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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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Yang Y, Murrali MG, Wang Y, Galvan S, Ajjampore N, Feigon J. HEXIM1 homodimer binds two sites on 7SK RNA to release autoinhibition for P-TEFb inactivation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.10.617642. [PMID: 39416148 PMCID: PMC11482958 DOI: 10.1101/2024.10.10.617642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Hexim proteins are RNA-dependent regulators whose main target is 7SK long non-coding RNA, a major regulator of eukaryotic mRNA transcription. 7SK RNPs control available intracellular concentrations of the kinase P-TEFb (Cdk9-CyclinT1/2) by sequestering it in an inactive form. Active P-TEFb phosphorylates NELF, DSIF, and the RNA polymerase II CTD to transition it from promoter-proximal pausing to productive elongation. P-TEFb associates with 7SK RNP via Hexim, which directly binds 7SK RNA. However, free Hexim is in an autoinhibited state that cannot inactivate P-TEFb, and how Hexim autoinhibition is released by 7SK remains unknown. Here, we show that one Hexim1 homodimer binds two sites on linear 7SK RNA in a manner that exposes the Cdk9 binding sites, which are otherwise masked within the autoinhibited dimer. These results provide mechanistic insights into Hexim-RNA specificity and explain how P-TEFb can be effectively regulated to respond to changing levels of transcriptional signaling.
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3
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Camara MB, Sobeh AM, Eichhorn CD. Progress in 7SK ribonucleoprotein structural biology. Front Mol Biosci 2023; 10:1154622. [PMID: 37051324 PMCID: PMC10083321 DOI: 10.3389/fmolb.2023.1154622] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
The 7SK ribonucleoprotein (RNP) is a dynamic and multifunctional regulator of RNA Polymerase II (RNAPII) transcription in metazoa. Comprised of the non-coding 7SK RNA, core proteins, and numerous accessory proteins, the most well-known 7SK RNP function is the sequestration and inactivation of the positive transcription elongation factor b (P-TEFb). More recently, 7SK RNP has been shown to regulate RNAPII transcription through P-TEFb-independent pathways. Due to its fundamental role in cellular function, dysregulation has been linked with human diseases including cancers, heart disease, developmental disorders, and viral infection. Significant advances in 7SK RNP structural biology have improved our understanding of 7SK RNP assembly and function. Here, we review progress in understanding the structural basis of 7SK RNA folding, biogenesis, and RNP assembly.
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Affiliation(s)
- Momodou B. Camara
- Department of Chemistry, University of Nebraska, Lincoln, NE, United States
| | - Amr M. Sobeh
- Department of Chemistry, University of Nebraska, Lincoln, NE, United States
| | - Catherine D. Eichhorn
- Department of Chemistry, University of Nebraska, Lincoln, NE, United States
- Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, United States
- *Correspondence: Catherine D. Eichhorn,
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4
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Pham VV, Gao M, Meagher JL, Smith JL, D'Souza VM. A structure-based mechanism for displacement of the HEXIM adapter from 7SK small nuclear RNA. Commun Biol 2022; 5:819. [PMID: 35970937 PMCID: PMC9378691 DOI: 10.1038/s42003-022-03734-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
Productive transcriptional elongation of many cellular and viral mRNAs requires transcriptional factors to extract pTEFb from the 7SK snRNP by modulating the association between HEXIM and 7SK snRNA. In HIV-1, Tat binds to 7SK by displacing HEXIM. However, without the structure of the 7SK-HEXIM complex, the constraints that must be overcome for displacement remain unknown. Furthermore, while structure details of the TatNL4-3-7SK complex have been elucidated, it is unclear how subtypes with more HEXIM-like Tat sequences accomplish displacement. Here we report the structures of HEXIM, TatG, and TatFin arginine rich motifs in complex with the apical stemloop-1 of 7SK. While most interactions between 7SK with HEXIM and Tat are similar, critical differences exist that guide function. First, the conformational plasticity of 7SK enables the formation of three different base pair configurations at a critical remodeling site, which allows for the modulation required for HEXIM binding and its subsequent displacement by Tat. Furthermore, the specific sequence variations observed in various Tat subtypes all converge on remodeling 7SK at this region. Second, we show that HEXIM primes its own displacement by causing specific local destabilization upon binding - a feature that is then exploited by Tat to bind 7SK more efficiently.
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Affiliation(s)
- Vincent V Pham
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Michael Gao
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Jennifer L Meagher
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Janet L Smith
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Victoria M D'Souza
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA.
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5
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Van Damme R, Li K, Zhang M, Bai J, Lee WH, Yesselman JD, Lu Z, Velema WA. Chemical reversible crosslinking enables measurement of RNA 3D distances and alternative conformations in cells. Nat Commun 2022; 13:911. [PMID: 35177610 PMCID: PMC8854666 DOI: 10.1038/s41467-022-28602-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Three-dimensional (3D) structures dictate the functions of RNA molecules in a wide variety of biological processes. However, direct determination of RNA 3D structures in vivo is difficult due to their large sizes, conformational heterogeneity, and dynamics. Here we present a method, Spatial 2'-Hydroxyl Acylation Reversible Crosslinking (SHARC), which uses chemical crosslinkers of defined lengths to measure distances between nucleotides in cellular RNA. Integrating crosslinking, exonuclease (exo) trimming, proximity ligation, and high throughput sequencing, SHARC enables transcriptome-wide tertiary structure contact maps at high accuracy and precision, revealing heterogeneous RNA structures and interactions. SHARC data provide constraints that improves Rosetta-based RNA 3D structure modeling at near-nanometer resolution. Integrating SHARC-exo with other crosslinking-based methods, we discover compact folding of the 7SK RNA, a critical regulator of transcriptional elongation. These results establish a strategy for measuring RNA 3D distances and alternative conformations in their native cellular context.
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Affiliation(s)
- Ryan Van Damme
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Kongpan Li
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Minjie Zhang
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jianhui Bai
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Wilson H Lee
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA
| | - Joseph D Yesselman
- Department of Chemistry, University of Nebraska-Lincoln, 832A Hamilton Hall, Lincoln, NE, 68588, USA
| | - Zhipeng Lu
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90033, USA.
| | - Willem A Velema
- Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen, The Netherlands.
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6
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Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods. QRB DISCOVERY 2022. [PMID: 37529277 PMCID: PMC10392686 DOI: 10.1017/qrd.2022.19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism.
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7
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RNA Modeling with the Computational Energy Landscape Framework. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2323:49-66. [PMID: 34086273 DOI: 10.1007/978-1-0716-1499-0_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The recent advances in computational abilities, such as the enormous speed-ups provided by GPU computing, allow for large scale computational studies of RNA molecules at an atomic level of detail. As RNA molecules are known to adopt multiple conformations with comparable energies, but different two-dimensional structures, all-atom models are necessary to better describe the structural ensembles for RNA molecules. This point is important because different conformations can exhibit different functions, and their regulation or mis-regulation is linked to a number of diseases. Problematically, the energy barriers between different conformational ensembles are high, resulting in long time scales for interensemble transitions. The computational potential energy landscape framework was designed to overcome this problem of broken ergodicity by use of geometry optimization. Here, we describe the algorithms used in the energy landscape explorations with the OPTIM and PATHSAMPLE programs, and how they are used in biomolecular simulations. We present a recent case study of the 5'-hairpin of RNA 7SK to illustrate how the method can be applied to interpret experimental results, and to obtain a detailed description of molecular properties.
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8
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Luo L, Chiu LY, Sugarman A, Gupta P, Rouskin S, Tolbert BS. HnRNP A1/A2 Proteins Assemble onto 7SK snRNA via Context Dependent Interactions. J Mol Biol 2021; 433:166885. [PMID: 33684393 DOI: 10.1016/j.jmb.2021.166885] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 02/13/2021] [Accepted: 02/15/2021] [Indexed: 02/08/2023]
Abstract
7SK small nuclear RNA (snRNA) is an abundant and ubiquitously expressed noncoding RNA that functions to modulate the activity of RNA Polymerase II (RNAPII) in part by stabilizing distinct pools of 7SK-protein complexes. Prevailing models suggest that the secondary structure of 7SK is dynamically remodeled within its alternative RNA-protein pools such that its architecture differentially regulates the exchange of cognate binding partners. The nuclear hnRNP A1/A2 proteins influence the biology of 7SK snRNA via processes that require an intact stem loop (SL) 3 domain; however, the molecular details by which hnRNPs assemble onto 7SK snRNA are yet to be described. Here, we have taken an integrated approach to present a detailed description of the 7SK-hnRNP A1 complex. We show that unbound 7SK snRNA adopts at least two major conformations in solution, with significant structural differences localizing to the SL2-3 linker and the base of SL3. Phylogenetic analysis indicates that this same region is the least genetically conserved feature of 7SK snRNA. By performing DMS modifications with the presence of excess protein, we reveal that hnRNP A1 binds with selectivity to SL3 through mechanisms that increase the flexibility of the RNA adjacent to putative binding sites. Calorimetric titrations further validate that hnRNP A1-SL3 assembly is complex with the affinity of discrete binding events modulated by the surrounding RNA structure. To interpret this context-dependent binding phenomenon, we determined a 3D model of SL3 to show that it folds to position minimal hnRNP A1/A2 binding sites (5'-Y/RAG-3') within different local environments. SL3-protein complexes resolved by SEC-MALS-SAXS confirm that up to four hnRNP A1 proteins bind along the entire surface of SL3 via interactions that preserve the overall structural integrity of this domain. In sum, the collective results presented here reveal a specific role for a folded SL3 domain to scaffold hnRNP A1/A2-7SK assembly via mechanisms modulated by the surrounding RNA structure.
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Affiliation(s)
- Le Luo
- Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Liang-Yuan Chiu
- Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Andrew Sugarman
- Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Paromita Gupta
- Whitehead Institute for Biomedical Research, Cambridge, MA, United States
| | - Silvi Rouskin
- Whitehead Institute for Biomedical Research, Cambridge, MA, United States
| | - Blanton S Tolbert
- Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, United States.
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9
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Brillet K, Martinez-Zapien D, Bec G, Ennifar E, Dock-Bregeon AC, Lebars I. Different views of the dynamic landscape covered by the 5'-hairpin of the 7SK small nuclear RNA. RNA (NEW YORK, N.Y.) 2020; 26:1184-1197. [PMID: 32430362 PMCID: PMC7430674 DOI: 10.1261/rna.074955.120] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/13/2020] [Indexed: 06/11/2023]
Abstract
The 7SK small nuclear RNA (7SKsnRNA) plays a key role in the regulation of RNA polymerase II by sequestrating and inhibiting the positive transcription elongation factor b (P-TEFb) in the 7SK ribonucleoprotein complex (7SKsnRNP), a process mediated by interaction with the protein HEXIM. P-TEFb is also an essential cellular factor recruited by the viral protein Tat to ensure the replication of the viral RNA in the infection cycle of the human immunodeficiency virus (HIV-1). Tat promotes the release of P-TEFb from the 7SKsnRNP and subsequent activation of transcription, by displacing HEXIM from the 5'-hairpin of the 7SKsnRNA. This hairpin (HP1), comprising the signature sequence of the 7SKsnRNA, has been the subject of three independent structural studies aimed at identifying the structural features that could drive the recognition by the two proteins, both depending on arginine-rich motifs (ARM). Interestingly, four distinct structures were determined. In an attempt to provide a comprehensive view of the structure-function relationship of this versatile RNA, we present here a structural analysis of the models, highlighting how HP1 is able to adopt distinct conformations with significant impact on the compactness of the molecule. Since these models are solved under different conditions by nuclear magnetic resonance (NMR) and crystallography, the impact of the buffer composition on the conformational variation was investigated by complementary biophysical approaches. Finally, using isothermal titration calorimetry, we determined the thermodynamic signatures of the Tat-ARM and HEXIM-ARM peptide interactions with the RNA, showing that they are associated with distinct binding mechanisms.
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Affiliation(s)
- Karl Brillet
- Université de Strasbourg, Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, F-67084 Strasbourg, France
| | - Denise Martinez-Zapien
- Department of Integrated Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS UMR 7104, INSERM U964, Université de Strasbourg, 67404 Illkirch Cedex, France
| | - Guillaume Bec
- Université de Strasbourg, Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, F-67084 Strasbourg, France
| | - Eric Ennifar
- Université de Strasbourg, Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, F-67084 Strasbourg, France
| | - Anne-Catherine Dock-Bregeon
- Laboratory of Integrative Biology of Marine Models (LBI2M), Sorbonne University-CNRS UMR 8227, Station Biologique de Roscoff, 29680 Roscoff Cedex, France
| | - Isabelle Lebars
- Université de Strasbourg, Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, F-67084 Strasbourg, France
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10
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Röder K, Stirnemann G, Dock-Bregeon AC, Wales DJ, Pasquali S. Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding. Nucleic Acids Res 2020; 48:373-389. [PMID: 31732748 PMCID: PMC7145557 DOI: 10.1093/nar/gkz1071] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 10/23/2019] [Accepted: 10/31/2019] [Indexed: 12/25/2022] Open
Abstract
7SK RNA, as part of the 7SK ribonucleoprotein complex, is crucial to the regulation of transcription by RNA-polymerase II, via its interaction with the positive transcription elongation factor P-TEFb. The interaction is induced by binding of the protein HEXIM to the 5′ hairpin (HP1) of 7SK RNA. Four distinct structural models have been obtained experimentally for HP1. Here, we employ computational methods to investigate the relative stability of these structures, transitions between them, and the effects of mutations on the observed structural ensembles. We further analyse the results with respect to mutational binding assays, and hypothesize a mechanism for HEXIM binding. Our results indicate that the dominant structure in the wild type exhibits a triplet involving the unpaired nucleotide U40 and the base pair A43-U66 in the GAUC/GAUC repeat. This conformation leads to an open major groove with enough potential binding sites for peptide recognition. Sequence mutations of the RNA change the relative stability of the different structural ensembles. Binding affinity is consequently lost if these changes alter the dominant structure.
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Affiliation(s)
- Konstantin Röder
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Guillaume Stirnemann
- CNRS Laboratoire de Biochimie Théorique, Institut de Biologie Physico-Chimique, PSL University, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France
| | - Anne-Catherine Dock-Bregeon
- Laboratoire de Biologie Intégrative des Modèles Marins, UMR CNRS 8227, Sorbonne Université, Station Biologique de Roscoff, 29680 Roscoff, France
| | - David J Wales
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Samuela Pasquali
- Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, 4 Avenue de l'observatoire, 75270 Paris, France
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11
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Asadi-Atoi P, Barraud P, Tisne C, Kellner S. Benefits of stable isotope labeling in RNA analysis. Biol Chem 2020; 400:847-865. [PMID: 30893050 DOI: 10.1515/hsz-2018-0447] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 03/11/2019] [Indexed: 02/07/2023]
Abstract
RNAs are key players in life as they connect the genetic code (DNA) with all cellular processes dominated by proteins. They contain a variety of chemical modifications and many RNAs fold into complex structures. Here, we review recent progress in the analysis of RNA modification and structure on the basis of stable isotope labeling techniques. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the key tools and many breakthrough developments were made possible by the analysis of stable isotope labeled RNA. Therefore, we discuss current stable isotope labeling techniques such as metabolic labeling, enzymatic labeling and chemical synthesis. RNA structure analysis by NMR is challenging due to two major problems that become even more salient when the size of the RNA increases, namely chemical shift overlaps and line broadening leading to complete signal loss. Several isotope labeling strategies have been developed to provide solutions to these major issues, such as deuteration, segmental isotope labeling or site-specific labeling. Quantification of modified nucleosides in RNA by MS is only possible through the application of stable isotope labeled internal standards. With nucleic acid isotope labeling coupled mass spectrometry (NAIL-MS), it is now possible to analyze the dynamic processes of post-transcriptional RNA modification and demodification. The trend, in both NMR and MS RNA analytics, is without doubt shifting from the analysis of snapshot moments towards the development and application of tools capable of analyzing the dynamics of RNA structure and modification profiles.
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Affiliation(s)
- Paria Asadi-Atoi
- Department of Chemistry, Ludwig-Maximilians-University Munich, Butenandtstr. 5-13, D-81377 Munich, Germany
| | - Pierre Barraud
- Institut de Biologie Physico-Chimique (IBPC), UMR 8261, CNRS, Université Paris Diderot, 13 rue Pierre et Marie Curie, F-75005 Paris, France
| | - Carine Tisne
- Institut de Biologie Physico-Chimique (IBPC), UMR 8261, CNRS, Université Paris Diderot, 13 rue Pierre et Marie Curie, F-75005 Paris, France
| | - Stefanie Kellner
- Department of Chemistry, Ludwig-Maximilians-University Munich, Butenandtstr. 5-13, D-81377 Munich, Germany
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12
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Wang PY, Sexton AN, Culligan WJ, Simon MD. Carbodiimide reagents for the chemical probing of RNA structure in cells. RNA (NEW YORK, N.Y.) 2019; 25:135-146. [PMID: 30389828 PMCID: PMC6298570 DOI: 10.1261/rna.067561.118] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 10/18/2018] [Indexed: 05/09/2023]
Abstract
Deciphering the conformations of RNAs in their cellular environment allows identification of RNA elements with potentially functional roles within biological contexts. Insight into the conformation of RNA in cells has been achieved using chemical probes that were developed to react specifically with flexible RNA nucleotides, or the Watson-Crick face of single-stranded nucleotides. The most widely used probes are either selective SHAPE (2'-hydroxyl acylation and primer extension) reagents that probe nucleotide flexibility, or dimethyl sulfate (DMS), which probes the base-pairing at adenine and cytosine but is unable to interrogate guanine or uracil. The constitutively charged carbodiimide N-cyclohexyl-N'-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate (CMC) is widely used for probing G and U nucleotides, but has not been established for probing RNA in cells. Here, we report the use of a smaller and conditionally charged reagent, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), as a chemical probe of RNA conformation, and the first reagent validated for structure probing of unpaired G and U nucleotides in intact cells. We showed that EDC demonstrates similar reactivity to CMC when probing transcripts in vitro. We found that EDC specifically reacted with accessible nucleotides in the 7SK noncoding RNA in intact cells. We probed structured regions within the Xist lncRNA with EDC and integrated these data with DMS probing data. Together, EDC and DMS allowed us to refine predicted structure models for the 3' extension of repeat C within Xist. These results highlight how complementing DMS probing experiments with EDC allows the analysis of Watson-Crick base-pairing at all four nucleotides of RNAs in their cellular context.
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Affiliation(s)
- Peter Y Wang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06511, USA
- Chemical Biology Institute, Yale University, West Haven, Connecticut 06516, USA
| | - Alec N Sexton
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06511, USA
- Chemical Biology Institute, Yale University, West Haven, Connecticut 06516, USA
| | - William J Culligan
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06511, USA
- Chemical Biology Institute, Yale University, West Haven, Connecticut 06516, USA
- Department of Cell Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Matthew D Simon
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06511, USA
- Chemical Biology Institute, Yale University, West Haven, Connecticut 06516, USA
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13
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Reovirus Nonstructural Protein σNS Acts as an RNA Stability Factor Promoting Viral Genome Replication. J Virol 2018; 92:JVI.00563-18. [PMID: 29769334 DOI: 10.1128/jvi.00563-18] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 05/07/2018] [Indexed: 12/23/2022] Open
Abstract
Viral nonstructural proteins, which are not packaged into virions, are essential for the replication of most viruses. Reovirus, a nonenveloped, double-stranded RNA (dsRNA) virus, encodes three nonstructural proteins that are required for viral replication and dissemination in the host. The reovirus nonstructural protein σNS is a single-stranded RNA (ssRNA)-binding protein that must be expressed in infected cells for production of viral progeny. However, the activities of σNS during individual steps of the reovirus replication cycle are poorly understood. We explored the function of σNS by disrupting its expression during infection using cells expressing a small interfering RNA (siRNA) targeting the σNS-encoding S3 gene and found that σNS is required for viral genome replication. Using complementary biochemical assays, we determined that σNS forms complexes with viral and nonviral RNAs. We also discovered, using in vitro and cell-based RNA degradation experiments, that σNS increases the RNA half-life. Cryo-electron microscopy revealed that σNS and ssRNAs organize into long, filamentous structures. Collectively, our findings indicate that σNS functions as an RNA-binding protein that increases the viral RNA half-life. These results suggest that σNS forms RNA-protein complexes in preparation for genome replication.IMPORTANCE Following infection, viruses synthesize nonstructural proteins that mediate viral replication and promote dissemination. Viruses from the family Reoviridae encode nonstructural proteins that are required for the formation of progeny viruses. Although nonstructural proteins of different viruses in the family Reoviridae diverge in primary sequence, they are functionally homologous and appear to facilitate conserved mechanisms of dsRNA virus replication. Using in vitro and cell culture approaches, we found that the mammalian reovirus nonstructural protein σNS binds and stabilizes viral RNA and is required for genome synthesis. This work contributes new knowledge about basic mechanisms of dsRNA virus replication and provides a foundation for future studies to determine how viruses in the family Reoviridae assort and replicate their genomes.
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14
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Eichhorn CD, Yang Y, Repeta L, Feigon J. Structural basis for recognition of human 7SK long noncoding RNA by the La-related protein Larp7. Proc Natl Acad Sci U S A 2018; 115:E6457-E6466. [PMID: 29946027 PMCID: PMC6048529 DOI: 10.1073/pnas.1806276115] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The La and the La-related protein (LARP) superfamily is a diverse class of RNA binding proteins involved in RNA processing, folding, and function. Larp7 binds to the abundant long noncoding 7SK RNA and is required for 7SK ribonucleoprotein (RNP) assembly and function. The 7SK RNP sequesters a pool of the positive transcription elongation factor b (P-TEFb) in an inactive state; on release, P-TEFb phosphorylates RNA Polymerase II to stimulate transcription elongation. Despite its essential role in transcription, limited structural information is available for the 7SK RNP, particularly for protein-RNA interactions. Larp7 contains an N-terminal La module that binds UUU-3'OH and a C-terminal atypical RNA recognition motif (xRRM) required for specific binding to 7SK and P-TEFb assembly. Deletion of the xRRM is linked to gastric cancer in humans. We report the 2.2-Å X-ray crystal structure of the human La-related protein group 7 (hLarp7) xRRM bound to the 7SK stem-loop 4, revealing a unique binding interface. Contributions of observed interactions to binding affinity were investigated by mutagenesis and isothermal titration calorimetry. NMR 13C spin relaxation data and comparison of free xRRM, RNA, and xRRM-RNA structures show that the xRRM is preordered to bind a flexible loop 4. Combining structures of the hLarp7 La module and the xRRM-7SK complex presented here, we propose a structural model for Larp7 binding to the 7SK 3' end and mechanism for 7SK RNP assembly. This work provides insight into how this domain contributes to 7SK recognition and assembly of the core 7SK RNP.
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Affiliation(s)
- Catherine D Eichhorn
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1569
| | - Yuan Yang
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1569
| | - Lucas Repeta
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1569
| | - Juli Feigon
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1569
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15
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Abstract
The 7SK RNA is a small nuclear RNA that is involved in the regulation of Pol-II transcription. It is very well conserved in vertebrates, but shows extensive variations in both sequence and structure across invertebrates. A systematic homology search extended the collection of 7SK genes in both Arthropods and Lophotrochozoa making use of the large number of recently published invertebrate genomes. The extended data set made it possible to infer complete consensus structures for invertebrate 7SK RNAs. These show that not only the well-conserved 5'- and 3'- domains but all the interior Stem A domain is universally conserved. In contrast, Stem B region exhibits substantial structural variation and does not adhere to a common structural model beyond phylum level.
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Affiliation(s)
- Ali M Yazbeck
- a Bioinformatics Group, Department of Computer Science , Leipzig University , Härtelstraße 16-18, Leipzig , Germany.,b Lebanese University, Doctoral School for Science and Technology, Rafic Hariri University Campus , Hadath , Lebanon
| | - Kifah R Tout
- b Lebanese University, Doctoral School for Science and Technology, Rafic Hariri University Campus , Hadath , Lebanon
| | - Peter F Stadler
- a Bioinformatics Group, Department of Computer Science , Leipzig University , Härtelstraße 16-18, Leipzig , Germany.,c Interdisciplinary Center for Bioinformatics, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases , Leipzig University.,d Department of Diagnostics , Fraunhofer Institute for Cell Therapy and Immunology - IZI , Perlickstraße 1, D-04103 Leipzig , Germany.,e Max Planck Institute for Mathematics in the Sciences , Inselstraße 22, D-04103 Leipzig , Germany.,f Department of Theoretical Chemistry , University of Vienna , Währingerstraße 17, A-1090 Wien , Austria.,g Center for non-coding RNA in Technology and Health , University of Copenhagen , Grønnegårdsvej 3, DK-1870 Frederiksberg C , Denmark.,h Santa Fe Institute , 1399 Hyde Park Rd., Santa Fe , NM 87501 , USA
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16
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Brogie JE, Price DH. Reconstitution of a functional 7SK snRNP. Nucleic Acids Res 2017; 45:6864-6880. [PMID: 28431135 PMCID: PMC5499737 DOI: 10.1093/nar/gkx262] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 04/11/2017] [Indexed: 01/29/2023] Open
Abstract
The 7SK small nuclear ribonucleoprotein (snRNP) plays a central role in RNA polymerase II elongation control by regulating the availability of active P-TEFb. We optimized conditions for analyzing 7SK RNA by SHAPE and demonstrated a hysteretic effect of magnesium on 7SK folding dynamics including a 7SK GAUC motif switch. We also found evidence that the 5΄ end pairs alternatively with two different regions of 7SK giving rise to open and closed forms that dictate the state of the 7SK motif. We then used recombinant P-TEFb, HEXIM1, LARP7 and MEPCE to reconstruct a functional 7SK snRNP in vitro. Stably associated P-TEFb was highly inhibited, but could still be released and activated by HIV-1 Tat. Notably, P-TEFb association with both in vitro-reconstituted and cellular snRNPs led to similar changes in SHAPE reactivities, confirming that 7SK undergoes a P-TEFb-dependent structural change. We determined that the xRRM of LARP7 binds to the 3΄ stem loop of 7SK and inhibits the methyltransferase activity of MEPCE through a C-terminal MEPCE interaction domain (MID). Inhibition of MEPCE is dependent on the structure of the 3΄ stem loop and the closed form of 7SK RNA. This study provides important insights into intramolecular interactions within the 7SK snRNP.
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Affiliation(s)
- John E Brogie
- Biochemistry Department, University of Iowa, Iowa City, IA 52242, USA
| | - David H Price
- Biochemistry Department, University of Iowa, Iowa City, IA 52242, USA
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17
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Affiliation(s)
- Uri Mbonye
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106
| | - Jonathan Karn
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106
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18
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Martinez-Zapien D, Legrand P, McEwen AG, Proux F, Cragnolini T, Pasquali S, Dock-Bregeon AC. The crystal structure of the 5΄ functional domain of the transcription riboregulator 7SK. Nucleic Acids Res 2017; 45:3568-3579. [PMID: 28082395 PMCID: PMC5389472 DOI: 10.1093/nar/gkw1351] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 01/06/2017] [Indexed: 12/22/2022] Open
Abstract
In vertebrates, the 7SK RNA forms the scaffold of a complex, which regulates transcription pausing of RNA-polymerase II. By binding to the HEXIM protein, the complex comprising proteins LARP7 and MePCE captures the positive transcription elongation factor P-TEFb and prevents phosphorylation of pausing factors. The HEXIM-binding site embedded in the 5΄-hairpin of 7SK (HP1) encompasses a short signature sequence, a GAUC repeat framed by single-stranded uridines. The present crystal structure of HP1 shows a remarkably straight helical stack involving several unexpected triples formed at a central region. Surprisingly, two uridines of the signature sequence make triple interactions in the major groove of the (GAUC)2. The third uridine is turned outwards or inward, wedging between the other uridines, thus filling the major groove. A molecular dynamics simulation indicates that these two conformations of the signature sequence represent stable alternatives. Analyses of the interaction with the HEXIM protein confirm the importance of the triple interactions at the signature sequence. Altogether, the present structural analysis of 7SK HP1 highlights an original mechanism of swapping bases, which could represent a possible ‘7SK signature’ and provides new insight into the functional importance of the plasticity of RNA.
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Affiliation(s)
- Denise Martinez-Zapien
- Biotechnologie et signalisation cellulaire, CNRS UMR 7242, Ecole Supérieure de Biotechnologie de Strasbourg, F-67412 Illkirch, France
| | - Pierre Legrand
- Synchrotron SOLEIL, L'Orme des Merisiers, F-91190 Gif-sur-Yvette, France
| | - Alastair G McEwen
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France.,Centre National de la Recherche Scientifique, UMR7104, Illkirch, France.,Institut National de la Santé et de la Recherche Médicale, U964, Illkirch, France.,Université de Strasbourg, Illkirch, France
| | - Florence Proux
- Department of functional genomics, CNRS UMR 8197, Institut de Biologie de l΄Ecole Normale Supérieure F-75005 Paris, France.,Department of functional genomics, INSERM-U1024, Institut de Biologie de l΄Ecole Normale Supérieure F-75005 Paris, France
| | | | - Samuela Pasquali
- Laboratoire de Biochimie Théorique, IBPC, CNRS UPR 9080, Université Sorbonne Paris Cite, Paris Diderot, 75005 Paris, France
| | - Anne-Catherine Dock-Bregeon
- Department of functional genomics, CNRS UMR 8197, Institut de Biologie de l΄Ecole Normale Supérieure F-75005 Paris, France.,Department of functional genomics, INSERM-U1024, Institut de Biologie de l΄Ecole Normale Supérieure F-75005 Paris, France.,Sorbonne Universités UPMC, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff cedex, France.,CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff cedex, France
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