1
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Thiel BC, Bussi G, Poblete S, Hofacker IL. Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data. Nucleic Acids Res 2024:gkae602. [PMID: 39021350 DOI: 10.1093/nar/gkae602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/05/2024] [Indexed: 07/20/2024] Open
Abstract
The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.
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Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, via Bonomea 265, Trieste 34136, Italy
| | - Simón Poblete
- Centro BASAL Ciencia & Vida, Avenida del Valle Norte 725, Santiago 8580702, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
- Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna 1090, Austria
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2
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Tants JN, Schlundt A. Advances, Applications, and Perspectives in Small-Angle X-ray Scattering of RNA. Chembiochem 2023; 24:e202300110. [PMID: 37466350 DOI: 10.1002/cbic.202300110] [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: 02/10/2023] [Revised: 04/22/2023] [Indexed: 07/20/2023]
Abstract
RNAs exhibit a plethora of functions far beyond transmitting genetic information. Often, RNA functions are entailed in their structure, be it as a regulatory switch, protein binding site, or providing catalytic activity. Structural information is a prerequisite for a full understanding of RNA-regulatory mechanisms. Owing to the inherent dynamics, size, and instability of RNA, its structure determination remains challenging. Methods such as NMR spectroscopy, X-ray crystallography, and cryo-electron microscopy can provide high-resolution structures; however, their limitations make structure determination, even for small RNAs, cumbersome, if at all possible. Although at a low resolution, small-angle X-ray scattering (SAXS) has proven valuable in advancing structure determination of RNAs as a complementary method, which is also applicable to large-sized RNAs. Here, we review the technological and methodological advancements of RNA SAXS. We provide examples of the powerful inclusion of SAXS in structural biology and discuss possible future applications to large RNAs.
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Affiliation(s)
- Jan-Niklas Tants
- Goethe University Frankfurt, Institute for Molecular Biosciences and Biomagnetic Resonance Centre (BMRZ), Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Andreas Schlundt
- Goethe University Frankfurt, Institute for Molecular Biosciences and Biomagnetic Resonance Centre (BMRZ), Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
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3
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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: 3] [Impact Index Per Article: 3.0] [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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4
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Visualizing RNA conformational and architectural heterogeneity in solution. Nat Commun 2023; 14:714. [PMID: 36759615 PMCID: PMC9911696 DOI: 10.1038/s41467-023-36184-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
RNA flexibility is reflected in its heterogeneous conformation. Through direct visualization using atomic force microscopy (AFM) and the adenosylcobalamin riboswitch aptamer domain as an example, we show that a single RNA sequence folds into conformationally and architecturally heterogeneous structures under near-physiological solution conditions. Recapitulated 3D topological structures from AFM molecular surfaces reveal that all conformers share the same secondary structural elements. Only a population-weighted cohort, not any single conformer, including the crystal structure, can account for the ensemble behaviors observed by small-angle X-ray scattering (SAXS). All conformers except one are functionally active in terms of ligand binding. Our findings provide direct visual evidence that the sequence-structure relationship of RNA under physiologically relevant solution conditions is more complex than the one-to-one relationship for well-structured proteins. The direct visualization of conformational and architectural ensembles at the single-molecule level in solution may suggest new approaches to RNA structural analyses.
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Zhang J, Fei Y, Sun L, Zhang QC. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat Methods 2022; 19:1193-1207. [PMID: 36203019 DOI: 10.1038/s41592-022-01623-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.
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Affiliation(s)
- Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Yuhan Fei
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Lei Sun
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
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6
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Fang X, Gallego J, Wang YX. Deriving RNA topological structure from SAXS. Methods Enzymol 2022; 677:479-529. [DOI: 10.1016/bs.mie.2022.08.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Dynamic Structural Biology Experiments at XFEL or Synchrotron Sources. Methods Mol Biol 2021; 2305:203-228. [PMID: 33950392 DOI: 10.1007/978-1-0716-1406-8_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Macromolecular crystallography (MX) leverages the methods of physics and the language of chemistry to reveal fundamental insights into biology. Often beautifully artistic images present MX results to support profound functional hypotheses that are vital to entire life science research community. Over the past several decades, synchrotrons around the world have been the workhorses for X-ray diffraction data collection at many highly automated beamlines. The newest tools include X-ray-free electron lasers (XFELs) located at facilities in the USA, Japan, Korea, Switzerland, and Germany that deliver about nine orders of magnitude higher brightness in discrete femtosecond long pulses. At each of these facilities, new serial femtosecond crystallography (SFX) strategies exploit slurries of micron-size crystals by rapidly delivering individual crystals into the XFEL X-ray interaction region, from which one diffraction pattern is collected per crystal before it is destroyed by the intense X-ray pulse. Relatively simple adaptions to SFX methods produce time-resolved data collection strategies wherein reactions are triggered by visible light illumination or by chemical diffusion/mixing. Thus, XFELs provide new opportunities for high temporal and spatial resolution studies of systems engaged in function at physiological temperature. In this chapter, we summarize various issues related to microcrystal slurry preparation, sample delivery into the X-ray interaction region, and some emerging strategies for time-resolved SFX data collection.
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Li B, Cao Y, Westhof E, Miao Z. Advances in RNA 3D Structure Modeling Using Experimental Data. Front Genet 2020; 11:574485. [PMID: 33193680 PMCID: PMC7649352 DOI: 10.3389/fgene.2020.574485] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/02/2020] [Indexed: 12/26/2022] Open
Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
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Affiliation(s)
- Bing Li
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France
| | - Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
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Théobald-Dietrich A, de Wijn R, Rollet K, Bluhm A, Rudinger-Thirion J, Paulus C, Lorber B, Thureau A, Frugier M, Sauter C. Structural Analysis of RNA by Small-Angle X-ray Scattering. Methods Mol Biol 2020; 2113:189-215. [PMID: 32006316 DOI: 10.1007/978-1-0716-0278-2_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Over the past two decades small-angle X-ray scattering (SAXS) has become a popular method to characterize solutions of biomolecules including ribonucleic acid (RNA). In an integrative structural approach, SAXS is complementary to crystallography, NMR, and electron microscopy and provides information about RNA architecture and dynamics. This chapter highlights the practical advantages of combining size-exclusion chromatography and SAXS at synchrotron facilities. It is illustrated by practical case studies of samples ranging from single hairpins and tRNA to a large IRES. The emphasis is also put on sample preparation which is a critical step of SAXS analysis and on optimized protocols for in vitro RNA synthesis ensuring the production of mg amount of pure and homogeneous molecules.
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Affiliation(s)
- Anne Théobald-Dietrich
- Architecture et Réactivité de l'ARN, UPR 9002, IBMC, CNRS, Université de Strasbourg, Strasbourg, France
| | - Raphaël de Wijn
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | - Kévin Rollet
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | - Alexandra Bluhm
- Architecture et Réactivité de l'ARN, UPR 9002, IBMC, CNRS, Université de Strasbourg, Strasbourg, France
| | - Joëlle Rudinger-Thirion
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | - Caroline Paulus
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | - Bernard Lorber
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | | | - Magali Frugier
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France
| | - Claude Sauter
- Architecture et Réactivité de l'ARN - CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, Strasbourg, France.
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10
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Ponce-Salvatierra A, Astha, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM. Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 2019; 39:BSR20180430. [PMID: 30670629 PMCID: PMC6367127 DOI: 10.1042/bsr20180430] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 01/02/2023] Open
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
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Affiliation(s)
- Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Astha
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Katarzyna Merdas
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, Poznan PL-61-614, Poland
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11
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Dans PD, Gallego D, Balaceanu A, Darré L, Gómez H, Orozco M. Modeling, Simulations, and Bioinformatics at the Service of RNA Structure. Chem 2019. [DOI: 10.1016/j.chempr.2018.09.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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