/01/2023] [Indexed: 11/01/2023]
Abstract
Recent advances in microbial ecology and synthetic biology have the potential to mitigate damage caused by anthropogenic activities that are deleteriously impacting Earth's soil ecosystems. Here, we discuss challenges and opportunities for harnessing natural and synthetic soil microbial communities, focusing on plant growth promotion under different scenarios. We explore current needs for microbial solutions in soil ecosystems, how these solutions are being developed and applied, and the potential for new biotechnology breakthroughs to tailor and target microbial products for specific applications. We highlight several scientific and technological advances in soil microbiome engineering, including characterization of microbes that impact soil ecosystems, directing how microbes assemble to interact in soil environments, and the developing suite of gene-engineering approaches. This Review underscores the need for an interdisciplinary approach to understand the composition, dynamics and deployment of beneficial soil microbiomes to drive efforts to mitigate or reverse environmental damage by restoring and protecting healthy soil ecosystems.
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Affiliation(s)
- Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Ryan McClure
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert G Egbert
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
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11
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Robbins M. Therapies for Tau-associated neurodegenerative disorders: targeting molecules, synapses, and cells. Neural Regen Res 2023; 18:2633-2637. [PMID: 37449601 PMCID: PMC10358644 DOI: 10.4103/1673-5374.373670] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/14/2023] [Accepted: 03/15/2023] [Indexed: 07/18/2023] Open
Abstract
Advances in experimental and computational technologies continue to grow rapidly to provide novel avenues for the treatment of neurodegenerative disorders. Despite this, there remain only a handful of drugs that have shown success in late-stage clinical trials for Tau-associated neurodegenerative disorders. The most commonly prescribed treatments are symptomatic treatments such as cholinesterase inhibitors and N-methyl-D-aspartate receptor blockers that were approved for use in Alzheimer's disease. As diagnostic screening can detect disorders at earlier time points, the field needs pre-symptomatic treatments that can prevent, or significantly delay the progression of these disorders (Koychev et al., 2019). These approaches may be different from late-stage treatments that may help to ameliorate symptoms and slow progression once symptoms have become more advanced should early diagnostic screening fail. This mini-review will highlight five key avenues of academic and industrial research for identifying therapeutic strategies to treat Tau-associated neurodegenerative disorders. These avenues include investigating (1) the broad class of chemicals termed "small molecules"; (2) adaptive immunity through both passive and active antibody treatments; (3) innate immunity with an emphasis on microglial modulation; (4) synaptic compartments with the view that Tau-associated neurodegenerative disorders are synaptopathies. Although this mini-review will focus on Alzheimer's disease due to its prevalence, it will also argue the need to target other tauopathies, as through understanding Alzheimer's disease as a Tau-associated neurodegenerative disorder, we may be able to generalize treatment options. For this reason, added detail linking back specifically to Tau protein as a direct therapeutic target will be added to each topic.
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Affiliation(s)
- Miranda Robbins
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Ave, Trumpington, Cambridge, UK; University of Cambridge, Department of Zoology, Cambridge, UK
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12
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Baryshev A, La Fleur A, Groves B, Michel C, Baker D, Ljubetič A, Seelig G. Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527770. [PMID: 36798377 PMCID: PMC9934699 DOI: 10.1101/2023.02.08.527770] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.
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Affiliation(s)
- Alexander Baryshev
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Alyssa La Fleur
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Benjamin Groves
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Cirstyn Michel
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department for Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana SI-1000, Slovenia
| | - Georg Seelig
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
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13
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Boldridge WC, Ljubetič A, Kim H, Lubock N, Szilágyi D, Lee J, Brodnik A, Jerala R, Kosuri S. A multiplexed bacterial two-hybrid for rapid characterization of protein-protein interactions and iterative protein design. Nat Commun 2023; 14:4636. [PMID: 37532706 PMCID: PMC10397247 DOI: 10.1038/s41467-023-38697-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 05/11/2023] [Indexed: 08/04/2023] Open
Abstract
Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Coiled-coils have been used as a model to study the sequence determinants of specificity. However, building well-behaved sets of orthogonal pairs of coiled-coils remains challenging due to inaccurate predictions of orthogonality and difficulties in testing at scale. To address this, we develop the next-generation bacterial two-hybrid (NGB2H) method, which allows for the rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way using next-generation sequencing readout. We design, build, and test large sets of orthogonal synthetic coiled-coils, assayed over 8,000 PPIs, and used the dataset to train a more accurate coiled-coil scoring algorithm (iCipa). After characterizing nearly 18,000 new PPIs, we identify to the best of our knowledge the largest set of orthogonal coiled-coils to date, with fifteen on-target interactions. Our approach provides a powerful tool for the design of orthogonal PPIs.
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Affiliation(s)
- W Clifford Boldridge
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA
| | - Ajasja Ljubetič
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, 1000, Ljubljana, Slovenia.
- EN-FIST Centre of Excellence, 1000, Ljubljana, Slovenia.
| | - Hwangbeom Kim
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA
- Samsung Biologics, Incheon, Republic of Korea
| | - Nathan Lubock
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA
- Octant Inc, Emeryville, CA, 94608, USA
| | | | - Jonathan Lee
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | | | - Roman Jerala
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, 1000, Ljubljana, Slovenia.
- EN-FIST Centre of Excellence, 1000, Ljubljana, Slovenia.
| | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, USA.
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Octant Inc, Emeryville, CA, 94608, USA.
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14
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Yang A, Jude KM, Lai B, Minot M, Kocyla AM, Glassman CR, Nishimiya D, Kim YS, Reddy ST, Khan AA, Garcia KC. Deploying synthetic coevolution and machine learning to engineer protein-protein interactions. Science 2023; 381:eadh1720. [PMID: 37499032 PMCID: PMC10403280 DOI: 10.1126/science.adh1720] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/16/2023] [Indexed: 07/29/2023]
Abstract
Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.
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Affiliation(s)
- Aerin Yang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kevin M. Jude
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ben Lai
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Mason Minot
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Anna M. Kocyla
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Caleb R. Glassman
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daisuke Nishimiya
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yoon Seok Kim
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Aly A. Khan
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
- Departments of Pathology, and Family Medicine, University of Chicago, Chicago, IL 60637, USA
| | - K. Christopher Garcia
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
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15
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Li L, Gupta E, Spaeth J, Shing L, Jaimes R, Engelhart E, Lopez R, Caceres RS, Bepler T, Walsh ME. Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries. Nat Commun 2023; 14:3454. [PMID: 37308471 DOI: 10.1038/s41467-023-39022-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/23/2023] [Indexed: 06/14/2023] Open
Abstract
Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured. In a head-to-head comparison with a directed evolution approach, we show that the best scFv generated from our method represents a 28.7-fold improvement in binding over the best scFv from the directed evolution. Additionally, 99% of designed scFvs in our most successful library are improvements over the initial candidate scFv. By comparing a library's predicted success to actual measurements, we demonstrate our method's ability to explore tradeoffs between library success and diversity. Results of our work highlight the significant impact machine learning models can have on scFv development. We expect our method to be broadly applicable and provide value to other protein engineering tasks.
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Affiliation(s)
- Lin Li
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA.
| | - Esther Gupta
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - John Spaeth
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Leslie Shing
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Rafael Jaimes
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | | | | | - Rajmonda S Caceres
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Tristan Bepler
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Matthew E Walsh
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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16
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Davey NE, Simonetti L, Ivarsson Y. The next wave of interactomics: Mapping the SLiM-based interactions of the intrinsically disordered proteome. Curr Opin Struct Biol 2023; 80:102593. [PMID: 37099901 DOI: 10.1016/j.sbi.2023.102593] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/09/2023] [Accepted: 03/17/2023] [Indexed: 04/28/2023]
Abstract
Short linear motifs (SLiMs) are a unique and ubiquitous class of protein interaction modules that perform key regulatory functions and drive dynamic complex formation. For decades, interactions mediated by SLiMs have accumulated through detailed low-throughput experiments. Recent methodological advances have opened this previously underexplored area of the human interactome to high-throughput protein-protein interaction discovery. In this article, we discuss that SLiM-based interactions represent a significant blind spot in the current interactomics data, introduce the key methods that are illuminating the elusive SLiM-mediated interactome of the human cell on a large scale, and discuss the implications for the field.
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Affiliation(s)
- Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
| | - Leandro Simonetti
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
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17
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Lopez-Morales J, Vanella R, Kovacevic G, Santos MS, Nash MA. Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator. ACS Synth Biol 2023; 12:419-431. [PMID: 36728831 PMCID: PMC9942200 DOI: 10.1021/acssynbio.2c00351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.
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Affiliation(s)
- Joanan Lopez-Morales
- Department
of Chemistry, University of Basel, Basel 4058, Switzerland,Swiss
Nanoscience Institute, University of Basel, Basel 4056, Switzerland,Department
of Biosystems Science and Engineering, ETH
Zurich, Basel 4058, Switzerland
| | - Rosario Vanella
- Department
of Chemistry, University of Basel, Basel 4058, Switzerland,Department
of Biosystems Science and Engineering, ETH
Zurich, Basel 4058, Switzerland
| | - Gordana Kovacevic
- Department
of Chemistry, University of Basel, Basel 4058, Switzerland,Department
of Biosystems Science and Engineering, ETH
Zurich, Basel 4058, Switzerland
| | - Mariana Sá Santos
- Department
of Chemistry, University of Basel, Basel 4058, Switzerland,Department
of Biosystems Science and Engineering, ETH
Zurich, Basel 4058, Switzerland
| | - Michael A. Nash
- Department
of Chemistry, University of Basel, Basel 4058, Switzerland,Swiss
Nanoscience Institute, University of Basel, Basel 4056, Switzerland,Department
of Biosystems Science and Engineering, ETH
Zurich, Basel 4058, Switzerland,
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18
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A focus on yeast mating: From pheromone signaling to cell-cell fusion. Semin Cell Dev Biol 2023; 133:83-95. [PMID: 35148940 DOI: 10.1016/j.semcdb.2022.02.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Abstract
Cells live in a chemical environment and are able to orient towards chemical cues. Unicellular haploid fungal cells communicate by secreting pheromones to reproduce sexually. In the yeast models Saccharomyces cerevisiae and Schizosaccharomyces pombe, pheromonal communication activates similar pathways composed of cognate G-protein-coupled receptors and downstream small GTPase Cdc42 and MAP kinase cascades. Local pheromone release and sensing, at a mobile surface polarity patch, underlie spatial gradient interpretation to form pairs between two cells of distinct mating types. Concentration of secretion at the point of cell-cell contact then leads to local cell wall digestion for cell fusion, forming a diploid zygote that prevents further fusion attempts. A number of asymmetries between mating types may promote efficiency of the system. In this review, we present our current knowledge of pheromone signaling in the two model yeasts, with an emphasis on how cells decode the pheromone signal spatially and ultimately fuse together. Though overall pathway architectures are similar in the two species, their large evolutionary distance allows to explore how conceptually similar solutions to a general biological problem can arise from divergent molecular components.
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19
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Velásquez-Zapata V, Elmore JM, Wise RP. Bioinformatic Analysis of Yeast Two-Hybrid Next-Generation Interaction Screen Data. Methods Mol Biol 2023; 2690:223-239. [PMID: 37450151 DOI: 10.1007/978-1-0716-3327-4_20] [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] [Indexed: 07/18/2023]
Abstract
Yeast two-hybrid next-generation interaction screening (Y2H-NGIS) uses the output of next-generation sequencing to mine for novel protein-protein interactions. Here, we outline the analytics underlying Y2H-NGIS datasets. Different systems, libraries, and experimental designs comprise Y2H-NGIS methodologies. We summarize the analysis in several layers that comprise the characterization of baits and preys, quantification, and identification of true interactions for subsequent secondary validation. We present two software designed for this purpose, NGPINT and Y2H-SCORES, which are used as front-end and back-end tools in the analysis. Y2H-SCORES software can be used and adapted to analyze different datasets not only from Y2H-NGIS but from other techniques ruled by similar biological principles.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA.
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA.
| | - J Mitch Elmore
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA
- USDA-Agricultural Research Service, Cereal Disease Laboratory, St. Paul, MN, USA
- USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research, Ames, IA, USA
| | - Roger P Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA, USA.
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA.
- USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research, Ames, IA, USA.
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20
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Engelhart E, Emerson R, Shing L, Lennartz C, Guion D, Kelley M, Lin C, Lopez R, Younger D, Walsh ME. A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide. Sci Data 2022; 9:653. [PMID: 36289234 PMCID: PMC9606274 DOI: 10.1038/s41597-022-01779-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
The dataset presented here contains quantitative binding scores of scFv-format antibodies against a SARS-CoV-2 target peptide collected via an AlphaSeq assay that can be used in the development and benchmarking of machine learning models. Starting from three seed sequences identified from a phage display campaign using a human naïve library, four sets of 29,900 antibodies were designed in silico by creating all k = 1 mutations and random k = 2 and k = 3 mutations throughout the complementary-determining regions (CDRs). Of the 119,600 designs, 104,972 were successfully built in to the AlphaSeq library and target binding was subsequently measured with 71,384 designs resulting in a predicted affinity value for at least one of the triplicate measurements. Data include antibodies with predicted affinity measurements ranging from 37 pM to 22 mM. To our knowledge, this dataset is the largest, publicly available dataset that contains antibody sequences, antigen sequence and quantitative measurements of binding scores and provides an opportunity to serve as a benchmark to evaluate antibody-specific representation models for machine learning. Measurement(s) | Antibody Binding | Technology Type(s) | AlphaSeq | Factor Type(s) | Antibody sequence | Sample Characteristic - Organism | Homo sapiens |
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Affiliation(s)
| | | | - Leslie Shing
- grid.504876.80000 0001 0684 1626Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA USA
| | - Chelsea Lennartz
- grid.504876.80000 0001 0684 1626Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA USA
| | | | | | | | | | | | - Matthew E. Walsh
- grid.504876.80000 0001 0684 1626Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA USA ,grid.21107.350000 0001 2171 9311Present Address: Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
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21
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Chao G, Wannier TM, Gutierrez C, Borders NC, Appleton E, Chadha A, Lebar T, Church GM. helixCAM: A platform for programmable cellular assembly in bacteria and human cells. Cell 2022; 185:3551-3567.e39. [PMID: 36055250 PMCID: PMC9481732 DOI: 10.1016/j.cell.2022.08.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/09/2022] [Accepted: 08/11/2022] [Indexed: 01/26/2023]
Abstract
Interactions between cells are indispensable for signaling and creating structure. The ability to direct precise cell-cell interactions would be powerful for engineering tissues, understanding signaling pathways, and directing immune cell targeting. In humans, intercellular interactions are mediated by cell adhesion molecules (CAMs). However, endogenous CAMs are natively expressed by many cells and tend to have cross-reactivity, making them unsuitable for programming specific interactions. Here, we showcase "helixCAM," a platform for engineering synthetic CAMs by presenting coiled-coil peptides on the cell surface. helixCAMs were able to create specific cell-cell interactions and direct patterned aggregate formation in bacteria and human cells. Based on coiled-coil interaction principles, we built a set of rationally designed helixCAM libraries, which led to the discovery of additional high-performance helixCAM pairs. We applied this helixCAM toolkit for various multicellular engineering applications, such as spherical layering, adherent cell targeting, and surface patterning.
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Affiliation(s)
- George Chao
- Genetics Department, Harvard Medical School, Boston, MA 02115, USA.
| | | | - Clair Gutierrez
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Evan Appleton
- Genetics Department, Harvard Medical School, Boston, MA 02115, USA
| | - Anjali Chadha
- Department of Bioengineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tina Lebar
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - George M Church
- Genetics Department, Harvard Medical School, Boston, MA 02115, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
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22
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Feuer E, Zimran G, Shpilman M, Mosquna A. A Modified Yeast Two-Hybrid Platform Enables Dynamic Control of Expression Intensities to Unmask Properties of Protein-Protein Interactions. ACS Synth Biol 2022; 11:2589-2598. [PMID: 35895499 PMCID: PMC9442787 DOI: 10.1021/acssynbio.2c00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The yeast two-hybrid (Y2H) assay is widely used for protein-protein interaction characterization due to its simplicity and accessibility. However, it may mask changes in affinity caused by mutations or ligand activation due to signal saturation. To overcome this drawback, we modified the Y2H system to have tunable protein expression by introducing a fluorescent reporter and a pair of synthetic inducible transcription factors to regulate the expression of interacting components. We found that the application of inducers allowed us to adjust the concentrations of interacting proteins to avoid saturation and observe interactions otherwise masked in the canonical Y2H assay, such as the abscisic acid-mediated increase in affinity of monomeric abscisic acid receptors to the coreceptor. When applied in future studies, our modified system may provide a more accurate characterization of protein-protein interactions.
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Affiliation(s)
- Erez Feuer
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610000, Israel
| | - Gil Zimran
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610000, Israel
| | - Michal Shpilman
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610000, Israel
| | - Assaf Mosquna
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot 7610000, Israel
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23
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Engelhart E, Lopez R, Emerson R, Lin C, Shikany C, Guion D, Kelley M, Younger D. Massively Multiplexed Affinity Characterization of Therapeutic Antibodies Against SARS-CoV-2 Variants. Antib Ther 2022; 5:130-137. [PMID: 35769631 PMCID: PMC9129155 DOI: 10.1093/abt/tbac011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Antibody therapies represent a valuable tool to reduce COVID-19 deaths and hospitalizations. Multiple antibody candidates have been granted emergency use authorization by the FDA and many more are in clinical trials. Most antibody therapies for COVID-19 are engineered to bind to the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein and disrupt its interaction with ACE2. Notably, several SARS-CoV-2 strains have accrued mutations throughout the RBD that improve ACE2 binding affinity, enhance viral transmission, and escape some existing antibody therapies. Here, we measure the binding affinity of 33 therapeutic antibodies against a large panel of SARS-CoV-2 variants and related strains of clinical significance using AlphaSeq, a high-throughput yeast mating based assay to determine epitopic residues, determine which mutations result in loss of binding, and predict how future RBD variants may impact antibody efficacy. We find that some antibodies are more susceptible to mutations in the RBD than others, for example Imdevimab and Sotrovimab display no loss in binding to B.1.1.7, B.1.351 and P.1 variants whereas we observe a 10-fold loss in binding of Etesevimab and Regdanvimab to these variants.
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24
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Wang L, Lan X. Rapid screening of TCR-pMHC interactions by the YAMTAD system. Cell Discov 2022; 8:30. [PMID: 35379810 PMCID: PMC8979966 DOI: 10.1038/s41421-022-00386-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 02/06/2022] [Indexed: 01/03/2023] Open
Abstract
Personalized immunotherapy, such as cancer vaccine and TCR-T methods, demands rapid screening of TCR-pMHC interactions. While several screening approaches have been developed, their throughput is limited. Here, the Yeast Agglutination Mediated TCR antigen Discovery system (YAMTAD) was designed and demonstrated to allow fast and unbiased library-on-library screening of TCR-pMHC interactions. Our proof-of-principle study achieved high sensitivity and specificity in identifying antigens for a given TCR and identifying TCRs recognizing a given pMHC for modest library sizes. Finally, the enrichment of high-affinity TCR-pMHC interactions by YAMTAD in library-on-library screening was demonstrated. Given the high throughput (106–108 × 106–108 in theory) and simplicity (identifying TCR-pMHC interactions without purification of TCR and pMHC) of YAMTAD, this study provides a rapid but effective platform for TCR-pMHC interaction screening, with valuable applications in future personalized immunotherapy.
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Affiliation(s)
- Lihui Wang
- Department of Basic Medical Science, School of Medicine, Tsinghua University, Beijing, China.,Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China.,MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| | - Xun Lan
- Department of Basic Medical Science, School of Medicine, Tsinghua University, Beijing, China. .,Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China. .,MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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25
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Dobson CS, Reich AN, Gaglione S, Smith BE, Kim EJ, Dong J, Ronsard L, Okonkwo V, Lingwood D, Dougan M, Dougan SK, Birnbaum ME. Antigen identification and high-throughput interaction mapping by reprogramming viral entry. Nat Methods 2022; 19:449-460. [PMID: 35396484 PMCID: PMC9012700 DOI: 10.1038/s41592-022-01436-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 03/01/2022] [Indexed: 01/11/2023]
Abstract
Deciphering immune recognition is critical for understanding a broad range of diseases and for the development of effective vaccines and immunotherapies. Efforts to do so are limited by a lack of technologies capable of simultaneously capturing the complexity of adaptive immunoreceptor repertoires and the landscape of potential antigens. To address this, we present receptor-antigen pairing by targeted retroviruses, which combines viral pseudotyping and molecular engineering approaches to enable one-pot library-on-library interaction screens by displaying antigens on the surface of lentiviruses and encoding their identity in the viral genome. Antigen-specific viral infection of cell lines expressing human T or B cell receptors allows readout of both antigen and receptor identities via single-cell sequencing. The resulting system is modular, scalable and compatible with any cell type. These techniques provide a suite of tools for targeted viral entry, molecular engineering and interaction screens with broad potential applications.
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Affiliation(s)
- Connor S Dobson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Anna N Reich
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Stephanie Gaglione
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Blake E Smith
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Program in Immunology, Harvard Medical School, Boston, MA, USA
| | - Ellen J Kim
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Jiayi Dong
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | | | - Vintus Okonkwo
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Michael Dougan
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephanie K Dougan
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michael E Birnbaum
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA.
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA.
- Singapore-MIT Alliance for Research and Technology Centre, Singapore, Singapore.
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26
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Cluet D, Vergier B, Levy NP, Dehau L, Thurman A, Amri I, Spichty M. Titration of apparent in-cellula affinities of protein-protein interactions. Chembiochem 2021; 23:e202100640. [PMID: 34932835 DOI: 10.1002/cbic.202100640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/21/2021] [Indexed: 11/07/2022]
Abstract
A genetic assay permits simultaneous quantification of two interacting proteins and their bound fraction at the single-cell level using flow cytometry. Apparent in-cellula affinities of protein-protein interactions can be extracted from the acquired data through a titration-like analysis. The applicability of this approach is demonstrated on a diverse set of interactions with proteins from different families and organisms and with in-vitro dissociation constants ranging from picomolar to micromolar.
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Affiliation(s)
- David Cluet
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Blandine Vergier
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Nicolas-Pierre Levy
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Lucie Dehau
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Alexandre Thurman
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Ikram Amri
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364, Lyon cedex 07, France
| | - Martin Spichty
- Laboratoire d'Innovation Moléculaire et Applications, Université de Strasbourg -, Centre National de la Recherche Scientifique, Université de Haute-Alsace, 3 bis rue Alfred Werner, 68057, Mulhouse Cedex, France
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27
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Fukunaga K, Yokobayashi Y. Directed evolution of orthogonal RNA-RBP pairs through library-vs-library in vitro selection. Nucleic Acids Res 2021; 50:601-616. [PMID: 34219162 PMCID: PMC8789040 DOI: 10.1093/nar/gkab527] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 12/30/2022] Open
Abstract
RNA-binding proteins (RBPs) and their RNA ligands play many critical roles in gene regulation and RNA processing in cells. They are also useful for various applications in cell biology and synthetic biology. However, re-engineering novel and orthogonal RNA-RBP pairs from natural components remains challenging while such synthetic RNA-RBP pairs could significantly expand the RNA-RBP toolbox for various applications. Here, we report a novel library-vs-library in vitro selection strategy based on Phage Display coupled with Systematic Evolution of Ligands by EXponential enrichment (PD-SELEX). Starting with pools of 1.1 × 1012 unique RNA sequences and 4.0 × 108 unique phage-displayed L7Ae-scaffold (LS) proteins, we selected RNA-RBP complexes through a two-step affinity purification process. After six rounds of library-vs-library selection, the selected RNAs and LS proteins were analyzed by next-generation sequencing (NGS). Further deconvolution of the enriched RNA and LS protein sequences revealed two synthetic and orthogonal RNA-RBP pairs that exhibit picomolar affinity and >4000-fold selectivity.
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Affiliation(s)
- Keisuke Fukunaga
- Nucleic Acid Chemistry and Engineering Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904 0495, Japan
| | - Yohei Yokobayashi
- Nucleic Acid Chemistry and Engineering Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904 0495, Japan
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28
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Arita Y, Kim G, Li Z, Friesen H, Turco G, Wang RY, Climie D, Usaj M, Hotz M, Stoops EH, Baryshnikova A, Boone C, Botstein D, Andrews BJ, McIsaac RS. A genome-scale yeast library with inducible expression of individual genes. Mol Syst Biol 2021; 17:e10207. [PMID: 34096681 PMCID: PMC8182650 DOI: 10.15252/msb.202110207] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/27/2021] [Accepted: 04/30/2021] [Indexed: 11/09/2022] Open
Abstract
The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.
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Affiliation(s)
- Yuko Arita
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
| | - Griffin Kim
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | - Zhijian Li
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Helena Friesen
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Gina Turco
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | | | - Dale Climie
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Matej Usaj
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Manuel Hotz
- Calico Life Sciences LLCSouth San FranciscoCAUSA
| | | | | | - Charles Boone
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | | | - Brenda J Andrews
- Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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29
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Pertseva M, Gao B, Neumeier D, Yermanos A, Reddy ST. Applications of Machine and Deep Learning in Adaptive Immunity. Annu Rev Chem Biomol Eng 2021; 12:39-62. [PMID: 33852352 DOI: 10.1146/annurev-chembioeng-101420-125021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.
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Affiliation(s)
- Margarita Pertseva
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8006 Zurich, Switzerland
| | - Beichen Gao
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Daniel Neumeier
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Department of Pathology and Immunology, University of Geneva, 1205 Geneva, Switzerland.,Department of Biology, Institute of Microbiology and Immunology, ETH Zurich, 8093 Zurich, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
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30
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Velásquez-Zapata V, Elmore JM, Banerjee S, Dorman KS, Wise RP. Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor. PLoS Comput Biol 2021; 17:e1008890. [PMID: 33798202 PMCID: PMC8046355 DOI: 10.1371/journal.pcbi.1008890] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 04/14/2021] [Accepted: 03/17/2021] [Indexed: 12/21/2022] Open
Abstract
Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
| | - J. Mitch Elmore
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, Iowa, United States of America
| | - Sagnik Banerjee
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
| | - Karin S. Dorman
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, United States of America
| | - Roger P. Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, Iowa, United States of America
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31
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Laustsen AH, Greiff V, Karatt-Vellatt A, Muyldermans S, Jenkins TP. Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery. Trends Biotechnol 2021; 39:1263-1273. [PMID: 33775449 DOI: 10.1016/j.tibtech.2021.03.003] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from non-animal-derived recombinant repertoires using display technologies. While it has been argued that using recombinant display libraries can reduce animal consumption, we hold that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies. Thus, improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires present unique advantages for discovering antibodies that are fit for purpose. Furthermore, we anticipate that machine learning will play a significant role within discovery workflows, refining current antibody discovery practices.
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Affiliation(s)
- Andreas H Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | | | - Serge Muyldermans
- Department of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Timothy P Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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32
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Bacon K, Blain A, Bowen J, Burroughs M, McArthur N, Menegatti S, Rao BM. Quantitative Yeast-Yeast Two Hybrid for the Discovery and Binding Affinity Estimation of Protein-Protein Interactions. ACS Synth Biol 2021; 10:505-514. [PMID: 33587591 DOI: 10.1021/acssynbio.0c00472] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Quantifying the binding affinity of protein-protein interactions is important for elucidating connections within biochemical signaling pathways, as well as characterization of binding proteins isolated from combinatorial libraries. We describe a quantitative yeast-yeast two-hybrid (qYY2H) system that not only enables the discovery of specific protein-protein interactions but also efficient, quantitative estimation of their binding affinities (KD). In qYY2H, the bait and prey proteins are expressed as yeast cell surface fusions using yeast surface display. We developed a semiempirical framework for estimating the KD of monovalent bait-prey interactions, using measurements of bait-prey yeast-yeast binding, which is mediated by multivalent interactions between yeast-displayed bait and prey. Using qYY2H, we identified interaction partners of SMAD3 and the tandem WW domains of YAP from a cDNA library and characterized their binding affinities. Finally, we showed that qYY2H could also quantitatively evaluate binding interactions mediated by post-translational modifications on the bait protein.
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Affiliation(s)
- Kaitlyn Bacon
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Abigail Blain
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - John Bowen
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Matthew Burroughs
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Nikki McArthur
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Golden LEAF Biomanufacturing Training and Education Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Balaji M. Rao
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Golden LEAF Biomanufacturing Training and Education Center, North Carolina State University, Raleigh, North Carolina 27695, United States
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33
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Vrana J, de Lange O, Yang Y, Newman G, Saleem A, Miller A, Cordray C, Halabiya S, Parks M, Lopez E, Goldberg S, Keller B, Strickland D, Klavins E. Aquarium: open-source laboratory software for design, execution and data management. Synth Biol (Oxf) 2021; 6:ysab006. [PMID: 34151028 PMCID: PMC8209617 DOI: 10.1093/synbio/ysab006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/12/2021] [Accepted: 01/22/2021] [Indexed: 11/23/2022] Open
Abstract
Automation has been shown to improve the replicability and scalability of biomedical and bioindustrial research. Although the work performed in many labs is repetitive and can be standardized, few academic labs can afford the time and money required to automate their workflows with robotics. We propose that human-in-the-loop automation can fill this critical gap. To this end, we present Aquarium, an open-source, web-based software application that integrates experimental design, inventory management, protocol execution and data capture. We provide a high-level view of how researchers can install Aquarium and use it in their own labs. We discuss the impacts of the Aquarium on working practices, use in biofoundries and opportunities it affords for collaboration and education in life science laboratory research and manufacture.
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Affiliation(s)
- Justin Vrana
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Orlando de Lange
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Yaoyu Yang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Garrett Newman
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Ayesha Saleem
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Abraham Miller
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Cameron Cordray
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Samer Halabiya
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Michelle Parks
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Eriberto Lopez
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Sarah Goldberg
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Benjamin Keller
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Devin Strickland
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Eric Klavins
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
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34
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Abstract
In yeast, many proteins are found in both the cytoplasmic and extracellular compartments, and consequently it can be difficult to distinguish nonconventional secretion from cellular leakage. Therefore, we monitored the extracellular glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activity of intact cells as a specific marker for nonconventional secretion. Extracellular GAPDH activity was proportional to the number of cells assayed, increased with incubation time, and was dependent on added substrates. Preincubation of intact cells with 100 μM dithiothreitol increased the reaction rate, consistent with increased access of the enzyme after reduction of cell wall disulfide cross-links. Such treatment did not increase cell permeability to propidium iodide, in contrast to effects of higher concentrations of reducing agents. An amine-specific membrane-impermeant biotinylation reagent specifically inactivated extracellular GAPDH. The enzyme was secreted again after a 30- to 60-min lag following the inactivation, and there was no concomitant increase in propidium iodide staining. There were about 4 × 104 active GAPDH molecules per cell at steady state, and secretion studies showed replenishment to that level 1 h after inactivation. These results establish conditions for specific quantitative assays of cell wall proteins in the absence of cytoplasmic leakage and for subsequent quantification of secretion rates in intact cells.IMPORTANCE Eukaryotic cells secrete many proteins, including many proteins that do not follow the classical secretion pathway. Among these, the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is unexpectedly found in the walls of yeasts and other fungi and in extracellular space in mammalian cell cultures. It is difficult to quantify extracellular GAPDH, because leakage of just a little of the very large amount of cytoplasmic enzyme can invalidate the determinations. We used enzymatic assays of intact cells while also maintaining membrane integrity. The results lead to estimates of the amount of extracellular enzyme and its rate of secretion to the wall in intact cells. Therefore, enzyme assays under controlled conditions can be used to investigate nonconventional secretion more generally.
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35
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Heermann T, Franquelim HG, Glock P, Harrington L, Schwille P. Probing Biomolecular Interactions by a Pattern-Forming Peptide-Conjugate Sensor. Bioconjug Chem 2020; 32:172-181. [PMID: 33314917 PMCID: PMC7872319 DOI: 10.1021/acs.bioconjchem.0c00596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
As a key mechanism
underpinning many biological processes, protein
self-organization has been extensively studied. However, the potential
to apply the distinctive, nonlinear biochemical properties of such
self-organizing systems to biotechnological problems such as the facile
detection and characterization of biomolecular interactions has not
yet been explored. Here, we describe an in vitro assay
in a 96-well plate format that harnesses the emergent behavior of
the Escherichia coli Min system to
provide a readout of biomolecular interactions. Crucial for the development
of our approach is a minimal MinE-derived peptide that stimulates
MinD ATPase activity only when dimerized. We found that this behavior
could be induced via any pair of foreign, mutually binding molecular
entities fused to the minimal MinE peptide. The resulting MinD ATPase
activity and the spatiotemporal nature of the produced protein patterns
quantitatively correlate with the affinity of the fused binding partners,
thereby enabling a highly sensitive assay for biomolecular interactions.
Our assay thus provides a unique means of quantitatively visualizing
biomolecular interactions and may prove useful for the assessment
of domain interactions within protein libraries and for the facile
investigation of potential inhibitors of protein–protein interactions.
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Affiliation(s)
- Tamara Heermann
- Department of Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany
| | - Henri G Franquelim
- Department of Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany
| | - Philipp Glock
- Department of Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany
| | - Leon Harrington
- Department of Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany
| | - Petra Schwille
- Department of Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Planegg, Germany
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36
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Oikonomou P, Salatino R, Tavazoie S. In vivo mRNA display enables large-scale proteomics by next generation sequencing. Proc Natl Acad Sci U S A 2020; 117:26710-26718. [PMID: 33037152 PMCID: PMC7604504 DOI: 10.1073/pnas.2002650117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.
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Affiliation(s)
- Panos Oikonomou
- Department of Biological Sciences, Columbia University, New York, NY 10027;
- Department of Systems Biology, Columbia University, New York, NY 10032
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY10032
| | - Roberto Salatino
- Department of Systems Biology, Columbia University, New York, NY 10032
| | - Saeed Tavazoie
- Department of Biological Sciences, Columbia University, New York, NY 10027;
- Department of Systems Biology, Columbia University, New York, NY 10032
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY10032
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37
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Hossain A, Lopez E, Halper SM, Cetnar DP, Reis AC, Strickland D, Klavins E, Salis HM. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat Biotechnol 2020; 38:1466-1475. [PMID: 32661437 DOI: 10.1038/s41587-020-0584-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 05/31/2020] [Indexed: 01/08/2023]
Abstract
Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.
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Affiliation(s)
- Ayaan Hossain
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA
| | - Eriberto Lopez
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Sean M Halper
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Daniel P Cetnar
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Alexander C Reis
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Devin Strickland
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Eric Klavins
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Howard M Salis
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA. .,Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA. .,Department of Biological Engineering, Pennsylvania State University, University Park, PA, USA. .,Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.
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38
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Cluet D, Amri I, Vergier B, Léault J, Audibert A, Grosjean C, Calabrési D, Spichty M. A Quantitative Tri-fluorescent Yeast Two-hybrid System: From Flow Cytometry to In cellula Affinities. Mol Cell Proteomics 2020; 19:701-715. [PMID: 32015065 PMCID: PMC7124468 DOI: 10.1074/mcp.tir119.001692] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 01/31/2020] [Indexed: 12/14/2022] Open
Abstract
We present a technological advancement for the estimation of the affinities of Protein-Protein Interactions (PPIs) in living cells. A novel set of vectors is introduced that enables a quantitative yeast two-hybrid system based on fluorescent fusion proteins. The vectors allow simultaneous quantification of the reaction partners (Bait and Prey) and the reporter at the single-cell level by flow cytometry. We validate the applicability of this system on a small but diverse set of PPIs (eleven protein families from six organisms) with different affinities; the dissociation constants range from 117 pm to 17 μm After only two hours of reaction, expression of the reporter can be detected even for the weakest PPI. Through a simple gating analysis, it is possible to select only cells with identical expression levels of the reaction partners. As a result of this standardization of expression levels, the mean reporter levels directly reflect the affinities of the studied PPIs. With a set of PPIs with known affinities, it is straightforward to construct an affinity ladder that permits rapid classification of PPIs with thus far unknown affinities. Conventional software can be used for this analysis. To permit automated analysis, we provide a graphical user interface for the Python-based FlowCytometryTools package.
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Affiliation(s)
- David Cluet
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Ikram Amri
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Blandine Vergier
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Jérémie Léault
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Astrid Audibert
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Clémence Grosjean
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Dylan Calabrési
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France
| | - Martin Spichty
- Laboratoire de Biologie et Modé lisation de la Cellule, Ecole Normale Supé rieure de Lyon, CNRS, Université Lyon 1, Université de Lyon, 46 allée d'Italie, 69364 Lyon cedex 07, France.
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39
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Zinkus-Boltz J, DeValk C, Dickinson BC. A Phage-Assisted Continuous Selection Approach for Deep Mutational Scanning of Protein-Protein Interactions. ACS Chem Biol 2019; 14:2757-2767. [PMID: 31808666 DOI: 10.1021/acschembio.9b00669] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs is often a key driver of disease. To better understand the biophysical basis of such disease processes-and to potentially target them-it is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage-assisted continuous selection (PACS) system using our previously reported proximity-dependent split RNA polymerase (RNAP) biosensors, with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed positions along the binding interface that are both tolerant and intolerant to mutations, as well as which substitutions are tolerated at each position. Critically, the "functional scores" obtained from enrichment data through continuous selection for individual variants correlated with KD values measured in vitro, indicating that biochemical data can be extrapolated from sequencing using our new system. Due to the plug and play nature of RNAP biosensors, this method can likely be extended to a variety of other PPIs. More broadly, this, and other methods under development support the continued development of evolutionary and high-throughput approaches to address biochemical problems, moving toward a more comprehensive understanding of sequence-function relationships in proteins.
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Affiliation(s)
- Julia Zinkus-Boltz
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Craig DeValk
- The Center for Physics of Evolving Systems, Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637, United States
| | - Bryan C. Dickinson
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
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40
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Liu X, Liu Z, Dziulko AK, Li F, Miller D, Morabito RD, Francois D, Levy SF. iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast. Cell Syst 2019; 8:338-344.e8. [PMID: 30954477 PMCID: PMC6483859 DOI: 10.1016/j.cels.2019.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/10/2019] [Accepted: 03/06/2019] [Indexed: 11/24/2022]
Abstract
We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.
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Affiliation(s)
- Xianan Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Zhimin Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Adam K Dziulko
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Robert D Morabito
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Danielle Francois
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA; Joint Initiative for Metrology in Biology, Stanford, CA 94305-4245, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA.
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41
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Brechun KE, Arndt KM, Woolley GA. Selection of Protein-Protein Interactions of Desired Affinities with a Bandpass Circuit. J Mol Biol 2019; 431:391-400. [PMID: 30448232 DOI: 10.1016/j.jmb.2018.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 11/07/2018] [Accepted: 11/08/2018] [Indexed: 11/17/2022]
Abstract
We have developed a genetic circuit in Escherichia coli that can be used to select for protein-protein interactions of different strengths by changing antibiotic concentrations in the media. The genetic circuit links protein-protein interaction strength to β-lactamase activity while simultaneously imposing tuneable positive and negative selection pressure for β-lactamase activity. Cells only survive if they express interacting proteins with affinities that fall within set high- and low-pass thresholds; i.e. the circuit therefore acts as a bandpass filter for protein-protein interactions. We show that the circuit can be used to recover protein-protein interactions of desired affinity from a mixed population with a range of affinities. The circuit can also be used to select for inhibitors of protein-protein interactions of defined strength.
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Affiliation(s)
- Katherine E Brechun
- Department of Chemistry, University of Toronto, Toronto, Canada; Molecular Biotechnology, University of Potsdam, Potsdam, Germany
| | - Katja M Arndt
- Molecular Biotechnology, University of Potsdam, Potsdam, Germany.
| | - G Andrew Woolley
- Department of Chemistry, University of Toronto, Toronto, Canada.
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Martín-Mora D, Fernández M, Velando F, Ortega Á, Gavira JA, Matilla MA, Krell T. Functional Annotation of Bacterial Signal Transduction Systems: Progress and Challenges. Int J Mol Sci 2018; 19:ijms19123755. [PMID: 30486299 PMCID: PMC6321045 DOI: 10.3390/ijms19123755] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/22/2018] [Accepted: 11/22/2018] [Indexed: 01/15/2023] Open
Abstract
Bacteria possess a large number of signal transduction systems that sense and respond to different environmental cues. Most frequently these are transcriptional regulators, two-component systems and chemosensory pathways. A major bottleneck in the field of signal transduction is the lack of information on signal molecules that modulate the activity of the large majority of these systems. We review here the progress made in the functional annotation of sensor proteins using high-throughput ligand screening approaches of purified sensor proteins or individual ligand binding domains. In these assays, the alteration in protein thermal stability following ligand binding is monitored using Differential Scanning Fluorimetry. We illustrate on several examples how the identification of the sensor protein ligand has facilitated the elucidation of the molecular mechanism of the regulatory process. We will also discuss the use of virtual ligand screening approaches to identify sensor protein ligands. Both approaches have been successfully applied to functionally annotate a significant number of bacterial sensor proteins but can also be used to study proteins from other kingdoms. The major challenge consists in the study of sensor proteins that do not recognize signal molecules directly, but that are activated by signal molecule-loaded binding proteins.
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Affiliation(s)
- David Martín-Mora
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Prof. Albareda 1, 18008 Granada, Spain.
| | - Matilde Fernández
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Prof. Albareda 1, 18008 Granada, Spain.
| | - Félix Velando
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Prof. Albareda 1, 18008 Granada, Spain.
| | - Álvaro Ortega
- Department of Biochemistry and Molecular Biology 'B' and Immunology, Faculty of Chemistry, University of Murcia, Campus of Espinardo, Regional Campus of International Excellence "Campus Mare Nostrum", 30100 Murcia, Spain.
| | - José A Gavira
- Laboratorio de Estudios Cristalográficos, IACT, (CSIC-UGR), Avenida las Palmeras 4, 18100 Armilla, Spain.
| | - Miguel A Matilla
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Prof. Albareda 1, 18008 Granada, Spain.
| | - Tino Krell
- Department of Environmental Protection, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Prof. Albareda 1, 18008 Granada, Spain.
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A Synthetic Bacterial Cell-Cell Adhesion Toolbox for Programming Multicellular Morphologies and Patterns. Cell 2018; 174:649-658.e16. [DOI: 10.1016/j.cell.2018.06.041] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 05/18/2018] [Accepted: 06/22/2018] [Indexed: 01/08/2023]
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Lipke PN. What We Do Not Know about Fungal Cell Adhesion Molecules. J Fungi (Basel) 2018; 4:jof4020059. [PMID: 29772751 PMCID: PMC6023273 DOI: 10.3390/jof4020059] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 04/27/2018] [Accepted: 05/10/2018] [Indexed: 12/16/2022] Open
Abstract
There has been extensive research on structure and function of fungal cell adhesion molecules, but the most of the work has been about adhesins in Candida albicans and Saccharomyces cerevisiae. These yeasts are members of a single ascomycete order, and adhesion molecules from the six other fungal phyla are only sparsely described in the literature. In these other phyla, most of the research is at the cellular level, rather than at the molecular level, so there has been little characterization of the adhesion molecules themselves. A catalog of known adhesins shows some common features: high Ser/Thr content, tandem repeats, N- and O-glycosylations, GPI anchors, dibasic sequence motifs, and potential amyloid-forming sequences. However, none of these features is universal. Known ligands include proteins and glycans on homologous cells and host cells. Existing and novel tools can exploit the availability of genome sequences to identify and characterize new fungal adhesins. These include bioinformatics tools and well-established yeast surface display models, which could be coupled with an adhesion substrate array. Thus, new knowledge could be exploited to answer key questions in fungal ecology, animal and plant pathogenesis, and roles of biofilms in infection and biomass turnover.
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Affiliation(s)
- Peter N Lipke
- Biology Department, Brooklyn College, City University of New York, Brooklyn, NY 11210, USA.
- The Graduate Center, City University of New York, New York, NY 10016, USA.
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