1
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Zhang Z, Shen W, Liu Q, Zitnik M. Efficient Generation of Protein Pockets with PocketGen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581968. [PMID: 38464121 PMCID: PMC10925136 DOI: 10.1101/2024.02.25.581968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing protein-binding proteins plays an important role in drug discovery. However, AI-based design of such proteins is challenging due to complex ligand-protein interactions, flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that produces both the residue sequence and atom structure of the protein regions where interactions with ligand molecules occur. PocketGen ensures sequence-structure consistency by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple granularities across atom, residue, and ligand levels. To enhance sequence refinement, PocketGen integrates a structural adapter with the protein language model, ensuring consistency between structure-based and sequence-based predictions. Results show that PocketGen can generate high-fidelity protein pockets with superior binding affinity and structural validity. It is ten times faster than physics-based methods and achieves a 95% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets, along with achieving an amino acid recovery rate exceeding 64%.
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Affiliation(s)
- Zaixi Zhang
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wanxiang Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Qi Liu
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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2
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An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, Juergens D, Dauparas J, Anishchenko I, Coventry B, Bera AK, Kang A, Levine PM, Alvarez V, Pillai A, Norn C, Feldman D, Zorine D, Hicks DR, Li X, Sanchez MG, Vafeados DK, Salveson PJ, Vorobieva AA, Baker D. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 2024; 385:276-282. [PMID: 39024436 DOI: 10.1126/science.adn3780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/03/2024] [Indexed: 07/20/2024]
Abstract
We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.
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Affiliation(s)
- Linna An
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Meerit Said
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Long Tran
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M Levine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Valentina Alvarez
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Dmitri Zorine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R Hicks
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Dionne K Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrick J Salveson
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Anastassia A Vorobieva
- VIB-VUB Center for Structural Biology, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
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3
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Zhou J, Huang M. Navigating the landscape of enzyme design: from molecular simulations to machine learning. Chem Soc Rev 2024. [PMID: 38990263 DOI: 10.1039/d4cs00196f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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4
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble and functional membrane protein analogues. Nature 2024; 631:449-458. [PMID: 38898281 PMCID: PMC11236705 DOI: 10.1038/s41586-024-07601-y] [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: 05/09/2023] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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Affiliation(s)
- Casper A Goverde
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Goldbach
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lars J Dornfeld
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra E M Balbi
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Srajan Kapoor
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Jagrity Choudhury
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christian Schellhaas
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Simon Kozlov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex J Vecchio
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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5
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Ferreira SS, Antunes MS. Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants. THE NEW PHYTOLOGIST 2024; 243:674-687. [PMID: 38752334 DOI: 10.1111/nph.19823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/30/2024] [Indexed: 06/21/2024]
Abstract
Synthetic biology has the potential to revolutionize biotechnology, public health, and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. We used bacterial allosteric transcription factors (aTFs) that control gene expression in a ligand-specific manner and tested their ability to repress semi-synthetic promoters in plants. We also tested the modulation of their repression activity in response to specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. Three aTFs, CouR, FapR, and TtgR, achieved c. 95% repression of their respective target promoters. For TtgR, a sixfold de-repression could be triggered by inducing its ligand accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY, and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a predefined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.
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Affiliation(s)
- Savio S Ferreira
- Department of Biological Sciences, University of North Texas, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA
| | - Mauricio S Antunes
- Department of Biological Sciences, University of North Texas, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA
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6
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Popa-Ion DA, Boldeanu L, Gheonea DI, Denicu MM, Boldeanu MV, Chiuțu LC. Anesthesia Medication's Impacts on Inflammatory and Neuroendocrine Immune Response in Patients Undergoing Digestive Endoscopy. Clin Pract 2024; 14:1171-1184. [PMID: 38921271 PMCID: PMC11203055 DOI: 10.3390/clinpract14030093] [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: 03/23/2024] [Revised: 05/25/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
The aim of this study was to explore the impact of anesthetic drugs currently used to perform lower digestive endoscopy on serum concentrations of inflammation markers and catecholamines. We selected 120 patients and divided them into three lots of 40 patients each: L1, in which no anesthetics were used; L2, in which propofol was used; and L3, in which propofol combined with fentanyl was used. All patients had serum concentrations of adrenaline/epinephrine (EPI), noradrenaline/norepinephrine (NE), tumor necrosis factor alpha (TNF-α), interleukin-4 (IL-4), IL-6, IL-8, and IL-10, taken at three time points: at the beginning of the endoscopic procedure (T0), 15 min after (T1), and 2 h after the end of the endoscopic procedure (T2). The results of the research showed changes in the levels of catecholamines and interleukins (ILs) at T0, with an increased response in L1 above the mean recorded in L2 and L3 (p < 0.001). At T1, increased values were recorded in all lots; values were significantly higher in L1. At T2, the values recorded in L3 were significantly lower than the values in L2 (student T, p < 0.001) and L1, in which the level of these markers continued to increase, reaching double values compared to T0 (student T, p < 0.001). In L2 at T1, the dose of propofol correlated much better with NE, EPI, and well-known cytokines. Our results show that propofol combined with fentanyl can significantly inhibit the activation of systemic immune and neuroendocrine response during painless lower digestive endoscopy.
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Affiliation(s)
- Denisa-Ancuța Popa-Ion
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (D.-A.P.-I.); (M.M.D.); (L.C.C.)
| | - Lidia Boldeanu
- Department of Microbiology, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Dan-Ionuț Gheonea
- Department of Gastroenterology, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Madalina Maria Denicu
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (D.-A.P.-I.); (M.M.D.); (L.C.C.)
| | - Mihail Virgil Boldeanu
- Department of Immunology, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Luminița Cristina Chiuțu
- Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (D.-A.P.-I.); (M.M.D.); (L.C.C.)
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7
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Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, Lee GR, Morey-Burrows FS, Anishchenko I, Humphreys IR, McHugh R, Vafeados D, Li X, Sutherland GA, Hitchcock A, Hunter CN, Kang A, Brackenbrough E, Bera AK, Baek M, DiMaio F, Baker D. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 2024; 384:eadl2528. [PMID: 38452047 DOI: 10.1126/science.adl2528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024]
Abstract
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
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Affiliation(s)
- Rohith Krishna
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Jue Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Pascal Sturmfels
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Indrek Kalvet
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | | | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ryan McHugh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | | | - Andrew Hitchcock
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - C Neil Hunter
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Kang
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Evans Brackenbrough
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Minkyung Baek
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
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8
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble functional analogues of integral membrane proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.09.540044. [PMID: 38496615 PMCID: PMC10942269 DOI: 10.1101/2023.05.09.540044] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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9
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Boo A, Toth T, Yu Q, Pfotenhauer A, Fields BD, Lenaghan SC, Stewart CN, Voigt CA. Synthetic microbe-to-plant communication channels. Nat Commun 2024; 15:1817. [PMID: 38418817 PMCID: PMC10901793 DOI: 10.1038/s41467-024-45897-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 02/07/2024] [Indexed: 03/02/2024] Open
Abstract
Plants and microbes communicate to collaborate to stop pests, scavenge nutrients, and react to environmental change. Microbiota consisting of thousands of species interact with each other and plants using a large chemical language that is interpreted by complex regulatory networks. In this work, we develop modular interkingdom communication channels, enabling bacteria to convey environmental stimuli to plants. We introduce a "sender device" in Pseudomonas putida and Klebsiella pneumoniae, that produces the small molecule p-coumaroyl-homoserine lactone (pC-HSL) when the output of a sensor or circuit turns on. This molecule triggers a "receiver device" in the plant to activate gene expression. We validate this system in Arabidopsis thaliana and Solanum tuberosum (potato) grown hydroponically and in soil, demonstrating its modularity by swapping bacteria that process different stimuli, including IPTG, aTc and arsenic. Programmable communication channels between bacteria and plants will enable microbial sentinels to transmit information to crops and provide the building blocks for designing artificial consortia.
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Affiliation(s)
- Alice Boo
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Tyler Toth
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Qiguo Yu
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alexander Pfotenhauer
- Center for Agricultural Synthetic Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Brandon D Fields
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Scott C Lenaghan
- Center for Agricultural Synthetic Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - C Neal Stewart
- Center for Agricultural Synthetic Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Christopher A Voigt
- Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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10
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Satalkar V, Degaga GD, Li W, Pang YT, McShan AC, Gumbart JC, Mitchell JC, Torres MP. Generative β-hairpin design using a residue-based physicochemical property landscape. Biophys J 2024:S0006-3495(24)00070-5. [PMID: 38297834 DOI: 10.1016/j.bpj.2024.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/20/2023] [Accepted: 01/25/2024] [Indexed: 02/02/2024] Open
Abstract
De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.
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Affiliation(s)
- Vardhan Satalkar
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Gemechis D Degaga
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Wei Li
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Yui Tik Pang
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - James C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
| | - Matthew P Torres
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia.
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11
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An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, Juergens D, Dauparas J, Anishchenko I, Coventry B, Bera AK, Kang A, Levine PM, Alvarez V, Pillai A, Norn C, Feldman D, Zorine D, Hicks DR, Li X, Sanchez MG, Vafeados DK, Salveson PJ, Vorobieva AA, Baker D. De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572602. [PMID: 38187589 PMCID: PMC10769206 DOI: 10.1101/2023.12.20.572602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.
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Affiliation(s)
- Linna An
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Meerit Said
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Long Tran
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Asim K. Bera
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M. Levine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Valentina Alvarez
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind Pillai
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Feldman
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
| | - Dmitri Zorine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R. Hicks
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Dionne K. Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrick J. Salveson
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Baker
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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12
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [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: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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13
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Lee GR, Pellock SJ, Norn C, Tischer D, Dauparas J, Anischenko I, Mercer JAM, Kang A, Bera A, Nguyen H, Goreshnik I, Vafeados D, Roullier N, Han HL, Coventry B, Haddox HK, Liu DR, Yeh AHW, Baker D. Small-molecule binding and sensing with a designed protein family. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565201. [PMID: 37961294 PMCID: PMC10635051 DOI: 10.1101/2023.11.01.565201] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
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14
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Kosugi T, Iida T, Tanabe M, Iino R, Koga N. Design of allosteric sites into rotary motor V 1-ATPase by restoring lost function of pseudo-active sites. Nat Chem 2023; 15:1591-1598. [PMID: 37414880 PMCID: PMC10624635 DOI: 10.1038/s41557-023-01256-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/26/2023] [Indexed: 07/08/2023]
Abstract
Allostery produces concerted functions of protein complexes by orchestrating the cooperative work between the constituent subunits. Here we describe an approach to create artificial allosteric sites in protein complexes. Certain protein complexes contain subunits with pseudo-active sites, which are believed to have lost functions during evolution. Our hypothesis is that allosteric sites in such protein complexes can be created by restoring the lost functions of pseudo-active sites. We used computational design to restore the lost ATP-binding ability of the pseudo-active site in the B subunit of a rotary molecular motor, V1-ATPase. Single-molecule experiments with X-ray crystallography analyses revealed that binding of ATP to the designed allosteric site boosts this V1's activity compared with the wild-type, and the rotation rate can be tuned by modulating ATP's binding affinity. Pseudo-active sites are widespread in nature, and our approach shows promise as a means of programming allosteric control over concerted functions of protein complexes.
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Affiliation(s)
- Takahiro Kosugi
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan.
| | - Tatsuya Iida
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Mikio Tanabe
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Japan
| | - Ryota Iino
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Nobuyasu Koga
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- Institute for Protein Research (IPR), Osaka University, Suita, Japan.
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15
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Wang Q, Hu Z, Li Z, Liu T, Bian G. Exploring the Application and Prospects of Synthetic Biology in Engineered Living Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2305828. [PMID: 37677048 DOI: 10.1002/adma.202305828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/05/2023] [Indexed: 09/09/2023]
Abstract
At the intersection of synthetic biology and materials science, engineered living materials (ELMs) exhibit unprecedented potential. Possessing unique "living" attributes, ELMs represent a significant paradigm shift in material design, showcasing self-organization, self-repair, adaptability, and evolvability, surpassing conventional synthetic materials. This review focuses on reviewing the applications of ELMs derived from bacteria, fungi, and plants in environmental remediation, eco-friendly architecture, and sustainable energy. The review provides a comprehensive overview of the latest research progress and emerging design strategies for ELMs in various application fields from the perspectives of synthetic biology and materials science. In addition, the review provides valuable references for the design of novel ELMs, extending the potential applications of future ELMs. The investigation into the synergistic application possibilities amongst different species of ELMs offers beneficial reference information for researchers and practitioners in this field. Finally, future trends and development challenges of synthetic biology for ELMs in the coming years are discussed in detail.
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Affiliation(s)
- Qiwen Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
- Center of Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhehui Hu
- Center of Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, 430071, China
| | - Zhixuan Li
- Center of Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Tiangang Liu
- Department of Urology, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
| | - Guangkai Bian
- Center of Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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16
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Wang Y, Demirer GS. Synthetic biology for plant genetic engineering and molecular farming. Trends Biotechnol 2023; 41:1182-1198. [PMID: 37012119 DOI: 10.1016/j.tibtech.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/03/2023] [Accepted: 03/09/2023] [Indexed: 04/03/2023]
Abstract
Many efforts have been put into engineering plants to improve crop yields and stress tolerance and boost the bioproduction of valuable molecules. Yet, our capabilities are still limited due to the lack of well-characterized genetic building blocks and resources for precise manipulation and given the inherently challenging properties of plant tissues. Advancements in plant synthetic biology can overcome these bottlenecks and release the full potential of engineered plants. In this review, we first discuss the recently developed plant synthetic elements from single parts to advanced circuits, software, and hardware tools expediting the engineering cycle. Next, we survey the advancements in plant biotechnology enabled by these recent resources. We conclude the review with outstanding challenges and future directions of plant synthetic biology.
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Affiliation(s)
- Yunqing Wang
- Department of Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Gozde S Demirer
- Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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17
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Di Rienzo L, Miotto M, Milanetti E, Ruocco G. Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors. Comput Struct Biotechnol J 2023; 21:3002-3009. [PMID: 37249971 PMCID: PMC10220229 DOI: 10.1016/j.csbj.2023.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/17/2023] [Accepted: 05/04/2023] [Indexed: 05/31/2023] Open
Abstract
Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.
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Affiliation(s)
- Lorenzo Di Rienzo
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Mattia Miotto
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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18
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Zhong V, Archibald BN, Brophy JAN. Transcriptional and post-transcriptional controls for tuning gene expression in plants. CURRENT OPINION IN PLANT BIOLOGY 2023; 71:102315. [PMID: 36462457 DOI: 10.1016/j.pbi.2022.102315] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
Plant biotechnologists seek to modify plants through genetic reprogramming, but our ability to precisely control gene expression in plants is still limited. Here, we review transcription and translation in the model plants Arabidopsis thaliana and Nicotiana benthamiana with an eye toward control points that may be used to predictably modify gene expression. We highlight differences in gene expression requirements between these plants and other species, and discuss the ways in which our understanding of gene expression has been used to engineer plants. This review is intended to serve as a resource for plant scientists looking to achieve precise control over gene expression.
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Affiliation(s)
- Vivian Zhong
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Bella N Archibald
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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19
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Chemically inducible split protein regulators for mammalian cells. Nat Chem Biol 2023; 19:64-71. [PMID: 36163385 DOI: 10.1038/s41589-022-01136-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/08/2022] [Indexed: 12/31/2022]
Abstract
Chemically inducible systems represent valuable synthetic biology tools that enable the external control of biological processes. However, their translation to therapeutic applications has been limited because of unfavorable ligand characteristics or the immunogenicity of xenogeneic protein domains. To address these issues, we present a strategy for engineering inducible split protein regulators (INSPIRE) in which ligand-binding proteins of human origin are split into two fragments that reassemble in the presence of a cognate physiological ligand or clinically approved drug. We show that the INSPIRE platform can be used for dynamic, orthogonal and multiplex control of gene expression in mammalian cells. Furthermore, we demonstrate the functionality of a glucocorticoid-responsive INSPIRE platform in vivo and apply it for perturbing an endogenous regulatory network. INSPIRE presents a generalizable approach toward designing small-molecule responsive systems that can be implemented for the construction of new sensors, regulatory networks and therapeutic applications.
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20
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Popa-Ion DA, Chiuțu LC, Denicu MM, Gheonea DI. The Role of Analgesia in the Identification and Treatment of Digestive Tract Lesions: A Randomized, Prospective, Double-Blind Study. CURRENT HEALTH SCIENCES JOURNAL 2023; 49:19-27. [PMID: 37780189 PMCID: PMC10541072 DOI: 10.12865/chsj.49.01.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 10/03/2023]
Abstract
The association of sedation with analgesia in endoscopic procedures represents the ideal combination of anesthetic drugs, which allows these exploratory procedures to be carried out safely, in an outpatient setting. The aim of this study is to compare the results of the use of simple Propofol or Propofol associated with Fentanyl in order to ensure optimal sedation necessary for the detection of benign or malignant lesions of the digestive tract. In this study, 80 patients aged between 18 and 80 years were included, 40 in Group 1 who were administered Propofol alone and 40 in Group 2 in which Propofol was administered associated with Fentanyl. The onset of anesthetic sleep was 19.3±5.1 seconds in Lot 2 versus 29.6±9.1 seconds in Lot 1. The average dose of Propofol used was 203.6±82.8 mg in Lot 1 and in Lot 2 it was lower, 166.3±8.3mg. Cardio respiratory changes were more frequent in Lot 2. The wake-up time was 3.2±1.2 minutes in Lot 1 as a result of the administration of Propofol alone and 7±1.4 minutes in Lot 2. The discharge time was equal for patients in both groups. The degree of postanesthesia safisfaction was 10 for all patients from Lot 2, due to the analgesia provided by the administration of Fentanyl. The use of Propofol associated with Fentanyl in gastrointestinal endoscopic procedures is associated with a rapid recovery of cognitive function at the time of discharge and minimal adverse events, ensuring optimal conditions of analgesia and stability of vital functions.
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Affiliation(s)
- Denisa-Ancuța Popa-Ion
- Resident physician, PhD student, University of Medicine and Pharmacy of Craiova, Romania
| | - Luminița Cristina Chiuțu
- Department of Anesthesia and Intensive Care, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Romania
| | | | - Dan-Ionuț Gheonea
- Department of Gastroenterology, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Romania
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21
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Chemla Y, Dorfan Y, Yannai A, Meng D, Cao P, Glaven S, Gordon DB, Elbaz J, Voigt CA. Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions. PLoS One 2022; 17:e0278471. [PMID: 36516154 PMCID: PMC9750038 DOI: 10.1371/journal.pone.0278471] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/15/2022] [Indexed: 12/15/2022] Open
Abstract
Engineered bacteria could perform many functions in the environment, for example, to remediate pollutants, deliver nutrients to crops or act as in-field biosensors. Model organisms can be unreliable in the field, but selecting an isolate from the thousands that naturally live there and genetically manipulating them to carry the desired function is a slow and uninformed process. Here, we demonstrate the parallel engineering of isolates from environmental samples by using the broad-host-range XPORT conjugation system (Bacillus subtilis mini-ICEBs1) to transfer a genetic payload to many isolates in parallel. Bacillus and Lysinibacillus species were obtained from seven soil and water samples from different locations in Israel. XPORT successfully transferred a genetic function (reporter expression) into 25 of these isolates. They were then screened to identify the best-performing chassis based on the expression level, doubling time, functional stability in soil, and environmentally-relevant traits of its closest annotated reference species, such as the ability to sporulate and temperature tolerance. From this library, we selected Bacillus frigoritolerans A3E1, re-introduced it to soil, and measured function and genetic stability in a contained environment that replicates jungle conditions. After 21 months of storage, the engineered bacteria were viable, could perform their function, and did not accumulate disruptive mutations.
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Affiliation(s)
- Yonatan Chemla
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Yuval Dorfan
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Adi Yannai
- School of Molecular Cell Biology & Biotechnology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Dechuan Meng
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Paul Cao
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Sarah Glaven
- Center for Bio/Molecular Science and Engineering, Naval Research Laboratory, Washington, DC, United States of America
| | - D. Benjamin Gordon
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Johann Elbaz
- School of Molecular Cell Biology & Biotechnology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Christopher A. Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- * E-mail:
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22
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Leonard AC, Whitehead TA. Design and engineering of genetically encoded protein biosensors for small molecules. Curr Opin Biotechnol 2022; 78:102787. [PMID: 36058141 DOI: 10.1016/j.copbio.2022.102787] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 12/14/2022]
Abstract
Genetically encoded protein biosensors controlled by small organic molecules are valuable tools for many biotechnology applications, including control of cellular decisions in living cells. Here, we review recent advances in protein biosensor design and engineering for binding to novel ligands. We categorize sensor architecture as either integrated or portable, where portable biosensors uncouple molecular recognition from signal transduction. Proposed advances to improve portable biosensor development include standardizing a limited set of protein scaffolds, and automating ligand-compatibility screening and ligand-protein-interface design.
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Affiliation(s)
- Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80305, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80305, USA.
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23
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Beltrán J, Steiner PJ, Bedewitz M, Wei S, Peterson FC, Li Z, Hughes BE, Hartley Z, Robertson NR, Medina-Cucurella AV, Baumer ZT, Leonard AC, Park SY, Volkman BF, Nusinow DA, Zhong W, Wheeldon I, Cutler SR, Whitehead TA. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat Biotechnol 2022; 40:1855-1861. [PMID: 35726092 PMCID: PMC9750858 DOI: 10.1038/s41587-022-01364-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 05/17/2022] [Indexed: 01/14/2023]
Abstract
A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense-response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense-response applications.
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Affiliation(s)
- Jesús Beltrán
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Matthew Bedewitz
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Shuang Wei
- Department of Biochemistry, University of California, Riverside, Riverside, CA, USA
| | - Francis C Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zongbo Li
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Brigid E Hughes
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zachary Hartley
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Nicholas R Robertson
- Department of Bioengineering, University of California, Riverside, Riverside, USA
| | | | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sang-Youl Park
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
| | - Brian F Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Wenwan Zhong
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Ian Wheeldon
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA.
| | - Sean R Cutler
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA.
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Center for Plant Cell Biology, University of California, Riverside, Riverside, CA, USA.
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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24
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McCann JJ, Pike DH, Brown MC, Crouse DT, Nanda V, Koder RL. Computational design of a sensitive, selective phase-changing sensor protein for the VX nerve agent. SCIENCE ADVANCES 2022; 8:eabh3421. [PMID: 35857443 PMCID: PMC9258810 DOI: 10.1126/sciadv.abh3421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
The VX nerve agent is one of the deadliest chemical warfare agents. Specific, sensitive, real-time detection methods for this neurotoxin have not been reported. The creation of proteins that use biological recognition to fulfill these requirements using directed evolution or library screening methods has been hampered because its toxicity makes laboratory experimentation extraordinarily expensive. A pair of VX-binding proteins were designed using a supercharged scaffold that couples a large-scale phase change from unstructured to folded upon ligand binding, enabling fully internal binding sites that present the maximum surface area possible for high affinity and specificity in target recognition. Binding site residues were chosen using a new distributed evolutionary algorithm implementation in protCAD. Both designs detect VX at parts per billion concentrations with high specificity. Computational design of fully buried molecular recognition sites, in combination with supercharged phase-changing chassis proteins, enables the ready development of a new generation of small-molecule biosensors.
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Affiliation(s)
- James J. McCann
- Department of Physics, The City College of New York, New York, NY 10031, USA
| | - Douglas H. Pike
- Center for Advanced Biotechnology and Medicine and the Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Mia C. Brown
- Department of Physics, The City College of New York, New York, NY 10031, USA
| | - David T. Crouse
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine and the Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ronald L. Koder
- Department of Physics, The City College of New York, New York, NY 10031, USA
- Graduate Programs of Physics, Biology, Chemistry, and Biochemistry, The Graduate Center of CUNY, New York, NY 10016, USA
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25
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Muthusamy A, Kim CH, Virgil SC, Knox HJ, Marvin JS, Nichols AL, Cohen BN, Dougherty DA, Looger LL, Lester HA. Three Mutations Convert the Selectivity of a Protein Sensor from Nicotinic Agonists to S-Methadone for Use in Cells, Organelles, and Biofluids. J Am Chem Soc 2022; 144:8480-8486. [PMID: 35446570 PMCID: PMC9121368 DOI: 10.1021/jacs.2c02323] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Indexed: 11/28/2022]
Abstract
We report a reagentless, intensity-based S-methadone fluorescent sensor, iS-methadoneSnFR, consisting of a circularly permuted GFP inserted within the sequence of a mutated bacterial periplasmic binding protein (PBP). We evolved a previously reported nicotine-binding PBP to become a selective S-methadone-binding sensor, via three mutations in the PBP's second shell and hinge regions. iS-methadoneSnFR displays the necessary sensitivity, kinetics, and selectivity─notably enantioselectivity against R-methadone─for biological applications. Robust iS-methadoneSnFR responses in human sweat and saliva and mouse serum enable diagnostic uses. Expression and imaging in mammalian cells demonstrate that S-methadone enters at least two organelles and undergoes acid trapping in the Golgi apparatus, where opioid receptors can signal. This work shows a straightforward strategy in adapting existing PBPs to serve real-time applications ranging from subcellular to personal pharmacokinetics.
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Affiliation(s)
- Anand
K. Muthusamy
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91106, United States
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Charlene H. Kim
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Scott C. Virgil
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Hailey J. Knox
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Jonathan S. Marvin
- Howard
Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia 20147, United States
| | - Aaron L. Nichols
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Bruce N. Cohen
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Dennis A. Dougherty
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91106, United States
| | - Loren L. Looger
- Howard
Hughes Medical Institute, University of
California, San Diego, San Diego, California 92093, United States
| | - Henry A. Lester
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91106, United States
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26
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Xie B, Goldberg A, Shi L. A comprehensive evaluation of the potential binding poses of fentanyl and its analogs at the µ-opioid receptor. Comput Struct Biotechnol J 2022; 20:2309-2321. [PMID: 35615021 PMCID: PMC9123087 DOI: 10.1016/j.csbj.2022.05.013] [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: 02/13/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022] Open
Abstract
Fentanyl and its analogs are selective agonists of the µ-opioid receptor (MOR). Among novel synthetic opioids (NSOs), they dominate the recreational drug market and are the main culprits for the opioid crisis, which has been exacerbated by the COVID-19 pandemic. By taking advantage of the crystal structures of the MOR, several groups have investigated the binding mechanism of fentanyl, but have not reached a consensus, in terms of both the binding orientation and the fentanyl conformation. Thus, the binding mechanism of fentanyl at the MOR remains an unsolved and challenging question. Here, we carried out a systematic computational study to investigate the preferred fentanyl conformations, and how these conformations are being accommodated in the MOR binding pocket. We characterized the free energy landscape of fentanyl conformations with metadynamics simulations, and compared and evaluated several possible fentanyl binding conditions in the MOR with long-timescale molecular dynamics simulations. Our results indicate that the most preferred binding pose in the MOR binding pocket corresponds well with the global minimum on the energy landscape of fentanyl in the absence of the receptor, while the energy landscape can be reconfigured by modifying the fentanyl scaffold. The interactions with the receptor may stabilize a slightly unfavored fentanyl conformation in an alternative binding pose. By extending similar investigations to fentanyl analogs, our findings establish a structure–activity relationship of fentanyl binding at the MOR. In addition to providing a structural basis to understand the potential toxicity of the emerging NSOs, such insights will contribute to developing new, safer analgesics.
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27
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Kretschmer S, Kortemme T. Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:659-674. [PMID: 36531560 PMCID: PMC9754107 DOI: 10.1109/jproc.2022.3157898] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.
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Affiliation(s)
- Simon Kretschmer
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94158 USA, and affiliated with the California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158 USA
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28
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Chen Y, Chen Q, Liu H. DEPACT and PACMatch: A Workflow of Designing De Novo Protein Pockets to Bind Small Molecules. J Chem Inf Model 2022; 62:971-985. [PMID: 35171604 DOI: 10.1021/acs.jcim.1c01398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Engineering of new functional proteins such as enzymes and biosensors involves the design of new protein pockets for the specific binding of small molecules. Here, we report a workflow composed of two new computational methods to execute this task. The DEPACT (Design Pocket as a Cluster based on Templates) method is a data-driven approach to design and evaluate small-molecule-binding pockets as isolated clusters, while the PACMatch method is a computational approach to match pocket residues in a cluster model to positions on given protein scaffolds. Using DEPACT and its scoring function, pocket clusters of natural-pocket-like chemical compositions and protein-ligand interaction strength can be designed. DEPACT can design pocket clusters containing water- or metal-ion-mediated protein-ligand interactions. While being able to efficiently treat relatively large pocket cluster models (e.g., of around 10 pocket residues), PACMatch outperforms previous methods in test cases of recovering the native positions of pocket residues in natural enzyme-substrate complexes.
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Affiliation(s)
- Yaoxi Chen
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Quan Chen
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China.,Biomedical Sciences and Health Laboratory of Anhui Province, University of Science & Technology of China, Hefei, Anhui 230027, China
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China.,Biomedical Sciences and Health Laboratory of Anhui Province, University of Science & Technology of China, Hefei, Anhui 230027, China.,School of Data Science, University of Science and Technology of China, Hefei, Anhui 230027, China
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29
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Abstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design. Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.
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30
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Simplified quality assessment for small-molecule ligands in the Protein Data Bank. Structure 2022; 30:252-262.e4. [PMID: 35026162 PMCID: PMC8849442 DOI: 10.1016/j.str.2021.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/14/2021] [Accepted: 10/06/2021] [Indexed: 02/05/2023]
Abstract
More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.
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31
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Miller CA, Ho JML, Bennett MR. Strategies for Improving Small-Molecule Biosensors in Bacteria. BIOSENSORS 2022; 12:bios12020064. [PMID: 35200325 PMCID: PMC8869690 DOI: 10.3390/bios12020064] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 05/03/2023]
Abstract
In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.
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Affiliation(s)
- Corwin A. Miller
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
| | - Joanne M. L. Ho
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
| | - Matthew R. Bennett
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
- Department of Bioengineering, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA
- Correspondence:
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32
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Pan X, Kortemme T. De novo protein fold families expand the designable ligand binding site space. PLoS Comput Biol 2021; 17:e1009620. [PMID: 34807909 PMCID: PMC8648124 DOI: 10.1371/journal.pcbi.1009620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 12/06/2021] [Accepted: 11/08/2021] [Indexed: 11/19/2022] Open
Abstract
A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein “scaffold”, which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions. De novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be designed into the de novo scaffolds is important for engineering novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be designed into de novo scaffolds and naturally existing scaffolds with the same fold topologies. The results showed that de novo scaffolds significantly expanded the potential ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (XP); (TK)
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, United States of America
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, United States of America
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (XP); (TK)
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33
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Sinhorini LF, Rodrigues CH, Leite VB, Bruni AT. Synthetic fentanyls evaluation and characterization by infrared spectroscopy employing in silico methods. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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34
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Recent applications of bio-engineering principles to modulate the functionality of proteins in food systems. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.04.055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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35
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Wu Y, Jameel A, Xing XH, Zhang C. Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol 2021; 40:38-59. [PMID: 33958227 DOI: 10.1016/j.tibtech.2021.04.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/17/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022]
Abstract
Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.
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Affiliation(s)
- Yinan Wu
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Aysha Jameel
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xin-Hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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36
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Meinen BA, Bahl CD. Breakthroughs in computational design methods open up new frontiers for de novo protein engineering. Protein Eng Des Sel 2021; 34:6243354. [DOI: 10.1093/protein/gzab007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/16/2021] [Accepted: 03/08/2021] [Indexed: 02/03/2023] Open
Abstract
Abstract
Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.
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Affiliation(s)
- Ben A Meinen
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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37
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Biswas S, Khimulya G, Alley EC, Esvelt KM, Church GM. Low-N protein engineering with data-efficient deep learning. Nat Methods 2021; 18:389-396. [PMID: 33828272 DOI: 10.1038/s41592-021-01100-y] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 02/22/2021] [Indexed: 11/09/2022]
Abstract
Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.
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Affiliation(s)
- Surojit Biswas
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.,Nabla Bio, Inc., Boston, MA, USA
| | | | - Ethan C Alley
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin M Esvelt
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - George M Church
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. .,Department of Genetics, Harvard Medical School, Boston, MA, USA.
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38
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Feng Z, Westbrook JD, Sala R, Smart OS, Bricogne G, Matsubara M, Yamada I, Tsuchiya S, Aoki-Kinoshita KF, Hoch JC, Kurisu G, Velankar S, Burley SK, Young JY. Enhanced validation of small-molecule ligands and carbohydrates in the Protein Data Bank. Structure 2021; 29:393-400.e1. [PMID: 33657417 PMCID: PMC8026741 DOI: 10.1016/j.str.2021.02.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/22/2021] [Accepted: 02/11/2021] [Indexed: 12/19/2022]
Abstract
The Worldwide Protein Data Bank (wwPDB) has provided validation reports based on recommendations from community Validation Task Forces for structures in the PDB since 2013. To further enhance validation of small molecules as recommended from the 2016 Ligand Validation Workshop, wwPDB, Global Phasing Ltd., and the Noguchi Institute, recently formed a public/private partnership to incorporate some of their software tools into the wwPDB validation package. Augmented wwPDB validation report features include: two-dimensional (2D) diagrams of small-molecule ligands and carbohydrates, highlighting geometric validation outcomes; 2D topological diagrams of oligosaccharides present in branched entities generated using 2D Symbol Nomenclature for Glycan representation; and views of 3D electron density maps for ligands and carbohydrates, illustrating the goodness-of-fit between the atomic structure and experimental data (X-ray crystallographic structures only). These improvements will impact confidence in ligand conformation and ligand-macromolecular interactions that will aid in understanding biochemical function and contribute to small-molecule drug discovery.
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Affiliation(s)
- Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Raul Sala
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Oliver S Smart
- Global Phasing Ltd., Sheraton House, Castle Park, Cambridge CB3 0AX, UK
| | - Gérard Bricogne
- Global Phasing Ltd., Sheraton House, Castle Park, Cambridge CB3 0AX, UK
| | - Masaaki Matsubara
- The Noguchi Institute, 1-9-7, Kaga, Itabashi-ku, Tokyo 173-0003, Japan
| | - Issaku Yamada
- The Noguchi Institute, 1-9-7, Kaga, Itabashi-ku, Tokyo 173-0003, Japan
| | | | - Kiyoko F Aoki-Kinoshita
- Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji-shi, Tokyo 192-8577, Japan; Glycan & Life Systems Integration Center, Soka University, 1-236 Tangi-machi, Hachioji-shi, Tokyo 192-8577, Japan
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, University of Connecticut, UConn Health, 263 Farmington Avenue, Farmington, CT 06030-3305, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita-shi, Osaka 565-0871, Japan
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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39
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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40
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Ding D, Li J, Bai D, Fang H, Lin J, Zhang D. Biosensor-based monitoring of the central metabolic pathway metabolites. Biosens Bioelectron 2020; 167:112456. [DOI: 10.1016/j.bios.2020.112456] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/08/2020] [Accepted: 07/14/2020] [Indexed: 12/31/2022]
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41
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Lucas JE, Kortemme T. New computational protein design methods for de novo small molecule binding sites. PLoS Comput Biol 2020; 16:e1008178. [PMID: 33017412 PMCID: PMC7575090 DOI: 10.1371/journal.pcbi.1008178] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 10/20/2020] [Accepted: 07/22/2020] [Indexed: 11/19/2022] Open
Abstract
Protein binding to small molecules is fundamental to many biological processes, yet it remains challenging to predictively design this functionality de novo. Current state-of-the-art computational design methods typically rely on existing small molecule binding sites or protein scaffolds with existing shape complementarity for a target ligand. Here we introduce new methods that utilize pools of discrete contacts between protein side chains and defined small molecule ligand substructures (ligand fragments) observed in the Protein Data Bank. We use the Rosetta Molecular Modeling Suite to recombine protein side chains in these contact pools to generate hundreds of thousands of energetically favorable binding sites for a target ligand. These composite binding sites are built into existing scaffold proteins matching the intended binding site geometry with high accuracy. In addition, we apply pools of side chain rotamers interacting with the target ligand to augment Rosetta's conventional design machinery and improve key metrics known to be predictive of design success. We demonstrate that our method reliably builds diverse binding sites into different scaffold proteins for a variety of target molecules. Our generalizable de novo ligand binding site design method provides a foundation for versatile design of protein to interface previously unattainable molecules for applications in medical diagnostics and synthetic biology.
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Affiliation(s)
- James E. Lucas
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States of America
| | - Tanja Kortemme
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, United States of America
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States of America
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42
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Lambert AR, Hallinan JP, Werther R, Glöw D, Stoddard BL. Optimization of Protein Thermostability and Exploitation of Recognition Behavior to Engineer Altered Protein-DNA Recognition. Structure 2020; 28:760-775.e8. [PMID: 32359399 PMCID: PMC7347439 DOI: 10.1016/j.str.2020.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/17/2020] [Accepted: 04/11/2020] [Indexed: 01/07/2023]
Abstract
The redesign of a macromolecular binding interface and corresponding alteration of recognition specificity is a challenging endeavor that remains recalcitrant to computational approaches. This is particularly true for the redesign of DNA binding specificity, which is highly dependent upon bending, hydrogen bonds, electrostatic contacts, and the presence of solvent and counterions throughout the molecular interface. Thus, redesign of protein-DNA binding specificity generally requires iterative rounds of amino acid randomization coupled to selections. Here, we describe the importance of scaffold thermostability for protein engineering, coupled with a strategy that exploits the protein's specificity profile, to redesign the specificity of a pair of meganucleases toward three separate genomic targets. We determine and describe a series of changes in protein sequence, stability, structure, and activity that accumulate during the engineering process, culminating in fully retargeted endonucleases.
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Affiliation(s)
- Abigail R. Lambert
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. Seattle WA 98109 USA
| | - Jazmine P. Hallinan
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. Seattle WA 98109 USA
| | - Rachel Werther
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. Seattle WA 98109 USA
| | - Dawid Glöw
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. Seattle WA 98109 USA,Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Barry L. Stoddard
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. Seattle WA 98109 USA,Corresponding Author and Lead Contact:
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43
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Patron NJ. Beyond natural: synthetic expansions of botanical form and function. THE NEW PHYTOLOGIST 2020; 227:295-310. [PMID: 32239523 PMCID: PMC7383487 DOI: 10.1111/nph.16562] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/03/2020] [Indexed: 05/05/2023]
Abstract
Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.
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Affiliation(s)
- Nicola J. Patron
- Engineering BiologyEarlham InstituteNorwich Research Park, NorwichNorfolkNR4 7UZUK
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44
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Overcoming Near-Cognate Suppression in a Release Factor 1-Deficient Host with an Improved Nitro-Tyrosine tRNA Synthetase. J Mol Biol 2020; 432:4690-4704. [PMID: 32569745 DOI: 10.1016/j.jmb.2020.06.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/10/2020] [Accepted: 06/13/2020] [Indexed: 12/15/2022]
Abstract
Genetic code expansion (GCE) technologies incorporate non-canonical amino acids (ncAAs) into proteins at amber stop codons. To avoid unwanted truncated protein and improve ncAA-protein yields, genomically recoded strains of Escherichia coli lacking Release Factor 1 (RF1) are becoming increasingly popular expression hosts for GCE applications. In the absence of RF1, however, endogenous near-cognate amber suppressing tRNAs can lead to contaminating protein forms with natural amino acids in place of the ncAA. Here, we show that a second-generation amino-acyl tRNA synthetase (aaRS)/tRNACUA pair for site-specific incorporation of 3-nitro-tyrosine could not outcompete near-cognate suppression in an RF1-deficient expression host and therefore could not produce homogenously nitrated protein. To resolve this, we used Rosetta to target positions in the nitroTyr aaRS active site for improved substrate binding, and then constructed of a small library of variants to subject to standard selection protocols. The top selected variant had an ~2-fold greater efficiency, and remarkably, this relatively small improvement enabled homogeneous incorporation of nitroTyr in an RF1-deficient expression host and thus eliminates truncation issues associated with typical RF1-containing expression hosts. Structural and biochemical data suggest the aaRS efficiency improvement is based on higher affinity substrate binding. Taken together, the modest improvement in aaRS efficiency provides a large practical impact and expands our ability to study the role protein nitration plays in disease development through producing homogenous, truncation-free nitroTyr-containing protein. This work establishes Rosetta-guided design and incremental aaRS improvement as a viable and accessible path to improve GCE systems challenged by truncation and/or near-cognate suppression issues.
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45
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Glasgow AA, Huang YM, Mandell DJ, Thompson M, Ritterson R, Loshbaugh AL, Pellegrino J, Krivacic C, Pache RA, Barlow KA, Ollikainen N, Jeon D, Kelly MJS, Fraser JS, Kortemme T. Computational design of a modular protein sense-response system. Science 2020; 366:1024-1028. [PMID: 31754004 DOI: 10.1126/science.aax8780] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/07/2019] [Indexed: 12/28/2022]
Abstract
Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.
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Affiliation(s)
- Anum A Glasgow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Yao-Ming Huang
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel J Mandell
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Michael Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ryan Ritterson
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Jenna Pellegrino
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Cody Krivacic
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA
| | - Roland A Pache
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Kyle A Barlow
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Deborah Jeon
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Mark J S Kelly
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. .,Bioinformatics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
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46
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Leydon AR, Gala HP, Guiziou S, Nemhauser JL. Engineering Synthetic Signaling in Plants. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:767-788. [PMID: 32092279 DOI: 10.1146/annurev-arplant-081519-035852] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Synthetic signaling is a branch of synthetic biology that aims to understand native genetic regulatory mechanisms and to use these insights to engineer interventions and devices that achieve specified design parameters. Applying synthetic signaling approaches to plants offers the promise of mitigating the worst effects of climate change and providing a means to engineer crops for entirely novel environments, such as those in space travel. The ability to engineer new traits using synthetic signaling methods will require standardized libraries of biological parts and methods to assemble them; the decoupling of complex processes into simpler subsystems; and mathematical models that can accelerate the design-build-test-learn cycle. The field of plant synthetic signaling is relatively new, but it is poised for rapid advancement. Translation from the laboratory to the field is likely to be slowed, however, by the lack of constructive dialogue between researchers and other stakeholders.
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Affiliation(s)
- Alexander R Leydon
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Hardik P Gala
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Sarah Guiziou
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Jennifer L Nemhauser
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
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47
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Meyer A, Saaem I, Silverman A, Varaljay VA, Mickol R, Blum S, Tobias AV, Schwalm ND, Mojadedi W, Onderko E, Bristol C, Liu S, Pratt K, Casini A, Eluere R, Moser F, Drake C, Gupta M, Kelley-Loughnane N, Lucks JP, Akingbade KL, Lux MP, Glaven S, Crookes-Goodson W, Jewett MC, Gordon DB, Voigt CA. Organism Engineering for the Bioproduction of the Triaminotrinitrobenzene (TATB) Precursor Phloroglucinol (PG). ACS Synth Biol 2019; 8:2746-2755. [PMID: 31750651 DOI: 10.1021/acssynbio.9b00393] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Organism engineering requires the selection of an appropriate chassis, editing its genome, combining traits from different source species, and controlling genes with synthetic circuits. When a strain is needed for a new target objective, for example, to produce a chemical-of-need, the best strains, genes, techniques, software, and expertise may be distributed across laboratories. Here, we report a project where we were assigned phloroglucinol (PG) as a target, and then combined unique capabilities across the United States Army, Navy, and Air Force service laboratories with the shared goal of designing an organism to produce this molecule. In addition to the laboratory strain Escherichia coli, organisms were screened from soil and seawater. Putative PG-producing enzymes were mined from a strain bank of bacteria isolated from aircraft and fuel depots. The best enzyme was introduced into the ocean strain Marinobacter atlanticus CP1 with its genome edited to redirect carbon flux from natural fatty acid ester (FAE) production. PG production was also attempted in Bacillus subtilis and Clostridium acetobutylicum. A genetic circuit was constructed in E. coli that responds to PG accumulation, which was then ported to an in vitro paper-based system that could serve as a platform for future low-cost strain screening or for in-field sensing. Collectively, these efforts show how distributed biotechnology laboratories with domain-specific expertise can be marshalled to quickly provide a solution for a targeted organism engineering project, and highlights data and material sharing protocols needed to accelerate future efforts.
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Affiliation(s)
- Adam Meyer
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Ishtiaq Saaem
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Adam Silverman
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Vanessa A. Varaljay
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Rebecca Mickol
- American Society for Engineering Education, 1818 N Street NW Suite 600, Washington, D.C. 20036, United States
| | - Steven Blum
- U.S. Army Combat Capabilities Development Command Chemical Biological Center, 8198 Blackhawk Road, Aberdeen Proving Ground, Maryland 21010, United States
| | - Alexander V. Tobias
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Nathan D. Schwalm
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Wais Mojadedi
- Oak Ridge Associate Universities, P.O.
Box 117, MS-29, Oak Ridge, Tennessee 37831, United States
| | - Elizabeth Onderko
- National Research Council, 500 5th Street NW, Washington, D.C. 20001, United States
| | - Cassandra Bristol
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Shangtao Liu
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
| | - Katelin Pratt
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Arturo Casini
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Raissa Eluere
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Felix Moser
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Carrie Drake
- UES, Inc., 4401 Dayton-Xenia Road, Dayton, Ohio 45432, United States
| | - Maneesh Gupta
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Nancy Kelley-Loughnane
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Julius P. Lucks
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Katherine L. Akingbade
- U.S. Army Research Laboratory, FCDD-RLS-EB, 2800 Powder Mill Road, Adelphi, Maryland 20783, United States
| | - Matthew P. Lux
- U.S. Army Combat Capabilities Development Command Chemical Biological Center, 8198 Blackhawk Road, Aberdeen Proving Ground, Maryland 21010, United States
| | - Sarah Glaven
- Center for Bio/Molecular Science and Engineering, Naval Research Laboratory, Washington, D.C. 20375, United States
| | - Wendy Crookes-Goodson
- Soft Matter Materials Branch, Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433, United States
| | - Michael C. Jewett
- Center for Synthetic Biology, Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - D. Benjamin Gordon
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Christopher A. Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- The Foundry, 75 Ames Street, Cambridge Massachusetts 02142, United States
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48
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Dimas RP, Jiang XL, Alberto de la Paz J, Morcos F, Chan CTY. Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset. Nucleic Acids Res 2019; 47:5449-5463. [PMID: 31162606 PMCID: PMC6547410 DOI: 10.1093/nar/gkz280] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022] Open
Abstract
Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information.
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Affiliation(s)
- Rey P Dimas
- Department of Biology, The University of Texas at Tyler, Tyler, TX 75799, USA
| | - Xian-Li Jiang
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Jose Alberto de la Paz
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Faruck Morcos
- Department of Biological Sciences, The University of Texas at Dallas, Dallas, TX 75080, USA.,Department of Bioengineering, The University of Texas at Dallas, Dallas, TX 75080, USA.,Center for Systems Biology, The University of Texas at Dallas, Dallas, TX 75080, USA
| | - Clement T Y Chan
- Department of Biology, The University of Texas at Tyler, Tyler, TX 75799, USA.,Department of Chemistry and Biochemistry, The University of Texas at Tyler, Tyler, TX 75799, USA
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Chica RA. Designer sense-response systems. Science 2019; 366:952-953. [DOI: 10.1126/science.aaz8085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Artificial chemically induced protein dimerization yields biological sensor-actuator devices
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Affiliation(s)
- Roberto A. Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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50
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Bera K, Kamajaya A, Shivange AV, Muthusamy AK, Nichols AL, Borden PM, Grant S, Jeon J, Lin E, Bishara I, Chin TM, Cohen BN, Kim CH, Unger EK, Tian L, Marvin JS, Looger LL, Lester HA. Biosensors Show the Pharmacokinetics of S-Ketamine in the Endoplasmic Reticulum. Front Cell Neurosci 2019; 13:499. [PMID: 31798415 PMCID: PMC6874132 DOI: 10.3389/fncel.2019.00499] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 10/22/2019] [Indexed: 12/20/2022] Open
Abstract
The target for the “rapid” (<24 h) antidepressant effects of S-ketamine is unknown, vitiating programs to rationally develop more effective rapid antidepressants. To describe a drug’s target, one must first understand the compartments entered by the drug, at all levels—the organ, the cell, and the organelle. We have, therefore, developed molecular tools to measure the subcellular, organellar pharmacokinetics of S-ketamine. The tools are genetically encoded intensity-based S-ketamine-sensing fluorescent reporters, iSKetSnFR1 and iSKetSnFR2. In solution, these biosensors respond to S-ketamine with a sensitivity, S-slope = delta(F/F0)/(delta[S-ketamine]) of 0.23 and 1.9/μM, respectively. The iSKetSnFR2 construct allows measurements at <0.3 μM S-ketamine. The iSKetSnFR1 and iSKetSnFR2 biosensors display >100-fold selectivity over other ligands tested, including R-ketamine. We targeted each of the sensors to either the plasma membrane (PM) or the endoplasmic reticulum (ER). Measurements on these biosensors expressed in Neuro2a cells and in human dopaminergic neurons differentiated from induced pluripotent stem cells (iPSCs) show that S-ketamine enters the ER within a few seconds after appearing in the external solution near the PM, then leaves as rapidly after S-ketamine is removed from the extracellular solution. In cells, S-slopes for the ER and PM-targeted sensors differ by <2-fold, indicating that the ER [S-ketamine] is less than 2-fold different from the extracellular [S-ketamine]. Organelles represent potential compartments for the engagement of S-ketamine with its antidepressant target, and potential S-ketamine targets include organellar ion channels, receptors, and transporters.
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Affiliation(s)
- Kallol Bera
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Aron Kamajaya
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Amol V Shivange
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Anand K Muthusamy
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States.,Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Aaron L Nichols
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States.,Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Philip M Borden
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Stephen Grant
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Janice Jeon
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Elaine Lin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Ishak Bishara
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Theodore M Chin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Bruce N Cohen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Charlene H Kim
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Elizabeth K Unger
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, United States
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, United States
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Henry A Lester
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
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