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Zhao M, Hu M, Han R, Ye C, Li X, Wang T, Liu Y, Xue Z, Liu K. Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione. Synth Syst Biotechnol 2024; 9:436-444. [PMID: 38616975 PMCID: PMC11015099 DOI: 10.1016/j.synbio.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 03/03/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.
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Affiliation(s)
| | | | - Rumeng Han
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Chao Ye
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Xiangfei Li
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Tianwen Wang
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Yan Liu
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Zhenglian Xue
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Kun Liu
- Anhui Engineering Laboratory for Industrial Microbiology Molecular Breeding, College of Biology and Food Engineering, Anhui Polytechnic University, Wuhu 241000, China
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2
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Cui Z, Ayva CE, Liew YJ, Guo Z, Mutschler R, Dreier B, Fiorito MM, Walden P, Howard CB, Ely F, Plückthun A, Pretorius C, Ungerer JP, Buckle AM, Alexandrov K. mRNA Display Pipeline for Protein Biosensor Construction. ACS Sens 2024. [PMID: 38807313 DOI: 10.1021/acssensors.3c02471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Despite the significant potential of protein biosensors, their construction remains a trial-and-error process. The most obvious approach for addressing this is to utilize modular biosensor architectures where specificity-conferring modalities can be readily generated to recognize new targets. Toward this goal, we established a workflow that uses mRNA display-based selection of hyper-stable monobody domains for the target of choice or ribosome display to select equally stable DARPins. These binders were integrated into a two-component allosteric biosensor architecture based on a calmodulin-reporter chimera. This workflow was tested by developing biosensors for liver toxicity markers such as cytosolic aspartate aminotransferase, mitochondrial aspartate aminotransferase, and alanine aminotransferase 1. We demonstrate that our pipeline consistently produced >103 unique binders for each target within a week. Our analysis revealed that the affinity of the binders for their targets was not a direct predictor of the binder's performance in a biosensor context. The interactions between the binding domains and the reporter module affect the biosensor activity and the dynamic range. We conclude that following binding domain selection, the multiplexed biosensor assembly and prototyping appear to be the most promising approach for identifying biosensors with the desired properties.
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Affiliation(s)
- Zhenling Cui
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland 4001, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Cagla Ergun Ayva
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Yi Jin Liew
- CSIRO Health & Biosecurity, Westmead, New South Wales 2145,Australia
| | - Zhong Guo
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland 4001, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Roxane Mutschler
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Birgit Dreier
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Maria M Fiorito
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Patricia Walden
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
| | - Christopher B Howard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Andreas Plückthun
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Carel Pretorius
- Department of Chemical Pathology, Pathology Queensland, Brisbane, Queensland 4006, Australia
- Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jacobus Pj Ungerer
- Department of Chemical Pathology, Pathology Queensland, Brisbane, Queensland 4006, Australia
- Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Kirill Alexandrov
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland 4001, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland 4001, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland 4001, Australia
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3
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Chen D, Han Z, Zhang J, Xue L, Liu S. Additive Manufacturing Provides Infinite Possibilities for Self-Sensing Technology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400816. [PMID: 38767180 DOI: 10.1002/advs.202400816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/12/2024] [Indexed: 05/22/2024]
Abstract
Integrating sensors and other functional parts in one device can enable a new generation of integrated intelligent devices that can perform self-sensing and monitoring autonomously. Applications include buildings that detect and repair damage, robots that monitor conditions and perform real-time correction and reconstruction, aircraft capable of real-time perception of the internal and external environment, and medical devices and prosthetics with a realistic sense of touch. Although integrating sensors and other functional parts into self-sensing intelligent devices has become increasingly common, additive manufacturing has only been marginally explored. This review focuses on additive manufacturing integrated design, printing equipment, and printable materials and stuctures. The importance of the material, structure, and function of integrated manufacturing are highlighted. The study summarizes current challenges to be addressed and provides suggestions for future development directions.
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Affiliation(s)
- Daobing Chen
- The Institute of Technological Science, Wuhan University, South Donghu Road 8, Wuhan, 430072, China
| | - Zhiwu Han
- The Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, Jilin, 130022, China
| | - Junqiu Zhang
- The Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, Jilin, 130022, China
| | - Longjian Xue
- School of Power and Mechanical Engineering, Wuhan University, South Donghu Road 8, Wuhan, 430072, China
| | - Sheng Liu
- The Institute of Technological Science, Wuhan University, South Donghu Road 8, Wuhan, 430072, China
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4
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Lu M, Sha Y, Kumar V, Xu Z, Zhai R, Jin M. Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis. Biotechnol Adv 2024; 72:108339. [PMID: 38508427 DOI: 10.1016/j.biotechadv.2024.108339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/07/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024]
Abstract
As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.
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Affiliation(s)
- Minrui Lu
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yuanyuan Sha
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Vinod Kumar
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, United Kingdom
| | - Zhaoxian Xu
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Rui Zhai
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Mingjie Jin
- School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Biorefinery Research Institution, Nanjing University of Science and Technology, Nanjing 210094, China.
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5
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Xu Y, Hu X, Wang C, Liu Y, Chen Q, Liu H. De novo design of cavity-containing proteins with a backbone-centered neural network energy function. Structure 2024; 32:424-432.e4. [PMID: 38325370 DOI: 10.1016/j.str.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 10/04/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.
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Affiliation(s)
- Yang Xu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; 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
| | - Xiuhong Hu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; 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
| | - Chenchen Wang
- 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
| | - Yongrui 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
| | - Quan Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; 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 and 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 and 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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6
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Chaisupa P, Wright RC. State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering. SLAS Technol 2024; 29:100113. [PMID: 37918525 DOI: 10.1016/j.slast.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/18/2023] [Accepted: 10/25/2023] [Indexed: 11/04/2023]
Abstract
Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.
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Affiliation(s)
- Patarasuda Chaisupa
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States
| | - R Clay Wright
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, United States; Translational Plant Sciences Center (TPSC), Virginia Tech, Blacksburg, VA 24061, United States.
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7
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Li Z, Shen Q, Usher ET, Anderson AP, Iburg M, Lin R, Zimmer B, Meyer MD, Holehouse AS, You L, Chilkoti A, Dai Y, Lu GJ. Phase transition of GvpU regulates gas vesicle clustering in bacteria. Nat Microbiol 2024; 9:1021-1035. [PMID: 38553608 DOI: 10.1038/s41564-024-01648-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 02/20/2024] [Indexed: 04/06/2024]
Abstract
Gas vesicles (GVs) are microbial protein organelles that support cellular buoyancy. GV engineering has multiple applications, including reporter gene imaging, acoustic control and payload delivery. GVs often cluster into a honeycomb pattern to minimize occupancy of the cytosol. The underlying molecular mechanism and the influence on cellular physiology remain unknown. Using genetic, biochemical and imaging approaches, here we identify GvpU from Priestia megaterium as a protein that regulates GV clustering in vitro and upon expression in Escherichia coli. GvpU binds to the C-terminal tail of the core GV shell protein and undergoes a phase transition to form clusters in subsaturated solution. These properties of GvpU tune GV clustering and directly modulate bacterial fitness. GV variants can be designed with controllable sensitivity to GvpU-mediated clustering, enabling design of genetically tunable biosensors. Our findings elucidate the molecular mechanisms and functional roles of GV clustering, enabling its programmability for biomedical applications.
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Affiliation(s)
- Zongru Li
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Qionghua Shen
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Emery T Usher
- Department of Biomedical Engineering, Center for Biomolecular Condensates, Washington University in St. Louis, Saint Louis, MO, USA
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, Saint Louis, MO, USA
| | | | - Manuel Iburg
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Richard Lin
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Brandon Zimmer
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Matthew D Meyer
- Shared Equipment Authority, Rice University, Houston, TX, USA
| | - Alex S Holehouse
- Department of Biomedical Engineering, Center for Biomolecular Condensates, Washington University in St. Louis, Saint Louis, MO, USA
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, Saint Louis, MO, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Quantitative BioDesign, Duke University, Durham, NC, USA.
| | - Ashutosh Chilkoti
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
| | - Yifan Dai
- Department of Biomedical Engineering, Center for Biomolecular Condensates, Washington University in St. Louis, Saint Louis, MO, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
| | - George J Lu
- Department of Bioengineering, Rice University, Houston, TX, USA.
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8
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Zhao Y, Han J, Huang J, Huang Q, Tao Y, Gu R, Li HY, Zhang Y, Zhang H, Liu H. A miniprotein receptor electrochemical biosensor chip based on quantum dots. LAB ON A CHIP 2024; 24:1875-1886. [PMID: 38372578 DOI: 10.1039/d3lc01100c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Recently protein binders have emerged as a promising substitute for antibodies due to their high specificity and low cost. Herein, we demonstrate an electrochemical biosensor chip through the electronic labelling strategy using lead sulfide (PbS) colloidal quantum dots (CQDs) and the unnatural SARS-CoV-2 spike miniprotein receptor LCB. The unnatural receptor can be utilized as a molecular probe for the construction of CQD-based electrochemical biosensor chips, through which the specific binding of LCB and the spike protein is transduced to sensor electrical signals. The biosensor exhibits a good linear response in the concentration range of 10 pg mL-1 to 1 μg mL-1 (13.94 fM to 1.394 nM) with the limit of detection (LOD) being 3.31 pg mL-1 (4.607 fM for the three-electrode system) and 9.58 fg mL-1 (0.013 fM for the HEMT device). Due to the high sensitivity of the electrochemical biosensor, it was also used to study the binding kinetics between the unnatural receptor LCB and spike protein, which has achieved comparable results as those obtained with commercial equipment. To the best of our knowledge, this is the first example of using a computationally designed miniprotein receptor based on electrochemical methods, and it is the first kinetic assay performed with an electrochemical assay alone. The miniprotein receptor electrochemical biosensor based on QDs is desirable for fabricating high-throughput, large-area, wafer-scale biochips.
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Affiliation(s)
- Yunong Zhao
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Juan Han
- Department of Biotechnology, College of Life Science and Technology, MOE Key Laboratory of Molecular Biophysics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Jing Huang
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Qing Huang
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Yanbing Tao
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Ruiqin Gu
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Hua-Yao Li
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Yang Zhang
- Key Laboratory of Semiconductor Materials Science, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
- College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Houjin Zhang
- Department of Biotechnology, College of Life Science and Technology, MOE Key Laboratory of Molecular Biophysics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
| | - Huan Liu
- School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Optics Valley Laboratory, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
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9
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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10
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Son S, Song WJ. Programming interchangeable and reversible heterooligomeric protein self-assembly using a bifunctional ligand. Chem Sci 2024; 15:2975-2983. [PMID: 38404387 PMCID: PMC10882485 DOI: 10.1039/d3sc05448a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/10/2024] [Indexed: 02/27/2024] Open
Abstract
Protein design for self-assembly allows us to explore the emergence of protein-protein interfaces through various chemical interactions. Heterooligomers, unlike homooligomers, inherently offer a comprehensive range of structural and functional variations. Besides, the macromolecular repertoire and their applications would significantly expand if protein components could be easily interchangeable. This study demonstrates that a rationally designed bifunctional linker containing an enzyme inhibitor and maleimide can guide the formation of diverse protein heterooligomers in an easily applicable and exchangeable manner without extensive sequence optimizations. As proof of concept, we selected four structurally and functionally unrelated proteins, carbonic anhydrase, aldolase, acetyltransferase, and encapsulin, as building block proteins. The combinations of two proteins with the bifunctional linker yielded four two-component heterooligomers with discrete sizes, shapes, and enzyme activities. Besides, the overall size and formation kinetics of the heterooligomers alter upon adding metal chelators, acidic buffer components, and reducing agents, showing the reversibility and tunability in the protein self-assembly. Given that the functional groups of both the linker and protein components are readily interchangeable, our work broadens the scope of protein-assembled architectures and their potential applications as functional biomaterials.
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Affiliation(s)
- Soyeun Son
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
| | - Woon Ju Song
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
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11
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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12
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Wang S, Mao X, Wang F, Zuo X, Fan C. Data Storage Using DNA. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307499. [PMID: 37800877 DOI: 10.1002/adma.202307499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/01/2023] [Indexed: 10/07/2023]
Abstract
The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.
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Affiliation(s)
- Shaopeng Wang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xiuhai Mao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Fei Wang
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaolei Zuo
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chunhai Fan
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
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13
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Kortemme T. De novo protein design-From new structures to programmable functions. Cell 2024; 187:526-544. [PMID: 38306980 PMCID: PMC10990048 DOI: 10.1016/j.cell.2023.12.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/03/2023] [Accepted: 12/19/2023] [Indexed: 02/04/2024]
Abstract
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
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Affiliation(s)
- Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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14
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Vázquez Torres S, Leung PJY, Venkatesh P, Lutz ID, Hink F, Huynh HH, Becker J, Yeh AHW, Juergens D, Bennett NR, Hoofnagle AN, Huang E, MacCoss MJ, Expòsit M, Lee GR, Bera AK, Kang A, De La Cruz J, Levine PM, Li X, Lamb M, Gerben SR, Murray A, Heine P, Korkmaz EN, Nivala J, Stewart L, Watson JL, Rogers JM, Baker D. De novo design of high-affinity binders of bioactive helical peptides. Nature 2024; 626:435-442. [PMID: 38109936 PMCID: PMC10849960 DOI: 10.1038/s41586-023-06953-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 12/07/2023] [Indexed: 12/20/2023]
Abstract
Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.
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Affiliation(s)
- Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Philip J Y Leung
- 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
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Isaac D Lutz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Fabian Hink
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Huu-Hien Huynh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Jessica Becker
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Andy Hsien-Wei Yeh
- 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
| | - Nathaniel R Bennett
- 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
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Marc Expòsit
- 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
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, 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
| | - Joshmyn De La Cruz
- 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
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Mila Lamb
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Piper Heine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Elif Nihal Korkmaz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jeff Nivala
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
| | - Joseph M Rogers
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
| | - 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.
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15
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Zhang JZ, Nguyen WH, Greenwood N, Rose JC, Ong SE, Maly DJ, Baker D. Computationally designed sensors detect endogenous Ras activity and signaling effectors at subcellular resolution. Nat Biotechnol 2024:10.1038/s41587-023-02107-w. [PMID: 38273065 DOI: 10.1038/s41587-023-02107-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024]
Abstract
The utility of genetically encoded biosensors for sensing the activity of signaling proteins has been hampered by a lack of strategies for matching sensor sensitivity to the physiological concentration range of the target. Here we used computational protein design to generate intracellular sensors of Ras activity (LOCKR-based Sensor for Ras activity (Ras-LOCKR-S)) and proximity labelers of the Ras signaling environment (LOCKR-based, Ras activity-dependent Proximity Labeler (Ras-LOCKR-PL)). These tools allow the detection of endogenous Ras activity and labeling of the surrounding environment at subcellular resolution. Using these sensors in human cancer cell lines, we identified Ras-interacting proteins in oncogenic EML4-Alk granules and found that Src-Associated in Mitosis 68-kDa (SAM68) protein specifically enhances Ras activity in the granules. The ability to subcellularly localize endogenous Ras activity should deepen our understanding of Ras function in health and disease and may suggest potential therapeutic strategies.
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Affiliation(s)
- Jason Z Zhang
- 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.
| | - William H Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Nathan Greenwood
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - John C Rose
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Shao-En Ong
- Department of Pharmacology, University of Washington, Seattle, WA, USA
| | - Dustin J Maly
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Department of Chemistry, University of Washington, Seattle, WA, 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.
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16
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Zhao F, Frandsen M, Capodaglio S, Sleiman HF. DNA-Mediated Peptide Assembly into Protein Mimics. J Am Chem Soc 2024; 146:1946-1956. [PMID: 38226787 DOI: 10.1021/jacs.3c08984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
The design of new protein structures is challenging due to their vast sequence space and the complexity of protein folding. Here, we report a new modular DNA-templated strategy to construct protein mimics. We achieve the spatial control of multiple peptide units by conjugation with DNA and hybridization to a branched DNA trimer template followed by covalent stapling of the preorganized peptides into a single unit. A library of protein mimics with different lengths, sequences, and heptad registers has been efficiently constructed. DNA-templated protein mimics show an α-helix or coiled-coil motif formation even when they are constructed from weakly interacting peptide units. Their attached DNA handles can be used to exert dynamic control over the protein mimics' secondary and tertiary structures. This modular strategy will facilitate the development of DNA-encoded protein libraries for the rapid discovery of new therapeutics, enzymes, and antibody mimics.
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Affiliation(s)
- Fangzhou Zhao
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montreal, QC H3A0B8, Canada
| | - Martin Frandsen
- Department of Chemistry and Interdisciplinary Nanoscience Centre (iNANO), Aarhus University, Gustav Wieds Vej 14, Aarhus C, Aarhus 8000, Denmark
| | - Sabrina Capodaglio
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area delle Scienze 17/A, Parma I-43124, Italy
| | - Hanadi F Sleiman
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montreal, QC H3A0B8, Canada
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17
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Plaper T, Merljak E, Fink T, Satler T, Ljubetič A, Lainšček D, Jazbec V, Benčina M, Stevanoska S, Džeroski S, Jerala R. Designed allosteric protein logic. Cell Discov 2024; 10:8. [PMID: 38228615 DOI: 10.1038/s41421-023-00635-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/03/2023] [Indexed: 01/18/2024] Open
Abstract
The regulation of protein function by external or internal signals is one of the key features of living organisms. The ability to directly control the function of a selected protein would represent a valuable tool for regulating biological processes. Here, we present a generally applicable regulation of proteins called INSRTR, based on inserting a peptide into a loop of a target protein that retains its function. We demonstrate the versatility and robustness of coiled-coil-mediated regulation, which enables designs for either inactivation or activation of selected protein functions, and implementation of two-input logic functions with rapid response in mammalian cells. The selection of insertion positions in tested proteins was facilitated by using a predictive machine learning model. We showcase the robustness of the INSRTR strategy on proteins with diverse folds and biological functions, including enzymes, signaling mediators, DNA binders, transcriptional regulators, reporters, and antibody domains implemented as chimeric antigen receptors in T cells. Our findings highlight the potential of INSRTR as a powerful tool for precise control of protein function, advancing our understanding of biological processes and developing biotechnological and therapeutic interventions.
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Affiliation(s)
- Tjaša Plaper
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Estera Merljak
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Tina Fink
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Tadej Satler
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
- Interdisciplinary doctoral study of biomedicine, Medical Faculty, University of Ljubljana, 1000, Ljubljana, Slovenia
| | - Ajasja Ljubetič
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Duško Lainšček
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Vid Jazbec
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
- Interdisciplinary doctoral study of biomedicine, Medical Faculty, University of Ljubljana, 1000, Ljubljana, Slovenia
| | - Mojca Benčina
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
- Centre for Technologies of Gene and Cell Therapy, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Sintija Stevanoska
- Department of knowledge technologies, Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Sašo Džeroski
- Department of knowledge technologies, Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Roman Jerala
- Department of Synthetic Biology and Immunology, National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia.
- Centre for Technologies of Gene and Cell Therapy, Hajdrihova 19, SI-1000, Ljubljana, Slovenia.
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18
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Teng F, Cui T, Zhou L, Gao Q, Zhou Q, Li W. Programmable synthetic receptors: the next-generation of cell and gene therapies. Signal Transduct Target Ther 2024; 9:7. [PMID: 38167329 PMCID: PMC10761793 DOI: 10.1038/s41392-023-01680-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/22/2023] [Accepted: 10/11/2023] [Indexed: 01/05/2024] Open
Abstract
Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.
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Affiliation(s)
- Fei Teng
- University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Tongtong Cui
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qingqin Gao
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qi Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Wei Li
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
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19
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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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20
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Qiu G, Zhang X, deMello AJ, Yao M, Cao J, Wang J. On-site airborne pathogen detection for infection risk mitigation. Chem Soc Rev 2023; 52:8531-8579. [PMID: 37882143 PMCID: PMC10712221 DOI: 10.1039/d3cs00417a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Indexed: 10/27/2023]
Abstract
Human-infecting pathogens that transmit through the air pose a significant threat to public health. As a prominent instance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic has affected the world in an unprecedented manner over the past few years. Despite the dissipating pandemic gloom, the lessons we have learned in dealing with pathogen-laden aerosols should be thoroughly reviewed because the airborne transmission risk may have been grossly underestimated. From a bioanalytical chemistry perspective, on-site airborne pathogen detection can be an effective non-pharmaceutic intervention (NPI) strategy, with on-site airborne pathogen detection and early-stage infection risk evaluation reducing the spread of disease and enabling life-saving decisions to be made. In light of this, we summarize the recent advances in highly efficient pathogen-laden aerosol sampling approaches, bioanalytical sensing technologies, and the prospects for airborne pathogen exposure measurement and evidence-based transmission interventions. We also discuss open challenges facing general bioaerosols detection, such as handling complex aerosol samples, improving sensitivity for airborne pathogen quantification, and establishing a risk assessment system with high spatiotemporal resolution for mitigating airborne transmission risks. This review provides a multidisciplinary outlook for future opportunities to improve the on-site airborne pathogen detection techniques, thereby enhancing the preparedness for more on-site bioaerosols measurement scenarios, such as monitoring high-risk pathogens on airplanes, weaponized pathogen aerosols, influenza variants at the workplace, and pollutant correlated with sick building syndromes.
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Affiliation(s)
- Guangyu Qiu
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
- Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
- Laboratory for Advanced Analytical Technologies, Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
| | - Xiaole Zhang
- Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
- Laboratory for Advanced Analytical Technologies, Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
| | - Andrew J deMello
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg1, Zürich, Switzerland
| | - Maosheng Yao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, China
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Science, China
| | - Jing Wang
- Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
- Laboratory for Advanced Analytical Technologies, Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
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21
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Yu TG, Lee J, Yoon J, Choi JM, Kim DG, Heo WD, Song JJ, Kim HS. Engineering of a Fluorescent Protein for a Sensing of an Intrinsically Disordered Protein through Transition in the Chromophore State. JACS AU 2023; 3:3055-3065. [PMID: 38034956 PMCID: PMC10685427 DOI: 10.1021/jacsau.3c00445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/18/2023] [Accepted: 10/02/2023] [Indexed: 12/02/2023]
Abstract
Intrinsically disordered proteins (IDPs) not only play important roles in biological processes but are also linked with the pathogenesis of various human diseases. Specific and reliable sensing of IDPs is crucial for exploring their roles but remains elusive due to structural plasticity. Here, we present the development of a new type of fluorescent protein for the ratiometric sensing and tracking of an IDP. A β-strand of green fluorescent protein (GFP) was truncated, and the resulting GFP was further engineered to undergo the transition in the absorption maximum upon binding of a target motif within amyloid-β (Aβ) as a model IDP through rational design and directed evolution. Spectroscopic and structural analyses of the engineered truncated GFP demonstrated that a shift in the absorption maximum is driven by the change in the chromophore state from an anionic (460 nm) state into a neutral (390 nm) state as the Aβ binds, allowing a ratiometric detection of Aβ. The utility of the developed GFP was shown by the efficient and specific detection of an Aβ and the tracking of its conformational change and localization in astrocytes.
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Affiliation(s)
- Tae-Geun Yu
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jinsu Lee
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jungmin Yoon
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jung Min Choi
- School
of Food Biotechnology and Nutrition, Kyungsung
University, Busan 48434, Korea
| | - Dong-Gun Kim
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Won Do Heo
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Ji-Joon Song
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Hak-Sung Kim
- Departement
of Biological Sciences, Korea Advanced Institute
of Science and Technology (KAIST), Daejeon 34141, Korea
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22
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Biggs BW, de Paz AM, Bhan NJ, Cybulski TR, Church GM, Tyo KEJ. Engineering Ca 2+-Dependent DNA Polymerase Activity. ACS Synth Biol 2023; 12:3301-3311. [PMID: 37856140 DOI: 10.1021/acssynbio.3c00302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.
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Affiliation(s)
- Bradley W Biggs
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Alexandra M de Paz
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Namita J Bhan
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Thaddeus R Cybulski
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, Illinois 60611, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
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23
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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24
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Padhi AK, Kalita P, Maurya S, Poluri KM, Tripathi T. From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics. J Phys Chem B 2023; 127:8717-8735. [PMID: 37815479 DOI: 10.1021/acs.jpcb.3c04542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".
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Affiliation(s)
- Aditya K Padhi
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Parismita Kalita
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
| | - Shweata Maurya
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Krishna Mohan Poluri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
- Department of Zoology, School of Life Sciences, North-Eastern Hill University, Shillong 793022, India
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25
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Zhang JZ, Ong SE, Baker D, Maly DJ. Single-cell signaling analysis reveals that Major Vault Protein facilitates RasG12C inhibitor resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560617. [PMID: 37873412 PMCID: PMC10592919 DOI: 10.1101/2023.10.02.560617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Recently developed covalent inhibitors for RasG12C provide the first pharmacological tools to target mutant Ras-driven cancers. However, the rapid development of resistance to current clinical Ras G12C inhibitors is common. Presumably, a subpopulation of RasG12C-expressing cells adapt their signaling to evade these inhibitors and the mechanisms for this phenomenon are unclear due to the lack of tools that can measure signaling with single-cell resolution. Here, we utilized recently developed Ras sensors to profile the environment of active Ras and to measure the activity of endogenous Ras in order to pair structure (Ras signalosome) to function (Ras activity), respectively, at a single-cell level. With this approach, we identified a subpopulation of KRasG12C cells treated with RasG12C-GDP inhibitors underwent oncogenic signaling and metabolic changes driven by WT Ras at the golgi and mutant Ras at the mitochondria, respectively. Our Ras sensors identified Major Vault Protein (MVP) as a mediator of Ras activation at both compartments by scaffolding Ras signaling pathway components and metabolite channels. We found that recently developed RasG12C-GTP inhibitors also led to MVP-mediated WT Ras signaling at the golgi, demonstrating that this a general mechanism RasG12C inhibitor resistance. Overall, single-cell analysis of structure-function relationships enabled the discovery of a RasG12C inhibitor-resistant subpopulation driven by MVP, providing insight into the complex and heterogenous rewiring occurring during drug resistance in cancer.
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Affiliation(s)
- Jason Z. Zhang
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
| | - Shao-En Ong
- Department of Pharmacology, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
| | - Dustin J. Maly
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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26
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Yu Y, Rué Casamajo A, Finnigan W, Schnepel C, Barker R, Morrill C, Heath RS, De Maria L, Turner NJ, Scrutton NS. Structure-Based Design of Small Imine Reductase Panels for Target Substrates. ACS Catal 2023; 13:12310-12321. [PMID: 37736118 PMCID: PMC10510103 DOI: 10.1021/acscatal.3c02278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/20/2023] [Indexed: 09/23/2023]
Abstract
Biocatalysis is important in the discovery, development, and manufacture of pharmaceuticals. However, the identification of enzymes for target transformations of interest requires major screening efforts. Here, we report a structure-based computational workflow to prioritize protein sequences by a score based on predicted activities on substrates, thereby reducing a resource-intensive laboratory-based biocatalyst screening. We selected imine reductases (IREDs) as a class of biocatalysts to illustrate the application of the computational workflow termed IREDFisher. Validation by using published data showed that IREDFisher can retrieve the best enzymes and increase the hit rate by identifying the top 20 ranked sequences. The power of IREDFisher is confirmed by computationally screening 1400 sequences for chosen reductive amination reactions with different levels of complexity. Highly active IREDs were identified by only testing 20 samples in vitro. Our speed test shows that it only takes 90 min to rank 85 sequences from user input and 30 min for the established IREDFisher database containing 591 IRED sequences. IREDFisher is available as a user-friendly web interface (https://enzymeevolver.com/IREDFisher). IREDFisher enables the rapid discovery of IREDs for applications in synthesis and directed evolution studies, with minimal time and resource expenditure. Future use of the workflow with other enzyme families could be implemented following the modification of the workflow scoring function.
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Affiliation(s)
- Yuqi Yu
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
- Augmented
Biologics Discovery & Design, Department of Biologics Engineering, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB21 6GH, U.K.
| | - Arnau Rué Casamajo
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - William Finnigan
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Christian Schnepel
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Rhys Barker
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Charlotte Morrill
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Rachel S. Heath
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Leonardo De Maria
- Medicinal
Chemistry, Research and Early Development, Respiratory and Immunology
(RI), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43150, Sweden
| | - Nicholas J. Turner
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
| | - Nigel S. Scrutton
- Department
of Chemistry, The University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, U.K.
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27
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Praetorius F, Leung PJY, Tessmer MH, Broerman A, Demakis C, Dishman AF, Pillai A, Idris A, Juergens D, Dauparas J, Li X, Levine PM, Lamb M, Ballard RK, Gerben SR, Nguyen H, Kang A, Sankaran B, Bera AK, Volkman BF, Nivala J, Stoll S, Baker D. Design of stimulus-responsive two-state hinge proteins. Science 2023; 381:754-760. [PMID: 37590357 PMCID: PMC10697137 DOI: 10.1126/science.adg7731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023]
Abstract
In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.
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Affiliation(s)
- Florian Praetorius
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Philip J. Y. Leung
- 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
| | - Maxx H. Tessmer
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - Adam Broerman
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Cullen Demakis
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, Washington, USA
| | - Acacia F. Dishman
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
- Medical Scientist Training Program, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Arvind Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Abbas Idris
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, 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
| | - Xinting Li
- 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
| | - Mila Lamb
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ryanne K. Ballard
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R. Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannah Nguyen
- 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
| | - Banumathi Sankaran
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian F. Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA
| | - Stefan Stoll
- Department of Chemistry, University of Washington, Seattle, WA, 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
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28
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Lee H, Xie T, Yu X, Schaffter SW, Schulman R. Plug-and-play protein biosensors using aptamer-regulated in vitro transcription. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.10.552680. [PMID: 37645783 PMCID: PMC10461910 DOI: 10.1101/2023.08.10.552680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors or digital protein biosensors with detection ranges that can be tuned over two orders of magnitude. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors suitable for diverse research and diagnostic applications.
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Affiliation(s)
- Heonjoon Lee
- Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21218
| | - Tian Xie
- Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Xinjie Yu
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | | | - Rebecca Schulman
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
- Computer Science, Johns Hopkins University, Baltimore, MD 21218
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29
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Cheng W, Nian B. Computer-Aided Lipase Engineering for Improving Their Stability and Activity in the Food Industry: State of the Art. Molecules 2023; 28:5848. [PMID: 37570817 PMCID: PMC10421223 DOI: 10.3390/molecules28155848] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
As some of the most widely used biocatalysts, lipases have exhibited extreme advantages in many processes, such as esterification, amidation, and transesterification reactions, which causes them to be widely used in food industrial production. However, natural lipases have drawbacks in terms of organic solvent resistance, thermostability, selectivity, etc., which limits some of their applications in the field of foods. In this systematic review, the application of lipases in various food processes was summarized. Moreover, the general structure of lipases is discussed in-depth, and the engineering strategies that can be used in lipase engineering are also summarized. The protocols of some classical methods are compared and discussed, which can provide some information about how to choose methods of lipase engineering. Thermostability engineering and solvent tolerance engineering are highlighted in this review, and the basic principles for improving thermostability and solvent tolerance are summarized. In the future, comput er-aided technology should be more emphasized in the investigation of the mechanisms of reactions catalyzed by lipases and guide the engineering of lipases. The engineering of lipase tunnels to improve the diffusion of substrates is also a promising prospect for further enhanced lipase activity and selectivity.
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Affiliation(s)
| | - Binbin Nian
- State Key Laboratory of Materials-Oriented Chemical Engineering, School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 210009, China;
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30
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Minami S, Kobayashi N, Sugiki T, Nagashima T, Fujiwara T, Tatsumi-Koga R, Chikenji G, Koga N. Exploration of novel αβ-protein folds through de novo design. Nat Struct Mol Biol 2023; 30:1132-1140. [PMID: 37400653 PMCID: PMC10442233 DOI: 10.1038/s41594-023-01029-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
A fundamental question in protein evolution is whether nature has exhaustively sampled nearly all possible protein folds throughout evolution, or whether a large fraction of the possible folds remains unexplored. To address this question, we defined a set of rules for β-sheet topology to predict novel αβ-folds and carried out a systematic de novo protein design exploration of the novel αβ-folds predicted by the rules. The designs for all eight of the predicted novel αβ-folds with a four-stranded β-sheet, including a knot-forming one, folded into structures close to the design models. Further, the rules predicted more than 10,000 novel αβ-folds with five- to eight-stranded β-sheets; this number far exceeds the number of αβ-folds observed in nature so far. This result suggests that a vast number of αβ-folds are possible, but have not emerged or have become extinct due to evolutionary bias.
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Affiliation(s)
- Shintaro Minami
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
| | - Naohiro Kobayashi
- Institute for Protein Research (IPR), Osaka University, Osaka, Japan
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Toshihiko Sugiki
- Institute for Protein Research (IPR), Osaka University, Osaka, Japan
| | - Toshio Nagashima
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | | | - Rie Tatsumi-Koga
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
| | - George Chikenji
- Department of Applied Physics, Graduate School of Engineering, Nagoya University, Nagoya, Japan
| | - Nobuyasu Koga
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- SOKENDAI, The Graduate University for Advanced Studies, Hayama, Japan.
- Research Center of Integrative Molecular Systems, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Laboratory for Protein Design, Institute for Protein Research (IPR), Osaka University, Osaka, Japan.
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31
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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32
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McLamore ES, Datta SPA. A Connected World: System-Level Support Through Biosensors. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:285-309. [PMID: 37018797 DOI: 10.1146/annurev-anchem-100322-040914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.
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Affiliation(s)
- Eric S McLamore
- Department of Agricultural Sciences, Clemson University, Clemson, South Carolina, USA;
- Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina, USA
| | - Shoumen P A Datta
- MIT Auto-ID Labs, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Medical Device (MDPnP) Interoperability and Cybersecurity Labs, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, Cambridge, Massachusetts, USA
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33
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Jefferson RE, Oggier A, Füglistaler A, Camviel N, Hijazi M, Villarreal AR, Arber C, Barth P. Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis. Nat Commun 2023; 14:2875. [PMID: 37208363 DOI: 10.1038/s41467-023-38491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/04/2023] [Indexed: 05/21/2023] Open
Abstract
Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.
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Affiliation(s)
- Robert E Jefferson
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
| | - Aurélien Oggier
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
| | - Andreas Füglistaler
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
| | - Nicolas Camviel
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
- Department of Oncology UNIL-CHUV, University Hospital Lausanne (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Mahdi Hijazi
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
| | - Ana Rico Villarreal
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
| | - Caroline Arber
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland
- Department of Oncology UNIL-CHUV, University Hospital Lausanne (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patrick Barth
- Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland.
- Ludwig Institute for Cancer Research Lausanne, Lausanne, Switzerland.
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34
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Yang LF, Ling M, Kacherovsky N, Pun SH. Aptamers 101: aptamer discovery and in vitro applications in biosensors and separations. Chem Sci 2023; 14:4961-4978. [PMID: 37206388 PMCID: PMC10189874 DOI: 10.1039/d3sc00439b] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/14/2023] [Indexed: 05/21/2023] Open
Abstract
Aptamers are single-stranded nucleic acids that bind and recognize targets much like antibodies. Recently, aptamers have garnered increased interest due to their unique properties, including inexpensive production, simple chemical modification, and long-term stability. At the same time, aptamers possess similar binding affinity and specificity as their protein counterpart. In this review, we discuss the aptamer discovery process as well as aptamer applications to biosensors and separations. In the discovery section, we describe the major steps of the library selection process for aptamers, called systematic evolution of ligands by exponential enrichment (SELEX). We highlight common approaches and emerging strategies in SELEX, from starting library selection to aptamer-target binding characterization. In the applications section, we first evaluate recently developed aptamer biosensors for SARS-CoV-2 virus detection, including electrochemical aptamer-based sensors and lateral flow assays. Then we discuss aptamer-based separations for partitioning different molecules or cell types, especially for purifying T cell subsets for therapeutic applications. Overall, aptamers are promising biomolecular tools and the aptamer field is primed for expansion in biosensing and cell separation.
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Affiliation(s)
- Lucy F Yang
- Department of Bioengineering and Molecular Engineering and Sciences Institute, University of Washington Seattle Washington USA
| | - Melissa Ling
- Department of Bioengineering and Molecular Engineering and Sciences Institute, University of Washington Seattle Washington USA
| | - Nataly Kacherovsky
- Department of Bioengineering and Molecular Engineering and Sciences Institute, University of Washington Seattle Washington USA
| | - Suzie H Pun
- Department of Bioengineering and Molecular Engineering and Sciences Institute, University of Washington Seattle Washington USA
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35
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Engelberger F, Zakary JD, Künze G. Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application. Front Mol Biosci 2023; 10:1178035. [PMID: 37228581 PMCID: PMC10204868 DOI: 10.3389/fmolb.2023.1178035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.
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36
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Hederman AP, Ackerman ME. Leveraging deep learning to improve vaccine design. Trends Immunol 2023; 44:333-344. [PMID: 37003949 PMCID: PMC10485910 DOI: 10.1016/j.it.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/05/2023] [Accepted: 03/05/2023] [Indexed: 04/03/2023]
Abstract
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.
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Affiliation(s)
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA; Department of Microbiology and Immunology, Geisel School of Medicine, Hanover, NH, USA.
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37
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Yang K, Lu ZJ, Zhu TY, Wang JX, Yang FQ, Abbas SZ, Zhou J, Yang Z, Mi JL, Ravi SK, Yong YC. Tailoring the whole-cell sensing spectrum with cyborgian redox machinery. Anal Chim Acta 2023; 1252:341046. [PMID: 36935150 DOI: 10.1016/j.aca.2023.341046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/18/2023] [Accepted: 03/03/2023] [Indexed: 03/06/2023]
Abstract
Whole-cell biosensors are an important class of analytical tools that offer the advantages of low cost, facile operation, and unique reproduction/regeneration ability. However, it has always been quite challenging to expand the sensing spectrum of the host. Here, a new approach to extend the cell sensing spectrum with biomineralized nanoparticles is developed. The nano-biohybrid design comprise biomineralized FeS nanoparticles firmly anchored onto the bacterium, Shewanella oneidensis MR-1, wherein the nanoparticles are wired to the cellular electron transfer machinery (MtrCAB/OmcA) of the bacterium, forming an artificial cyborgian redox machinery consisting of FeS-MtrCAB/OmcA-FccA. Strikingly, with this cyborgian redox machinery, the sensing spectrum of FeS hybridized S. oneidensis MR-1 cell is successfully expanded to enable whole-cell electrochemical detection of Vitamin B12, while an unhybridized native cell is incapable of sensing. This proof-of-concept nano-biohybrid design offers a new perspective on manipulating the microbial toolkit for an expanded sensing spectrum in whole-cell biosensors.
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Affiliation(s)
- Kai Yang
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Zi-Jie Lu
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Tian-Yu Zhu
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Jing-Xian Wang
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Fu-Qiao Yang
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Syed Zaghum Abbas
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China
| | - Jun Zhou
- Bioenergy Research Institute, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu Province, China
| | - Zhugen Yang
- School of Water, Energy and Environment, Cranfield University, Milton Keynes, MK43 0AL, United Kingdom
| | - Jian-Li Mi
- Institute for Advanced Materials, School of Materials Science and Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu Province, China
| | - Sai Kishore Ravi
- School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region of China
| | - Yang-Chun Yong
- Biofuels Institute, Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, School of Emergency Management & School of Environment and Safety Engineering, Zhenjiang, 212013, Jiangsu Province, China.
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38
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Lostao A, Lim K, Pallarés MC, Ptak A, Marcuello C. Recent advances in sensing the inter-biomolecular interactions at the nanoscale - A comprehensive review of AFM-based force spectroscopy. Int J Biol Macromol 2023; 238:124089. [PMID: 36948336 DOI: 10.1016/j.ijbiomac.2023.124089] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/13/2023] [Accepted: 03/15/2023] [Indexed: 03/24/2023]
Abstract
Biomolecular interactions underpin most processes inside the cell. Hence, a precise and quantitative understanding of molecular association and dissociation events is crucial, not only from a fundamental perspective, but also for the rational design of biomolecular platforms for state-of-the-art biomedical and industrial applications. In this context, atomic force microscopy (AFM) appears as an invaluable experimental technique, allowing the measurement of the mechanical strength of biomolecular complexes to provide a quantitative characterization of their interaction properties from a single molecule perspective. In the present review, the most recent methodological advances in this field are presented with special focus on bioconjugation, immobilization and AFM tip functionalization, dynamic force spectroscopy measurements, molecular recognition imaging and theoretical modeling. We expect this work to significantly aid in grasping the principles of AFM-based force spectroscopy (AFM-FS) technique and provide the necessary tools to acquaint the type of data that can be achieved from this type of experiments. Furthermore, a critical assessment is done with other nanotechnology techniques to better visualize the future prospects of AFM-FS.
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Affiliation(s)
- Anabel Lostao
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza 50009, Spain; Laboratorio de Microscopias Avanzadas (LMA), Universidad de Zaragoza, Zaragoza 50018, Spain; Fundación ARAID, Aragón, Spain.
| | - KeeSiang Lim
- WPI-Nano Life Science Institute, Kanazawa University, Ishikawa 920-1192, Japan
| | - María Carmen Pallarés
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza 50009, Spain; Laboratorio de Microscopias Avanzadas (LMA), Universidad de Zaragoza, Zaragoza 50018, Spain
| | - Arkadiusz Ptak
- Institute of Physics, Faculty of Materials Engineering and Technical Physics, Poznan University of Technology, Poznan 60-925, Poland
| | - Carlos Marcuello
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza 50009, Spain; Laboratorio de Microscopias Avanzadas (LMA), Universidad de Zaragoza, Zaragoza 50018, Spain.
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39
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Ji J, Hossain MS, Krueger EN, Zhang Z, Nangia S, Carpentier B, Martel M, Nangia S, Mozhdehi D. Lipidation Alters the Structure and Hydration of Myristoylated Intrinsically Disordered Proteins. Biomacromolecules 2023; 24:1244-1257. [PMID: 36757021 PMCID: PMC10017028 DOI: 10.1021/acs.biomac.2c01309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/25/2023] [Indexed: 02/10/2023]
Abstract
Lipidated proteins are an emerging class of hybrid biomaterials that can integrate the functional capabilities of proteins into precisely engineered nano-biomaterials with potential applications in biotechnology, nanoscience, and biomedical engineering. For instance, fatty-acid-modified elastin-like polypeptides (FAMEs) combine the hierarchical assembly of lipids with the thermoresponsive character of elastin-like polypeptides (ELPs) to form nanocarriers with emergent temperature-dependent structural (shape or size) characteristics. Here, we report the biophysical underpinnings of thermoresponsive behavior of FAMEs using computational nanoscopy, spectroscopy, scattering, and microscopy. This integrated approach revealed that temperature and molecular syntax alter the structure, contact, and hydration of lipid, lipidation site, and protein, aligning with the changes in the nanomorphology of FAMEs. These findings enable a better understanding of the biophysical consequence of lipidation in biology and the rational design of the biomaterials and therapeutics that rival the exquisite hierarchy and capabilities of biological systems.
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Affiliation(s)
- Jingjing Ji
- Department
of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States
| | - Md Shahadat Hossain
- Department
of Chemistry, Syracuse University, Syracuse, New York 13244, United States
| | - Emily N. Krueger
- Department
of Chemistry, Syracuse University, Syracuse, New York 13244, United States
| | - Zhe Zhang
- Department
of Chemistry, Syracuse University, Syracuse, New York 13244, United States
| | - Shivangi Nangia
- Department
of Chemistry, University of Hartford, West Hartford, Connecticut 06117, United States
| | - Britnie Carpentier
- Department
of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States
| | - Mae Martel
- Department
of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States
| | - Shikha Nangia
- Department
of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States
- BioInspired
Syracuse: Institute for Material and Living Systems, Syracuse University, Syracuse, New York 13244, United States
| | - Davoud Mozhdehi
- Department
of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States
- Department
of Chemistry, Syracuse University, Syracuse, New York 13244, United States
- BioInspired
Syracuse: Institute for Material and Living Systems, Syracuse University, Syracuse, New York 13244, United States
- Department
of Biology, Syracuse University, Syracuse, New York 13244, United States
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40
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Chen Z. Protein circuit design using de novo proteins. Trends Biotechnol 2023; 41:593-594. [PMID: 36906493 DOI: 10.1016/j.tibtech.2023.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/12/2023]
Abstract
Protein-based biological circuits enable customized control of cellular functions, and de novo protein design enables circuit functionalities that are not possible by repurposing natural proteins. Here, I highlight recent progress in protein circuit design, including CHOMP, developed by Gao et al., and SPOC, developed by Fink et al.
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Affiliation(s)
- Zibo Chen
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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41
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d'Amone L, Matzeu G, Quijano-Rubio A, Callahan GP, Napier B, Baker D, Omenetto FG. Reshaping de Novo Protein Switches into Bioresponsive Materials for Biomarker, Toxin, and Viral Detection. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208556. [PMID: 36493355 DOI: 10.1002/adma.202208556] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/14/2022] [Indexed: 06/17/2023]
Abstract
De novo designed protein switches are powerful tools to specifically and sensitively detect diverse targets with simple chemiluminescent readouts. Finding an appropriate material host for de novo designed protein switches without altering their thermodynamics while preserving their intrinsic stability over time would enable the development of a variety of sensing formats to monitor exposure to pathogens, toxins, and for disease diagnosis. Here, a de novo protein-biopolymer hybrid that maintains the detection capabilities induced by the conformational change of the incorporated proteins in response to analytes of interest is generated in multiple, shelf-stable material formats without the need of refrigerated storage conditions. A set of functional demonstrator devices including personal protective equipment such as masks and laboratory gloves, free-standing films, air quality monitors, and wearable devices is presented to illustrate the versatility of the approach. Such formats are designed to be responsive to human epidermal growth factor receptor (HER2), anti-hepatitis B (HBV) antibodies, Botulinum neurotoxin B (BoNT/B), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This combination of form and function offers wide opportunities for ubiquitous sensing in multiple environments by enabling a large class of bio-responsive interfaces of broad utility.
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Affiliation(s)
- Luciana d'Amone
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - Giusy Matzeu
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - Alfredo Quijano-Rubio
- Department of Biochemistry, Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
| | - Gregory P Callahan
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - Bradley Napier
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - David Baker
- Department of Biochemistry, Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Fiorenzo G Omenetto
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
- Department of Physics, Tufts University, Medford, MA, 02155, USA
- Laboratory for Living Devices, Tufts University, Medford, MA, 02155, USA
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, 02155, USA
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42
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Differential sensing with arrays of de novo designed peptide assemblies. Nat Commun 2023; 14:383. [PMID: 36693847 PMCID: PMC9873944 DOI: 10.1038/s41467-023-36024-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023] Open
Abstract
Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.
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43
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Yang KK, Zanichelli N, Yeh H. Masked inverse folding with sequence transfer for protein representation learning. Protein Eng Des Sel 2023; 36:gzad015. [PMID: 37883472 DOI: 10.1093/protein/gzad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
Self-supervised pretraining on protein sequences has led to state-of-the art performance on protein function and fitness prediction. However, sequence-only methods ignore the rich information contained in experimental and predicted protein structures. Meanwhile, inverse folding methods reconstruct a protein's amino-acid sequence given its structure, but do not take advantage of sequences that do not have known structures. In this study, we train a masked inverse folding protein masked language model parameterized as a structured graph neural network. During pretraining, this model learns to reconstruct corrupted sequences conditioned on the backbone structure. We then show that using the outputs from a pretrained sequence-only protein masked language model as input to the inverse folding model further improves pretraining perplexity. We evaluate both of these models on downstream protein engineering tasks and analyze the effect of using information from experimental or predicted structures on performance.
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Affiliation(s)
- Kevin K Yang
- Microsoft Research, 1 Memorial Drive, Cambridge, MA, USA
| | | | - Hugh Yeh
- Pritzker School of Medicine, University of Chicago, 924 E 57th Street, Chicago, IL, USA
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44
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Lu H, Cheng Z, Hu Y, Tang LV. What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders? BIOLOGY 2023; 12:biology12020166. [PMID: 36829445 PMCID: PMC9952452 DOI: 10.3390/biology12020166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.
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Zhu B, Qian C, Tang H, Kitaguchi T, Ueda H. Creating a Thermostable β-Glucuronidase Switch for Homogeneous Immunoassay by Disruption of Conserved Salt Bridges at Diagonal Interfaces. Biochemistry 2023; 62:309-317. [PMID: 35849118 DOI: 10.1021/acs.biochem.2c00165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Escherichia coli β-glucuronidase (GUS) has been used as a reporter enzyme in molecular biology and engineered as an enzyme switch for the development of homogeneous biosensors. In this study, we developed a thermostable GUS enzyme switch based on the thermostable GUS mutant TR3337 by disrupting a conserved salt bridge (H514-E523) between the diagonal subunits of its homotetramer. A combinatorial library (240 variants) was screened using a novel high-throughput strategy, which led to the identification of mutant DLW (H514D/M516L/Y517W) as a functional enzyme switch in a caffeine-recognizing immunosensor. Molecular dynamics simulations were performed to predict the topology change around position 514, and a side-chain flip of D514 (repulsion with E523) was observed in the DLW mutant. Up to 1.8-fold of signal-to-background ratio was confirmed when measured at up to 45 °C, thereby highlighting the DLW mutant as a versatile tool for developing thermostable immunosensors for in vitro and in cellulo applications.
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Affiliation(s)
- Bo Zhu
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Cheng Qian
- Graduate School of Life Science and Technology, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Haoxuan Tang
- Graduate School of Life Science and Technology, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Tetsuya Kitaguchi
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Hiroshi Ueda
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
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Van Thillo T, Van Deuren V, Dedecker P. Smart genetically-encoded biosensors for the chemical monitoring of living systems. Chem Commun (Camb) 2023; 59:520-534. [PMID: 36519509 DOI: 10.1039/d2cc05363b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Genetically-encoded biosensors provide the all-optical and non-invasive visualization of dynamic biochemical events within living systems, which has allowed the discovery of profound new insights. Twenty-five years of biosensor development has steadily improved their performance and has provided us with an ever increasing biosensor repertoire. In this feature article, we present recent advances made in biosensor development and provide a perspective on the future direction of the field.
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Affiliation(s)
- Toon Van Thillo
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
| | - Vincent Van Deuren
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
| | - Peter Dedecker
- Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Leuven, Belgium.
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Moussa S, Kilgour M, Jans C, Hernandez-Garcia A, Cuperlovic-Culf M, Bengio Y, Simine L. Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning. J Phys Chem B 2023; 127:62-68. [PMID: 36574492 DOI: 10.1021/acs.jpcb.2c05660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.
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Affiliation(s)
- Siba Moussa
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QuebecH3A 0B8, Canada
| | - Michael Kilgour
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QuebecH3A 0B8, Canada
| | - Clara Jans
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QuebecH3A 0B8, Canada
| | - Alex Hernandez-Garcia
- Montreal Institute for Learning Algorithms, 6666 St. Urbain, #200, Montreal, QuebecH2S 3H1, Canada
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Centre, National Research Council of Canada, 1200 Montreal Road, Ottawa, OntarioK1A 0R6, Canada
| | - Yoshua Bengio
- Montreal Institute for Learning Algorithms, 6666 St. Urbain, #200, Montreal, QuebecH2S 3H1, Canada
| | - Lena Simine
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QuebecH3A 0B8, Canada
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Patel SK, Surve J, Parmar J, Ahmed K, Bui FM, Al-Zahrani FA. Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses. IEEE Rev Biomed Eng 2023; 16:22-37. [PMID: 36197867 PMCID: PMC10009816 DOI: 10.1109/rbme.2022.3212038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/28/2022] [Accepted: 09/23/2022] [Indexed: 11/06/2022]
Abstract
This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.
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Affiliation(s)
- Shobhit K. Patel
- Department of Computer EngineeringMarwadi UniversityRajkot360003India
| | - Jaymit Surve
- Department of Electrical EngineeringMarwadi UniversityRajkot360003India
| | - Juveriya Parmar
- Department of Mechanical and Materials EngineeringUniversity of Nebraska - LincolnNebraska68588USA
- Department of Electronics and Communication EngineeringMarwadi UniversityRajkot360003India
| | - Kawsar Ahmed
- Department of Electrical and Computer EngineeringUniversity of SaskatchewanSaskatoonSKS79 5A9Canada
- Group of Bio-PhotomatiX, Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Francis M. Bui
- Department of Electrical and Computer EngineeringUniversity of SaskatchewanSaskatoonSKS79 5A9Canada
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Wang D, Chen Y, Xiang S, Hu H, Zhan Y, Yu Y, Zhang J, Wu P, Liu FY, Kai T, Ding P. Recent advances in immunoassay technologies for the detection of human coronavirus infections. Front Cell Infect Microbiol 2023; 12:1040248. [PMID: 36683684 PMCID: PMC9845787 DOI: 10.3389/fcimb.2022.1040248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/30/2022] [Indexed: 01/05/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the seventh coronavirus (CoV) that has spread in humans and has become a global pandemic since late 2019. Efficient and accurate laboratory diagnostic methods are one of the crucial means to control the development of the current pandemic and to prevent potential future outbreaks. Although real-time reverse transcription-polymerase chain reaction (rRT-PCR) is the preferred laboratory method recommended by the World Health Organization (WHO) for diagnosing and screening SARS-CoV-2 infection, the versatile immunoassays still play an important role for pandemic control. They can be used not only as supplemental tools to identify cases missed by rRT-PCR, but also for first-line screening tests in areas with limited medical resources. Moreover, they are also indispensable tools for retrospective epidemiological surveys and the evaluation of the effectiveness of vaccination. In this review, we summarize the mainstream immunoassay methods for human coronaviruses (HCoVs) and address their benefits, limitations, and applications. Then, technical strategies based on bioinformatics and advanced biosensors were proposed to improve the performance of these methods. Finally, future suggestions and possibilities that can lead to higher sensitivity and specificity are provided for further research.
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Affiliation(s)
- Danqi Wang
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Yuejun Chen
- Breast Surgery Department I, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Shan Xiang
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Huiting Hu
- Breast Surgery Department I, Hunan Cancer Hospital, Changsha, Hunan, China
| | - Yujuan Zhan
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Ying Yu
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Jingwen Zhang
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Pian Wu
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Fei Yue Liu
- Department of Economics and Management, ChangSha University, Changsha, Hunan, China
| | - Tianhan Kai
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
| | - Ping Ding
- Xiang Ya School of Public Health, Central South University, Changsha, Hunan, China
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Jeong KJ, Jeong S, Lee S, Son CY. Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204272. [PMID: 36373701 DOI: 10.1002/adma.202204272] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/05/2022] [Indexed: 06/16/2023]
Abstract
Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode-electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.
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Affiliation(s)
- Kyeong-Jun Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Seungwon Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Sangmin Lee
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Chang Yun Son
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
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