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Jeung K, Kim M, Jang E, Shon YJ, Jung GY. Cell-free systems: A synthetic biology tool for rapid prototyping in metabolic engineering. Biotechnol Adv 2025; 79:108522. [PMID: 39863189 DOI: 10.1016/j.biotechadv.2025.108522] [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: 08/20/2024] [Revised: 01/16/2025] [Accepted: 01/17/2025] [Indexed: 01/27/2025]
Abstract
Microbial cell factories provide sustainable alternatives to petroleum-based chemical production using cost-effective substrates. A deep understanding of their metabolism is essential to harness their potential along with continuous efforts to improve productivity and yield. However, the construction and evaluation of numerous genetic variants are time-consuming and labor-intensive. Cell-free systems (CFSs) serve as powerful platforms for rapid prototyping of genetic circuits, metabolic pathways, and enzyme functionality. They offer numerous advantages, including minimizing unwanted metabolic interference, precise control of reaction conditions, reduced labor, and shorter Design-Build-Test-Learn cycles. Additionally, the introduction of in vitro compartmentalization strategies in CFSs enables ultra-high-throughput screening in physically separated spaces, which significantly enhances prototyping efficiency. This review highlights the latest examples of using CFS to overcome prototyping limitations in living cells with a focus on rapid prototyping, particularly regarding gene regulation, enzymes, and multienzymatic reactions in bacteria. Finally, this review evaluates CFSs as a versatile prototyping platform and discusses its future applications, emphasizing its potential for producing high-value chemicals through microbial biosynthesis.
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Affiliation(s)
- Kumyoung Jeung
- Division of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Minsun Kim
- Center for Bio-based Chemistry, Korea Research Institute of Chemical Technology (KRICT), 406-30, Jongga-Ro, Jung-Gu, Ulsan 44429, Republic of Korea
| | - Eunsoo Jang
- Division of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Yang Jun Shon
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea
| | - Gyoo Yeol Jung
- Division of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea; Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea.
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Bu F, Adam Y, Adamiak RW, Antczak M, de Aquino BRH, Badepally NG, Batey RT, Baulin EF, Boinski P, Boniecki MJ, Bujnicki JM, Carpenter KA, Chacon J, Chen SJ, Chiu W, Cordero P, Das NK, Das R, Dawson WK, DiMaio F, Ding F, Dock-Bregeon AC, Dokholyan NV, Dror RO, Dunin-Horkawicz S, Eismann S, Ennifar E, Esmaeeli R, Farsani MA, Ferré-D'Amaré AR, Geniesse C, Ghanim GE, Guzman HV, Hood IV, Huang L, Jain DS, Jaryani F, Jin L, Joshi A, Karelina M, Kieft JS, Kladwang W, Kmiecik S, Koirala D, Kollmann M, Kretsch RC, Kurciński M, Li J, Li S, Magnus M, Masquida B, Moafinejad SN, Mondal A, Mukherjee S, Nguyen THD, Nikolaev G, Nithin C, Nye G, Pandaranadar Jeyeram IPN, Perez A, Pham P, Piccirilli JA, Pilla SP, Pluta R, Poblete S, Ponce-Salvatierra A, Popenda M, Popenda L, Pucci F, Rangan R, Ray A, Ren A, Sarzynska J, Sha CM, Stefaniak F, Su Z, Suddala KC, Szachniuk M, Townshend R, Trachman RJ, Wang J, Wang W, Watkins A, Wirecki TK, Xiao Y, Xiong P, Xiong Y, Yang J, Yesselman JD, Zhang J, Zhang Y, Zhang Z, Zhou Y, Zok T, Zhang D, Zhang S, Żyła A, Westhof E, Miao Z. RNA-Puzzles Round V: blind predictions of 23 RNA structures. Nat Methods 2025; 22:399-411. [PMID: 39623050 PMCID: PMC11810798 DOI: 10.1038/s41592-024-02543-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 10/29/2024] [Indexed: 01/16/2025]
Abstract
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson-Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.
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Grants
- T32 GM066706 NIGMS NIH HHS
- NSFC T2225007 National Natural Science Foundation of China (National Science Foundation of China)
- R35 GM134919 NIGMS NIH HHS
- R35GM145409 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 GM145409 NIGMS NIH HHS
- 32270707 National Natural Science Foundation of China (National Science Foundation of China)
- R35 GM122579 NIGMS NIH HHS
- R35 GM134864 NIGMS NIH HHS
- T32 grant GM066706 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM121342 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21 CA219847 NCI NIH HHS
- 32171191 National Natural Science Foundation of China (National Science Foundation of China)
- P20 GM121342 NIGMS NIH HHS
- R35 GM152029 NIGMS NIH HHS
- R01 GM073850 NIGMS NIH HHS
- F32 GM112294 NIGMS NIH HHS
- ZIA DK075136 Intramural NIH HHS
- Z.M. is supported by Major Projects of Guangzhou National Laboratory, (Grant No. GZNL2023A01006, GZNL2024A01002, SRPG22-003, SRPG22-006, SRPG22-007, HWYQ23-003, YW-YFYJ0102), the National Key R&D Programs of China (2023YFF1204700, 2023YFF1204701, 2021YFF1200900, 2021YFF1200903). This work is part of the ITI 2021-2028 program and supported by IdEx Unistra (ANR-10-IDEX-0002 to E.W.), SFRI-STRAT’US project (ANR-20-SFRI-0012) and EUR IMCBio (IMCBio ANR-17-EURE-0023 to E.W.) under the framework of the French Investments for the Future Program.
- E.W. acknowledges also support from Wenzhou Institute, University of Chinese Academy of Sciences (WIUCASQD2024002).
- E.F.B. was additionally supported by European Molecular Biology Organization (EMBO) fellowship (ALTF 525-2022).
- Boniecki’s research was supported by the Polish National Science Center Poland (NCN) (grant 2016/23/B/ST6/03433 to Michal J. Boniecki). Predictions were performed using computational resources of the Interdisciplinary Centre for Mathematical and Computational Modelling of the University of Warsaw (ICM) (grant G66-9).
- J.M.B. is supported by the National Science Centre in Poland (NCN grants: 2017/26/A/NZ1/01083 to J.M.B., 2021/43/D/NZ1/03360 to S.M., 2020/39/B/NZ2/03127 to F.S., 2020/39/D/NZ2/02837 to T.K.W.). J.M.B. acknowledge Poland high-performance computing Infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, PCSS, CI TASK, WCSS) for providing computer facilities and support within the computational grant PLG/2023/016080.
- S.J.C. is supported by the National Institutes of Health under Grant R35-GM134919.
- R.D. is supported by Stanford Bio-X (to R.D., R.O.D., R.C.K., and S.E.); Stanford Gerald J. Lieberman Fellowship (to R.R.); the National Institutes of Health (R21 CA219847 and R35 GM122579 to R.D.), the Howard Hughes Medical Institute (HHMI, to R.D.); Consejo Nacional de Ciencia y Tecnología CONACyT Fellowship 312765 (P.C.); the Ruth L. Kirschstein National Research Service Award Postdoctoral Fellowships GM112294 (to J.D.Y.); National Science Foundation Graduate Research Fellowships (R.J.L.T. and R.R.); the National Library of Medicine T15 Training Grant (NLM T15007033 to K.A.C.); the U.S. Department of Energy, Office of Science Graduate Student Research program (R.J.L.T.).
- The National Institutes of Health grants 1R35 GM134864 and the Passan Foundation.
- R.O.D. is supported by the U.S. Department of Energy, Office of Science, Scientific Discovery through Advanced Computing (SciDAC) program (R.O.D.); Intel (R.O.D.).
- A.F.D. is supported, in part, by the intramural program of the National Heart, Lung and Blood Institute, National Institutes of Health, USA.
- Guangdong Science and Technology Department (2022A1515010328, 2023B1212060013, 2020B1212030004), Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23ptpy41).
- D.K. is supported by the NSF CAREER award MCB-2236996, and start-up, SURFF, and START awards from the University of Maryland Baltimore County to D.K.
- BM is supported by the Interdisciplinary Thematic Institute IMCBio, as part of the ITI 2021-2028 program at the University of Strasbourg, CNRS and Inserm, by IdEx Unistra (ANR-10-IDEX-0002), and EUR (IMCBio ANR-17-EUR-0023), under the framework of the French Investments Program for the Future.
- T.H.D.N. is supported by UKRI-Medical Research Council grant MC_UP_1201/19.
- C.N. and M.K. acknowledge funding from the National Science Centre, Poland [OPUS 2019/33/B/NZ2/02100]; S.P.P. acknowledges funding from the National Science Centre, Poland [OPUS 2020/39/B/NZ2/01301]; S.K. acknowledges funding from the National Science Centre, Poland [Sheng 2021/40/Q/NZ2/00078]; C.N. acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: PCSS, ACK Cyfronet AGH, CI TASK, WCSS) for providing computer facilities and support within the computational grants PLG/2022/016043, PLG/2022/015327 and PLG/2020/013424.
- AP is supported by an NSF-CAREER award CHE-2235785
- A.R. is supported by grants from the Natural Science Foundation of China (32325029, 32022039, 91940302, and 91640104), the National Key Research and Development Project of China (2021YFC2300300 and 2023YFC2604300).
- Marta Szachniuk are supported by the National Science Centre, Poland (2019/35/B/ST6/03074 to M.S.), the statutory funds of IBCH PAS and Poznan University of Technology.
- J.W. is supported by the Penn State College of Medicine’s Artificial Intelligence and Biomedical Informatics Program.
- J.Z. is supported by the Intramural Research Program of the NIH, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (ZIADK075136 to J.Z.), and an NIH Deputy Director for Intramural Research (DDIR) Challenge Award to J.Z.
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Affiliation(s)
- Fan Bu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yagoub Adam
- Inter-institutional Graduate Program on Bioinformatics, Department of Computer Science and Mathematics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Ryszard W Adamiak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Belisa Rebeca H de Aquino
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Nagendar Goud Badepally
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Robert T Batey
- Department of Biochemistry, University of Colorado at Boulder, Boulder, CO, USA
| | - Eugene F Baulin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Pawel Boinski
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Michal J Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Kristy A Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Jose Chacon
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Department of Cell and Developmental Biology, University of California San Diego, San Diego, CA, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Wah Chiu
- Department of Bioengineering and James H. Clark Center, Stanford University, Stanford, CA, USA
| | - Pablo Cordero
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Stripe, South San Francisco, CA, USA
| | - Naba Krishna Das
- Department of Chemistry and Biochemistry, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
- Biophysics program, Stanford University, Stanford, CA, USA
| | - Wayne K Dawson
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, SC, USA
| | - Anne-Catherine Dock-Bregeon
- Laboratory of Integrative Biology of Marine Models (LBI2M), Sorbonne University-CNRS UMR8227, Roscoff, France
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Structural Biology, Stanford University, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Stanisław Dunin-Horkawicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Stephan Eismann
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Atomic AI, South San Francisco, CA, USA
| | - Eric Ennifar
- Architecture et Réactivité de l'ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France
| | - Reza Esmaeeli
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Masoud Amiri Farsani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Adrian R Ferré-D'Amaré
- Laboratory of Nucleic Acids, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - George E Ghanim
- Medical Research Council Laboratory of Molecular Biology, Cambridge, UK
| | - Horacio V Guzman
- Instituto de Ciencia de Materials de Barcelona, ICMAB-CSIC, Bellaterra E-08193, Spain & Departamento de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid, Madrid, Spain
| | - Iris V Hood
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Lin Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University Guangzhou, Guangdong, China
| | - Dharm Skandh Jain
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Farhang Jaryani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Lei Jin
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Astha Joshi
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Masha Karelina
- Biophysics program, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jeffrey S Kieft
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver School of Medicine, Aurora, CO, USA
- New York Structural Biology Center, New York, NY, USA
| | - Wipapat Kladwang
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Sebastian Kmiecik
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Deepak Koirala
- Department of Chemistry and Biochemistry, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Markus Kollmann
- Department of Computer Science, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | | | - Mateusz Kurciński
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Shuang Li
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Marcin Magnus
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - BenoÎt Masquida
- UMR 7156, CNRS - Université de Strasbourg, IPCB, Strasbourg, France
| | - S Naeim Moafinejad
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | | | - Grigory Nikolaev
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Grace Nye
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Iswarya P N Pandaranadar Jeyeram
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Phillip Pham
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Joseph A Piccirilli
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA
- Department of Chemistry, The University of Chicago, Chicago, IL, USA
| | - Smita Priyadarshini Pilla
- Laboratory of Computational Biology, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland
| | - Radosław Pluta
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Simón Poblete
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile
- Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Lukasz Popenda
- NanoBioMedical Centre, Adam Mickiewicz University, Poznan, Poland
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
| | - Ramya Rangan
- Biophysics program, Stanford University, Stanford, CA, USA
- Atomic AI, South San Francisco, CA, USA
| | - Angana Ray
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Aiming Ren
- Life Sciences Institute, Zhejiang University, Hangzhou, China
| | - Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Congzhou Mike Sha
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, West China Hospital, Chengdu, China
| | - Krishna C Suddala
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Raphael Townshend
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Atomic AI, South San Francisco, CA, USA
| | - Robert J Trachman
- Laboratory of Nucleic Acids, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Wenkai Wang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Andrew Watkins
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Prescient Design, Genentech Research and Early Development, South San Francisco, CA, USA
| | - Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Yi Xiao
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Xiong
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
| | - Yiduo Xiong
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Joseph David Yesselman
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
- Department of Chemistry, University of Nebraska, Lincoln, NE, USA
| | - Jinwei Zhang
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Yi Zhang
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenzhen Zhang
- Department of Physics and Astronomy, Clemson University, Clemson, SC, USA
| | - Yuanzhe Zhou
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Dong Zhang
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Sicheng Zhang
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA
| | - Adriana Żyła
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France.
- Engineering Research Center of Clinical Functional Materials and Diagnosis & Treatment Devices of Zhejiang Province, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Cambridge, UK.
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Sha CM, Wang J, Dokholyan NV. Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks. Biophys J 2024; 123:2671-2681. [PMID: 37838833 PMCID: PMC11393712 DOI: 10.1016/j.bpj.2023.10.011] [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: 08/07/2023] [Revised: 09/19/2023] [Accepted: 10/12/2023] [Indexed: 10/16/2023] Open
Abstract
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.
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Affiliation(s)
- Congzhou M Sha
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Nikolay V Dokholyan
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Chemistry, Penn State University, State College, Pennsylvania; Department of Biomedical Engineering, Penn State University, State College, Pennsylvania.
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4
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Muscat S, Martino G, Manigrasso J, Marcia M, De Vivo M. On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules. J Chem Theory Comput 2024. [PMID: 39150960 DOI: 10.1021/acs.jctc.4c00773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
RNA molecules play a vital role in biological processes within the cell, with significant implications for science and medicine. Notably, the biological functions exerted by specific RNA molecules are often linked to the RNA conformational ensemble. However, the experimental characterization of such three-dimensional RNA structures is challenged by the structural heterogeneity of RNA and by its multiple dynamic interactions with binding partners such as small molecules, proteins, and metal ions. Consequently, our current understanding of the structure-function relationship of RNA molecules is still limited. In this context, we highlight molecular dynamics (MD) simulations as a powerful tool to complement experimental efforts on RNAs. Despite the recognized limitations of current force fields for RNA MD simulations, examining the dynamics of selected RNAs has provided valuable functional insights into their structures.
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Affiliation(s)
- Stefano Muscat
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Gianfranco Martino
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Jacopo Manigrasso
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden
| | - Marco Marcia
- European Molecular Biology Laboratory Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
- Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Marco De Vivo
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
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5
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Niu X, Xu Z, Zhang Y, Zuo X, Chen C, Fang X. Structural and dynamic mechanisms for coupled folding and tRNA recognition of a translational T-box riboswitch. Nat Commun 2023; 14:7394. [PMID: 37968328 PMCID: PMC10651847 DOI: 10.1038/s41467-023-43232-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/03/2023] [Indexed: 11/17/2023] Open
Abstract
T-box riboswitches are unique riboregulators where gene regulation is mediated through interactions between two highly structured RNAs. Despite extensive structural insights, how RNA-RNA interactions drive the folding and structural transitions of T-box to achieve functional conformations remains unclear. Here, by combining SAXS, single-molecule FRET and computational modeling, we elaborate the folding energy landscape of a translational T-box aptamer consisting of stems I, II and IIA/B, which Mg2+-induced global folding and tRNA binding are cooperatively coupled. smFRET measurements reveal that high Mg2+ stabilizes IIA/B and its stacking on II, which drives the pre-docking of I and II into a competent conformation, subsequent tRNA binding promotes docking of I and II to form a high-affinity tRNA binding groove, of which the essentiality of IIA/B and S-turn in II is substantiated with mutational analysis. We highlight a delicate balance among Mg2+, the intra- and intermolecular RNA-RNA interactions in modulating RNA folding and function.
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Affiliation(s)
- Xiaolin Niu
- Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Zhonghe Xu
- Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yufan Zhang
- Key Laboratory of RNA Science and Engineering, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaobing Zuo
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Chunlai Chen
- Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- State Key Laboratory of Membrane Biology, Tsinghua University, Beijing, 100084, China.
| | - Xianyang Fang
- Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- Key Laboratory of RNA Science and Engineering, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
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6
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Xu J, Hou J, Ding M, Wang Z, Chen T. Riboswitches, from cognition to transformation. Synth Syst Biotechnol 2023; 8:357-370. [PMID: 37325181 PMCID: PMC10265488 DOI: 10.1016/j.synbio.2023.05.008] [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: 03/01/2023] [Revised: 05/20/2023] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
Riboswitches are functional RNA elements that regulate gene expression by directly detecting metabolites. Twenty years have passed since it was first discovered, researches on riboswitches are becoming increasingly standardized and refined, which could significantly promote people's cognition of RNA function as well. Here, we focus on some representative orphan riboswitches, enumerate the structural and functional transformation and artificial design of riboswitches including the coupling with ribozymes, hoping to attain a comprehensive understanding of riboswitch research.
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Affiliation(s)
- Jingdong Xu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Junyuan Hou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Mengnan Ding
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Zhiwen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Tao Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
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7
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Deng J, Fang X, Huang L, Li S, Xu L, Ye K, Zhang J, Zhang K, Zhang QC. RNA structure determination: From 2D to 3D. FUNDAMENTAL RESEARCH 2023; 3:727-737. [PMID: 38933295 PMCID: PMC11197651 DOI: 10.1016/j.fmre.2023.06.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2024] Open
Abstract
RNA molecules serve a wide range of functions that are closely linked to their structures. The basic structural units of RNA consist of single- and double-stranded regions. In order to carry out advanced functions such as catalysis and ligand binding, certain types of RNAs can adopt higher-order structures. The analysis of RNA structures has progressed alongside advancements in structural biology techniques, but it comes with its own set of challenges and corresponding solutions. In this review, we will discuss recent advances in RNA structure analysis techniques, including structural probing methods, X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and small-angle X-ray scattering. Often, a combination of multiple techniques is employed for the integrated analysis of RNA structures. We also survey important RNA structures that have been recently determined using various techniques.
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Affiliation(s)
- Jie Deng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Xianyang Fang
- Beijing Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Key Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lin Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Shanshan Li
- MOE Key Laboratory for Cellular Dynamics and Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Lilei Xu
- Beijing Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Keqiong Ye
- Key Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Kaiming Zhang
- MOE Key Laboratory for Cellular Dynamics and Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
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8
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Abstract
Riboswitches are conserved functional domains in mRNA that almost exclusively exist in bacteria. They regulate the biosynthesis and transport of amino acids and essential metabolites such as coenzymes, nucleobases, and their derivatives by specifically binding small molecules. Due to their ability to precisely discriminate between different cognate molecules as well as their common existence in bacteria, riboswitches have become potential antibacterial drug targets that could deliver urgently needed antibiotics with novel mechanisms of action. In this work, we report the recognition mechanisms of four oxidization products (XAN, AZA, UAC, and HPA) generated during purine degradation by an RNA motif termed the NMT1 riboswitch. Specifically, we investigated the physical interactions between the riboswitch and the oxidized metabolites by computing the changes in the free energy on mutating key nucleobases in the ligand binding pocket of the riboswitch. We discovered that the electrostatic interactions are central to ligand discrimination by this riboswitch. The relative binding free energies of the mutations further indicated that some of the mutations can also strengthen the binding affinities of the ligands (AZA, UAC, and HPA). These mechanistic details are also potentially relevant in the design of novel compounds targeting riboswitches.
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Affiliation(s)
- Amit Kumar
- Department of Chemical Engineering, University of New Hampshire, Durham, New Hampshire 03824, United States
| | - Harish Vashisth
- Department of Chemical Engineering, University of New Hampshire, Durham, New Hampshire 03824, United States
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9
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Hu G, Zhou HX. Magnesium ions mediate ligand binding and conformational transition of the SAM/SAH riboswitch. Commun Biol 2023; 6:791. [PMID: 37524918 PMCID: PMC10390503 DOI: 10.1038/s42003-023-05175-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023] Open
Abstract
The SAM/SAH riboswitch binds S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) with similar affinities. Mg2+ is generally known to stabilize RNA structures by neutralizing phosphates, but how it contributes to ligand binding and conformational transition is understudied. Here, extensive molecular dynamics simulations (totaling 120 μs) predicted over 10 inner-shell Mg2+ ions in the SAM/SAH riboswitch. Six of them line the two sides of a groove to widen it and thereby pre-organize the riboswitch for ligand entry. They also form outer-shell coordination with the ligands and stabilize an RNA-ligand hydrogen bond, which effectively diminishes the selectivity between SAM and SAH. One Mg2+ ion unique to the apo form maintains the Shine-Dalgarno sequence in an autonomous mode and thereby facilitates its release for ribosome binding. Mg2+ thus plays vital roles in SAM/SAH riboswitch function.
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Affiliation(s)
- Guodong Hu
- Shandong Key Laboratory of Biophysics, Dezhou University, Dezhou, 253023, China
- Department of Chemistry, University of Illinois Chicago, Chicago, IL, 60607, USA
| | - Huan-Xiang Zhou
- Department of Chemistry, University of Illinois Chicago, Chicago, IL, 60607, USA.
- Department of Physics, University of Illinois Chicago, Chicago, IL, 60607, USA.
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10
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Hu G, Zhou HX. Magnesium ions mediate ligand binding and conformational transition of the SAM/SAH riboswitch. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.12.532287. [PMID: 36945415 PMCID: PMC10029009 DOI: 10.1101/2023.03.12.532287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
The SAM/SAH riboswitch binds S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) with similar affinities. Mg 2+ is generally known to stabilize RNA structures by neutralizing phosphates, but how it contributes to ligand binding and conformational transition is understudied. Here, extensive molecular dynamics simulations (totaling 120 μs) identified over 10 inner-shell Mg 2+ ions in the SAM/SAH riboswitch. Six of them line the two sides of a groove to widen it and thereby pre-organize the riboswitch for ligand entry. They also form outer-shell coordination with the ligands and stabilize an RNA-ligand hydrogen bond, which effectively diminish the selectivity between SAM and SAH. One Mg 2+ ion unique to the apo form maintains the Shine-Dalgarno sequence in an autonomous mode and thereby facilitates its release for ribosome binding. Mg 2+ thus plays vital roles in SAM/SAH riboswitch function.
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Affiliation(s)
- Guodong Hu
- Shandong Key Laboratory of Biophysics, Dezhou University, Dezhou 253023, China
- Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607
| | - Huan-Xiang Zhou
- Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607
- Department of Physics, University of Illinois Chicago, Chicago, IL 60607
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11
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Xu L, Xiao Y, Zhang J, Fang X. Structural insights into translation regulation by the THF-II riboswitch. Nucleic Acids Res 2023; 51:952-965. [PMID: 36620887 PMCID: PMC9881143 DOI: 10.1093/nar/gkac1257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/12/2022] [Accepted: 12/15/2022] [Indexed: 01/10/2023] Open
Abstract
In bacteria, expression of folate-related genes is controlled by the tetrahydrofolate (THF) riboswitch in response to specific binding of THF and its derivatives. Recently, a second class of THF riboswitches, named THF-II, was identified in Gram-negative bacteria, which exhibit distinct architecture from the previously characterized THF-I riboswitches found in Gram-positive bacteria. Here, we present the crystal structures of the ligand-bound THF-II riboswitch from Mesorhizobium loti. These structures exhibit a long rod-like fold stabilized by continuous base pair and base triplet stacking across two helices of P1 and P2 and their interconnecting ligand-bound binding pocket. The pterin moiety of the ligand docks into the binding pocket by forming hydrogen bonds with two highly conserved pyrimidines in J12 and J21, which resembles the hydrogen-bonding pattern at the ligand-binding site FAPK in the THF-I riboswitch. Using small-angle X-ray scattering and isothermal titration calorimetry, we further characterized the riboswitch in solution and reveal that Mg2+ is essential for pre-organization of the binding pocket for efficient ligand binding. RNase H cleavage assay indicates that ligand binding reduces accessibility of the ribosome binding site in the right arm of P1, thus down-regulating the expression of downstream genes. Together, these results provide mechanistic insights into translation regulation by the THF-II riboswitch.
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Affiliation(s)
| | | | - Jie Zhang
- Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China,Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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12
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Potential effects of metal ion induced two-state allostery on the regulatory mechanism of add adenine riboswitch. Commun Biol 2022; 5:1120. [PMID: 36273041 PMCID: PMC9588036 DOI: 10.1038/s42003-022-04096-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
Riboswitches normally regulate gene expression through structural changes in response to the specific binding of cellular metabolites or metal ions. Taking add adenine riboswitch as an example, we explore the influences of metal ions (especially for K+ and Mg2+ ions) on the structure and dynamics of riboswitch aptamer (with and without ligand) by using molecular dynamic (MD) simulations. Our results show that a two-state transition marked by the structural deformation at the connection of J12 and P1 (CJ12-P1) is not only related to the binding of cognate ligands, but also strongly coupled with the change of metal ion environments. Moreover, the deformation of the structure at CJ12-P1 can be transmitted to P1 directly connected to the expression platform in multiple ways, which will affect the structure and stability of P1 to varying degrees, and finally change the regulation state of this riboswitch. Molecular dynamic simulations are employed to assess the influence of metal ions on riboswitch structure and dynamics, suggesting a conformational control of riboswitch aptamers by metal ions before ligand binding.
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13
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Mair S, Erharter K, Renard E, Brillet K, Brunner M, Lusser A, Kreutz C, Ennifar E, Micura R. Towards a comprehensive understanding of RNA deamination: synthesis and properties of xanthosine-modified RNA. Nucleic Acids Res 2022; 50:6038-6051. [PMID: 35687141 PMCID: PMC9226506 DOI: 10.1093/nar/gkac477] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/28/2022] [Accepted: 05/20/2022] [Indexed: 12/25/2022] Open
Abstract
Nucleobase deamination, such as A-to-I editing, represents an important posttranscriptional modification of RNA. When deamination affects guanosines, a xanthosine (X) containing RNA is generated. However, the biological significance and chemical consequences on RNA are poorly understood. We present a comprehensive study on the preparation and biophysical properties of X-modified RNA. Thermodynamic analyses revealed that base pairing strength is reduced to a level similar to that observed for a G•U replacement. Applying NMR spectroscopy and X-ray crystallography, we demonstrate that X can form distinct wobble geometries with uridine depending on the sequence context. In contrast, X pairing with cytidine occurs either through wobble geometry involving protonated C or in Watson-Crick-like arrangement. This indicates that the different pairing modes are of comparable stability separated by low energetic barriers for switching. Furthermore, we demonstrate that the flexible pairing properties directly affect the recognition of X-modified RNA by reverse transcription enzymes. Primer extension assays and PCR-based sequencing analysis reveal that X is preferentially read as G or A and that the ratio depends on the type of reverse transcriptase. Taken together, our results elucidate important properties of X-modified RNA paving the way for future studies on its biological significance.
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Affiliation(s)
- Stefan Mair
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Kevin Erharter
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Eva Renard
- Architecture et Réactivité de l’ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, 67000 Strasbourg, France
| | - Karl Brillet
- Architecture et Réactivité de l’ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, 67000 Strasbourg, France
| | - Melanie Brunner
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Alexandra Lusser
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Christoph Kreutz
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Eric Ennifar
- Architecture et Réactivité de l’ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, 67000 Strasbourg, France
| | - Ronald Micura
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
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14
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Hamal Dhakal S, Panchapakesan SSS, Slattery P, Roth A, Breaker RR. Variants of the guanine riboswitch class exhibit altered ligand specificities for xanthine, guanine, or 2'-deoxyguanosine. Proc Natl Acad Sci U S A 2022; 119:e2120246119. [PMID: 35622895 PMCID: PMC9295807 DOI: 10.1073/pnas.2120246119] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/12/2022] [Indexed: 12/30/2022] Open
Abstract
The aptamer portions of previously reported riboswitch classes that sense guanine, adenine, or 2′-deoxyguanosine are formed by a highly similar three-stem junction with distinct nucleotide sequences in the regions joining the stems. The nucleotides in these joining regions form the major features of the selective ligand-binding pocket for each aptamer. Previously, we reported the existence of additional, rare variants of the predominant guanine-sensing riboswitch class that carry nucleotide differences in the ligand-binding pocket, suggesting that these RNAs have further diversified their structures and functions. Herein, we report the discovery and analysis of three naturally occurring variants of guanine riboswitches that are narrowly distributed across Firmicutes. These RNAs were identified using comparative sequence analysis methods, which also revealed that some of the gene associations for these variants are atypical for guanine riboswitches or their previously known natural variants. Binding assays demonstrate that the newfound variant riboswitch representatives recognize xanthine, guanine, or 2′-deoxyguanosine, with the guanine class exhibiting greater discrimination against related purines than the more common guanine riboswitch class reported previously. These three additional variant classes, together with the four previously discovered riboswitch classes that employ the same three-stem junction architecture, reveal how a simple structural framework can be diversified to expand the range of purine-based ligands sensed by RNA.
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Affiliation(s)
- Siddhartha Hamal Dhakal
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520-8103
| | | | - Paul Slattery
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520-8103
| | - Adam Roth
- HHMI, Yale University, New Haven, CT 06520-8103
| | - Ronald R. Breaker
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520-8103
- HHMI, Yale University, New Haven, CT 06520-8103
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8103
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15
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Structure and mechanism of the methyltransferase ribozyme MTR1. Nat Chem Biol 2022; 18:547-555. [PMID: 35301481 PMCID: PMC7612680 DOI: 10.1038/s41589-022-00976-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/13/2022] [Indexed: 11/20/2022]
Abstract
RNA-catalysed RNA methylation was recently shown to be part of the catalytic repertoire of ribozymes. The methyltransferase ribozyme MTR1 catalyses the site-specific synthesis of 1-methyladenosine (m1A) in RNA, using O6-methylguanine (m6G) as methyl group donor. Here we report the crystal structure of MTR1 at a resolution of 2.8 Å, which reveals a guanine binding site reminiscent of natural guanine riboswitches. The structure represents the postcatalytic state of a split ribozyme in complex with the m1A-containing RNA product and the demethylated cofactor guanine. The structural data suggest the mechanistic involvement of a protonated cytidine in the methyl transfer reaction. A synergistic effect of two 2'-O-methylated ribose residues in the active site results in accelerated methyl group transfer. Supported by these results, it seems plausible that modified nucleotides may have enhanced early RNA catalysis and that metabolite-binding riboswitches may resemble inactivated ribozymes that have lost their catalytic activity during evolution.
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16
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Wang S, Chen D, Gao L, Liu Y. Short Oligonucleotides Facilitate Co-transcriptional Labeling of RNA at Specific Positions. J Am Chem Soc 2022; 144:5494-5502. [PMID: 35293210 DOI: 10.1021/jacs.2c00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Labeling RNA molecules at specific positions is critical for RNA research and applications. Such methods are in high demand but still a challenge, especially those that enable native co-synthesis rather than post-synthesis labeling of long RNAs. The method we developed in this work meets these requirements, in which a leader RNA is extended on the hybrid solid-liquid phase by an engineered transcriptional complex following the pause-restart mode. A custom-designed short oligonucleotide is used to functionalize the engineered complex. This remarkable co-transcriptional labeling method incorporates labels into RNAs in high yields with great flexibility. We demonstrate the method by successfully introducing natural modifications, a fluorescent nucleotide analogue and a donor-acceptor fluorophore pair to specific sites located at an internal loop, a pseudoknot, a junction, a helix, and the middle of consecutive identical nucleotides of various RNAs. This newly developed method overcomes efficiency and position-choosing constraints that have hampered routine strategies to label RNAs beyond 200 nucleotides (nt).
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Affiliation(s)
- Siyu Wang
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dian Chen
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lingzhi Gao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yu Liu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.,Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
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17
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Li X, Yang H, He J, Yang B, Zhao Y, Wu P. Full liberation of 2-Aminopurine with nucleases digestion for highly sensitive biosensing. Biosens Bioelectron 2022; 196:113721. [PMID: 34673482 DOI: 10.1016/j.bios.2021.113721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/09/2021] [Accepted: 10/15/2021] [Indexed: 02/05/2023]
Abstract
2-Aminopurine (2-AP), a fluorescent isomer of adenine, is a popular fluorescent tag for DNA-based biosensors. The fluorescence of 2-AP is highly dependent on its microenvironment, i.e., almost non-fluorescent and merely fluorescent in dsDNA and ssDNA, respectively, but can be greatly brightened as mononucleotide. In most 2-AP-based biosensors, DNA transformation from dsDNA to ssDNA was employed, while selective digestion of 2-AP-labeled DNA with nucleases represents an appealing approach for improving the biosensor sensitivity. However, some detailed fundamental information, such as the reason for nuclease digestion, the influence of the labeling site, neighboring bases, or the label number of 2-AP for final signal output, are still largely unknown, which greatly limits the utility of 2-AP-based biosensors. In this work, using both steady- and excited-state fluorescence (lifetime), we demonstrated that nuclease digestion resulted in almost full liberation of 2-AP mononucleotides, and was free from labeling site and neighboring bases. Furthermore, we also found that nuclease digestion could lead to multiplexed sensitivity from increasing number of 2-AP labelling, but was not achievable for the conventional biosensors without full liberation of 2-AP. Considering the popularity of 2-AP in biosensing and other related applications, the above obtained information in sensitivity boosting is fundamentally important for future design of 2-AP-based biosensors.
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Affiliation(s)
- Xianming Li
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Yang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jialun He
- Analytical & Testing Center, Sichuan University, Chengdu, 610064, China
| | - Bin Yang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peng Wu
- Analytical & Testing Center, Sichuan University, Chengdu, 610064, China.
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