1
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McDonnell L, Evans S, Lu Z, Suchoronczak M, Leighton J, Ordeniza E, Ritchie B, Valado N, Walsh N, Antoney J, Wang C, Luna-Flores CH, Scott C, Speight R, Vickers CE, Peng B. Cyanamide-inducible expression of homing nuclease I- SceI for selectable marker removal and promoter characterisation in Saccharomyces cerevisiae. Synth Syst Biotechnol 2024; 9:820-827. [PMID: 39072146 PMCID: PMC11277796 DOI: 10.1016/j.synbio.2024.06.009] [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: 04/07/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
In synthetic biology, microbial chassis including yeast Saccharomyces cerevisiae are iteratively engineered with increasing complexity and scale. Wet-lab genetic engineering tools are developed and optimised to facilitate strain construction but are often incompatible with each other due to shared regulatory elements, such as the galactose-inducible (GAL) promoter in S. cerevisiae. Here, we prototyped the cyanamide-induced I- SceI expression, which triggered double-strand DNA breaks (DSBs) for selectable marker removal. We further combined cyanamide-induced I- SceI-mediated DSB and maltose-induced MazF-mediated negative selection for plasmid-free in situ promoter substitution, which simplified the molecular cloning procedure for promoter characterisation. We then characterised three tetracycline-inducible promoters showing differential strength, a non-leaky β-estradiol-inducible promoter, cyanamide-inducible DDI2 promoter, bidirectional MAL32/MAL31 promoters, and five pairs of bidirectional GAL1/GAL10 promoters. Overall, alternative regulatory controls for genome engineering tools can be developed to facilitate genomic engineering for synthetic biology and metabolic engineering applications.
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Affiliation(s)
- Liam McDonnell
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
| | - Samuel Evans
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
| | - Zeyu Lu
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Mitch Suchoronczak
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Jonah Leighton
- School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Eugene Ordeniza
- School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Blake Ritchie
- School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Nik Valado
- School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Niamh Walsh
- School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - James Antoney
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
| | - Chengqiang Wang
- College of Life Sciences, Shandong Agricultural University, Taian, Shandong Province, 271018, People's Republic of China
| | - Carlos Horacio Luna-Flores
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Colin Scott
- CSIRO Environment, Black Mountain Science and Innovation Park, Canberra, ACT, 2601, Australia
| | - Robert Speight
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
- Advanced Engineering Biology Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain, ACT, 2601, Australia
| | - Claudia E. Vickers
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
| | - Bingyin Peng
- Centre of Agriculture and the Bioeconomy, School of Biology and Environmental Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- ARC Centre of Excellence in Synthetic Biology, Australia
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, 4072, Australia
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2
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Hilvert D. Spiers Memorial Lecture: Engineering biocatalysts. Faraday Discuss 2024; 252:9-28. [PMID: 39046423 PMCID: PMC11389855 DOI: 10.1039/d4fd00139g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024]
Abstract
Enzymes are being engineered to catalyze chemical reactions for many practical applications in chemistry and biotechnology. The approaches used are surveyed in this short review, emphasizing methods for accessing reactivities not expressed by native protein scaffolds. The successful generation of completely de novo enzymes that rival the rates and selectivities of their natural counterparts highlights the potential role that designer enzymes may play in the coming years in research, industry, and medicine. Some challenges that need to be addressed to realize this ambitious dream are considered together with possible solutions.
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Affiliation(s)
- Donald Hilvert
- Laboratory of Organic Chemistry, ETH Zürich, 8093 Zürich, Switzerland.
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3
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Thornton EL, Paterson SM, Stam MJ, Wood CW, Laohakunakorn N, Regan L. Applications of cell free protein synthesis in protein design. Protein Sci 2024; 33:e5148. [PMID: 39180484 PMCID: PMC11344276 DOI: 10.1002/pro.5148] [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: 05/30/2024] [Revised: 07/26/2024] [Accepted: 08/02/2024] [Indexed: 08/26/2024]
Abstract
In protein design, the ultimate test of success is that the designs function as desired. Here, we discuss the utility of cell free protein synthesis (CFPS) as a rapid, convenient and versatile method to screen for activity. We champion the use of CFPS in screening potential designs. Compared to in vivo protein screening, a wider range of different activities can be evaluated using CFPS, and the scale on which it can easily be used-screening tens to hundreds of designed proteins-is ideally suited to current needs. Protein design using physics-based strategies tended to have a relatively low success rate, compared with current machine-learning based methods. Screening steps (such as yeast display) were often used to identify proteins that displayed the desired activity from many designs that were highly ranked computationally. We also describe how CFPS is well-suited to identify the reasons designs fail, which may include problems with transcription, translation, and solubility, in addition to not achieving the desired structure and function.
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Affiliation(s)
- Ella Lucille Thornton
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
| | - Sarah Maria Paterson
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
| | - Michael J. Stam
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
| | - Christopher W. Wood
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
| | - Nadanai Laohakunakorn
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
| | - Lynne Regan
- Centre for Engineering Biology, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological SciencesUniversity of EdinburghEdinburghUK
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4
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024; 42:1081-1096. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [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: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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5
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Bloomfield D, Pannu J, Zhu AW, Ng MY, Lewis A, Bendavid E, Asch SM, Hernandez-Boussard T, Cicero A, Inglesby T. AI and biosecurity: The need for governance. Science 2024; 385:831-833. [PMID: 39172825 DOI: 10.1126/science.adq1977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Governments should evaluate advanced models and if needed impose safety measures.
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Affiliation(s)
- Doni Bloomfield
- Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- School of Law, Fordham University, New York, NY, USA
| | - Jaspreet Pannu
- Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Alex W Zhu
- Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Madelena Y Ng
- Department of Medicine (Biomedical Informatics), Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Ashley Lewis
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Eran Bendavid
- Department of Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Steven M Asch
- Department of Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Tina Hernandez-Boussard
- Department of Medicine (Biomedical Informatics), Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Anita Cicero
- Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Tom Inglesby
- Center for Health Security, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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6
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Zhou J, Huang M. Navigating the landscape of enzyme design: from molecular simulations to machine learning. Chem Soc Rev 2024; 53:8202-8239. [PMID: 38990263 DOI: 10.1039/d4cs00196f] [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: 07/12/2024]
Abstract
Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.
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Affiliation(s)
- Jiahui Zhou
- School of Chemistry and Chemical Engineering, Queen's University, David Keir Building, Stranmillis Road, Belfast BT9 5AG, Northern Ireland, UK.
| | - Meilan Huang
- School of Chemistry and Chemical Engineering, Queen's University, David Keir Building, Stranmillis Road, Belfast BT9 5AG, Northern Ireland, UK.
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7
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Omidvar M, Zhang H, Ihalage AA, Saunders TG, Giddens H, Forrester M, Haq S, Hao Y. Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization. Nat Commun 2024; 15:6554. [PMID: 39095463 PMCID: PMC11297172 DOI: 10.1038/s41467-024-50884-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Accelerating perovskite solid solution discovery and sustainable synthesis is crucial for addressing challenges in wireless communication and biosensors. However, the vast array of chemical compositions and their dependence on factors such as crystal structure, and sintering temperature require time-consuming manual processes. To overcome these constraints, we introduce an automated materials discovery approach encompassing machine learning (ML) assisted material screening, robotic synthesis, and high-throughput characterization. Our proposed platform for rapid sintering and dielectric analysis streamlines the characterization of perovskites and the discovery of disordered materials. The setup has been successfully validated, demonstrating processing materials within minutes, in stark contrast to conventional procedures that can take hours or days. Following setup validation with established samples, we showcase synthesizing single-phase solid solutions within the barium family, such as (BaxSr1-x)CeO3, identified through ML-guided chemistry.
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Affiliation(s)
- Mojan Omidvar
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Hangfeng Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Achintha Avin Ihalage
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Theo Graves Saunders
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Henry Giddens
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | | | - Sajad Haq
- QinetiQ, Cody Technology Park, Farnborough, Hampshire, UK
| | - Yang Hao
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
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8
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Freschlin CR, Fahlberg SA, Heinzelman P, Romero PA. Neural network extrapolation to distant regions of the protein fitness landscape. Nat Commun 2024; 15:6405. [PMID: 39080282 PMCID: PMC11289474 DOI: 10.1038/s41467-024-50712-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks' capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models' extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture's inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust.
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Affiliation(s)
- Chase R Freschlin
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Sarah A Fahlberg
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Pete Heinzelman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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9
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Vornholt T, Mutný M, Schmidt GW, Schellhaas C, Tachibana R, Panke S, Ward TR, Krause A, Jeschek M. Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning. ACS CENTRAL SCIENCE 2024; 10:1357-1370. [PMID: 39071060 PMCID: PMC11273458 DOI: 10.1021/acscentsci.4c00258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 07/30/2024]
Abstract
Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.
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Affiliation(s)
- Tobias Vornholt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Mojmír Mutný
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Gregor W. Schmidt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Christian Schellhaas
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Ryo Tachibana
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Sven Panke
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Thomas R. Ward
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Andreas Krause
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Markus Jeschek
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- Institute
of Microbiology, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany
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10
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Malard F, Dias K, Baudy M, Thore S, Vialet B, Barthélémy P, Fribourg S, Karginov FV, Campagne S. Molecular Basis for the Calcium-Dependent Activation of the Ribonuclease EndoU. RESEARCH SQUARE 2024:rs.3.rs-4654759. [PMID: 39070628 PMCID: PMC11275989 DOI: 10.21203/rs.3.rs-4654759/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Ribonucleases (RNases) are ubiquitous enzymes that process or degrade RNA, essential for cellular functions and immune responses. The EndoU-like superfamily includes endoribonucleases conserved across bacteria, eukaryotes, and certain viruses, with an ancient evolutionary link to the ribonuclease A-like superfamily. Both bacterial EndoU and animal RNase A share a similar fold and function independently of cofactors. In contrast, the eukaryotic EndoU catalytic domain requires divalent metal ions for catalysis, possibly due to an N-terminal extension near the catalytic core. In this study, we used biophysical and computational techniques along with in vitro assays to investigate the calcium-dependent activation of human EndoU. We determined the crystal structure of EndoU bound to calcium and found that calcium binding remote from the catalytic triad triggers water-mediated intramolecular signaling and structural changes, activating the enzyme through allostery. Calcium-binding involves residues from both the catalytic core and the N-terminal extension, indicating that the N-terminal extension interacts with the catalytic core to modulate activity in response to calcium. Our findings suggest that similar mechanisms may be present across all eukaryotic EndoUs, highlighting a unique evolutionary adaptation that connects endoribonuclease activity to cellular signaling in eukaryotes.
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Affiliation(s)
- Florian Malard
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
| | - Kristen Dias
- Department of Molecular, Cell and Systems Biology, Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA, 92521, USA
| | - Margaux Baudy
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
| | - Stéphane Thore
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Brune Vialet
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Philippe Barthélémy
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Sébastien Fribourg
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Fedor V Karginov
- Department of Molecular, Cell and Systems Biology, Institute for Integrative Genome Biology, University of California at Riverside, Riverside, CA, 92521, USA
| | - Sébastien Campagne
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
- Univ. Bordeaux, CNRS, INSERM, IECB, US1, UAR 3033, F-33600 Pessac, France
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11
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Chaves EJF, Coêlho DF, Cruz CHB, Moreira EG, Simões JCM, Nascimento-Filho MJ, Lins RD. Structure-based computational design of antibody mimetics: challenges and perspectives. FEBS Open Bio 2024. [PMID: 38925955 DOI: 10.1002/2211-5463.13855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.
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Affiliation(s)
- Elton J F Chaves
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
| | - Danilo F Coêlho
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Carlos H B Cruz
- Institute of Structural and Molecular Biology, University College London, UK
| | | | - Júlio C M Simões
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Manassés J Nascimento-Filho
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Roberto D Lins
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
- Fiocruz Genomics Network, Brazil
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12
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Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun 2024; 15:3447. [PMID: 38658554 PMCID: PMC11043082 DOI: 10.1038/s41467-024-46574-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
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Affiliation(s)
- Enrico Orsi
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam-Golm, Germany.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, 10117, Berlin, Germany.
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13
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Adam D. The automated lab of tomorrow. Proc Natl Acad Sci U S A 2024; 121:e2406320121. [PMID: 38630717 PMCID: PMC11046582 DOI: 10.1073/pnas.2406320121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
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14
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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15
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Callaway E. 'Set it and forget it': automated lab uses AI and robotics to improve proteins. Nature 2024; 625:436. [PMID: 38212617 DOI: 10.1038/d41586-024-00093-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
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