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Villagomez-Bernabe B, Chan SW, Coulter JA, Roseman AM, Currell FJ. Fast Ion-Beam Inactivation of Viruses, Where Radiation Track Structure Meets RNA Structural Biology. Radiat Res 2022; 198:68-80. [PMID: 35436347 DOI: 10.1667/rade-21-00133.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 03/17/2022] [Indexed: 11/03/2022]
Abstract
Here we show an interplay between the structures present in ionization tracks and nucleocapsid RNA structural biology, using fast ion-beam inactivation of the severe acute respiratory syndrome coronavirus (SARS-CoV) virion as an example. This interplay could be a key factor in predicting dose-inactivation curves for high-energy ion-beam inactivation of virions. We also investigate the adaptation of well-established cross-section data derived from radiation interactions with water to the interactions involving the components of a virion, going beyond the density-scaling approximation developed previously. We conclude that solving one of the grand challenges of structural biology - the determination of RNA tertiary/quaternary structure - is linked to predicting ion-beam inactivation of viruses and that the two problems can be mutually informative. Indeed, our simulations show that fast ion beams have a key role to play in elucidating RNA tertiary/quaternary structure.
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Affiliation(s)
- B Villagomez-Bernabe
- The Dalton Cumbrian Facility and the Department of Chemistry, The University of Manchester, Westlakes Science & Technology Park, Moor Row, Cumbria, CA24 3HA, United Kingdom
| | - S W Chan
- School of Biological Sciences, Faculty of Biology, Medicine & Health, The University of Manchester, Manchester Academic Health Science Centre, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PL, United Kingdom
| | - J A Coulter
- School of Pharmacy, Queen's University Belfast, Belfast, BT9 7BL
| | - A M Roseman
- School of Biological Sciences, Faculty of Biology, Medicine & Health, The University of Manchester, Manchester Academic Health Science Centre, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PL, United Kingdom
| | - F J Currell
- The Dalton Cumbrian Facility and the Department of Chemistry, The University of Manchester, Westlakes Science & Technology Park, Moor Row, Cumbria, CA24 3HA, United Kingdom
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52
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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: 18] [Impact Index Per Article: 9.0] [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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53
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Carrascoza F, Antczak M, Miao Z, Westhof E, Szachniuk M. Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions. RNA (NEW YORK, N.Y.) 2022; 28:250-262. [PMID: 34819324 PMCID: PMC8906551 DOI: 10.1261/rna.078685.121] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.
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Affiliation(s)
- Francisco Carrascoza
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- 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 200081, China
| | - Eric Westhof
- Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire CNRS, Architecture et Réactivité de l'ARN, 67084 Strasbourg, France
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
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54
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Dingle K, Ghaddar F, Šulc P, Louis AA. Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes. Mol Biol Evol 2022; 39:msab280. [PMID: 34542628 PMCID: PMC8763027 DOI: 10.1093/molbev/msab280] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Morphospaces-representations of phenotypic characteristics-are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or "findability constraint," which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to "find."
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Affiliation(s)
- Kamaludin Dingle
- Centre for Applied Mathematics and Bioinformatics, Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally, Kuwait
| | - Fatme Ghaddar
- Centre for Applied Mathematics and Bioinformatics, Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally, Kuwait
| | - Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics at the Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom
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55
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Zhao J, Kennedy SD, Turner DH. Nuclear Magnetic Resonance Spectra and AMBER OL3 and ROC-RNA Simulations of UCUCGU Reveal Force Field Strengths and Weaknesses for Single-Stranded RNA. J Chem Theory Comput 2022; 18:1241-1254. [PMID: 34990548 DOI: 10.1021/acs.jctc.1c00643] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Single-stranded regions of RNA are important for folding of sequences into 3D structures and for design of therapeutics targeting RNA. Prediction of ensembles of 3D structures for single-stranded regions often involves classical mechanical approximations of interactions defined by quantum mechanical calculations on small model systems. Nuclear magnetic resonance (NMR) spectra and molecular dynamics (MD) simulations of short single strands provide tests for how well the approximations model many of the interactions. Here, the NMR spectra for UCUCGU at 2, 15, and 30 °C are compared to simulations with the AMBER force fields, OL3 and ROC-RNA. This is the first such comparison to an oligoribonucleotide containing an internal guanosine nucleotide (G). G is particularly interesting because of its many H-bonding groups, large dipole moment, and proclivity for both syn and anti conformations. Results reveal formation of a G amino to phosphate non-bridging oxygen H-bond. The results also demonstrate dramatic differences in details of the predicted structures. The variations emphasize the dependence of predictions on individual parameters and their balance with the rest of the force field. The NMR data can serve as a benchmark for future force fields.
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56
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rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J 2022; 121:142-156. [PMID: 34798137 PMCID: PMC8758408 DOI: 10.1016/j.bpj.2021.11.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.
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57
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Zerihun MB, Pucci F, Schug A. CoCoNet-boosting RNA contact prediction by convolutional neural networks. Nucleic Acids Res 2021; 49:12661-12672. [PMID: 34871451 PMCID: PMC8682773 DOI: 10.1093/nar/gkab1144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/27/2021] [Accepted: 11/05/2021] [Indexed: 11/24/2022] Open
Abstract
Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins. Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps. We introduce an algorithm called CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the positive predictive value (PPV) of predicted contacts by about 70% with respect to DCA as tested by cross-validation of about eighty RNA structures. However, the direct inclusion of the CoCoNet contacts in 3D modeling tools does not result in a proportional increase of the 3D RNA structure prediction accuracy. Therefore, we suggest that the field develops, in addition to contact PPV, metrics which estimate the expected impact for 3D structure modeling tools better. CoCoNet is freely available and can be found at https://github.com/KIT-MBS/coconet.
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Affiliation(s)
- Mehari B Zerihun
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Fabrizio Pucci
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Computational Biology and Bioinformatics, Université Libre de Bruxelles 1050, Brussels, Belgium
| | - Alexander Schug
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Biology, University of Duisburg-Essen, 45117 Essen, Germany
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58
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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59
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De Bisschop G, Allouche D, Frezza E, Masquida B, Ponty Y, Will S, Sargueil B. Progress toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure. Noncoding RNA 2021; 7:71. [PMID: 34842779 PMCID: PMC8628965 DOI: 10.3390/ncrna7040071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/29/2021] [Accepted: 11/02/2021] [Indexed: 01/04/2023] Open
Abstract
As more sequencing data accumulate and novel puzzling genetic regulations are discovered, the need for accurate automated modeling of RNA structure increases. RNA structure modeling from chemical probing experiments has made tremendous progress, however accurately predicting large RNA structures is still challenging for several reasons: RNA are inherently flexible and often adopt many energetically similar structures, which are not reliably distinguished by the available, incomplete thermodynamic model. Moreover, computationally, the problem is aggravated by the relevance of pseudoknots and non-canonical base pairs, which are hardly predicted efficiently. To identify nucleotides involved in pseudoknots and non-canonical interactions, we scrutinized the SHAPE reactivity of each nucleotide of the 188 nt long lariat-capping ribozyme under multiple conditions. Reactivities analyzed in the light of the X-ray structure were shown to report accurately the nucleotide status. Those that seemed paradoxical were rationalized by the nucleotide behavior along molecular dynamic simulations. We show that valuable information on intricate interactions can be deduced from probing with different reagents, and in the presence or absence of Mg2+. Furthermore, probing at increasing temperature was remarkably efficient at pointing to non-canonical interactions and pseudoknot pairings. The possibilities of following such strategies to inform structure modeling software are discussed.
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Affiliation(s)
- Grégoire De Bisschop
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada
| | - Delphine Allouche
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
- Institut Necker-Enfants Malades (INEM), Inserm U1151, 156 rue de Vaugirard, CEDEX 15, 75015 Paris, France
| | - Elisa Frezza
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
| | - Benoît Masquida
- Université de Strasbourg, CNRS UMR7156 GMGM, 67084 Strasbourg, France;
| | - Yann Ponty
- Ecole Polytechnique, CNRS UMR 7161, LIX, 91120 Palaiseau, France; (Y.P.); (S.W.)
| | - Sebastian Will
- Ecole Polytechnique, CNRS UMR 7161, LIX, 91120 Palaiseau, France; (Y.P.); (S.W.)
| | - Bruno Sargueil
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
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60
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Zhang D, Chen SJ, Zhou R. Modeling Noncanonical RNA Base Pairs by a Coarse-Grained IsRNA2 Model. J Phys Chem B 2021; 125:11907-11915. [PMID: 34694128 DOI: 10.1021/acs.jpcb.1c07288] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Noncanonical base pairs contribute crucially to the three-dimensional architecture of large RNA molecules; however, how to accurately model them remains an open challenge in RNA 3D structure prediction. Here, we report a promising coarse-grained (CG) IsRNA2 model to predict noncanonical base pairs in large RNAs through molecular dynamics simulations. By introducing a five-bead per nucleotide CG representation to reserve the three interacting edges of nucleobases, IsRNA2 accurately models various base-pairing interactions, including both canonical and noncanonical base pairs. A benchmark test indicated that IsRNA2 achieves a comparable performance to the atomic model in de novo modeling of noncanonical RNA structures. In addition, IsRNA2 was able to refine the 3D structure predictions for large RNAs in RNA-puzzle challenges. Finally, the graphics processing unit acceleration was introduced to speed up the sampling efficiency in IsRNA2 for very large RNA molecules. Therefore, the CG IsRNA2 model reported here offers a reliable approach to predict the structures and dynamics of large RNAs.
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Affiliation(s)
- Dong Zhang
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou 310058, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Ruhong Zhou
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou 310058, China
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61
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Fairman CW, Lever AML, Kenyon JC. Evaluating RNA Structural Flexibility: Viruses Lead the Way. Viruses 2021; 13:v13112130. [PMID: 34834937 PMCID: PMC8624864 DOI: 10.3390/v13112130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 12/11/2022] Open
Abstract
Our understanding of RNA structure has lagged behind that of proteins and most other biological polymers, largely because of its ability to adopt multiple, and often very different, functional conformations within a single molecule. Flexibility and multifunctionality appear to be its hallmarks. Conventional biochemical and biophysical techniques all have limitations in solving RNA structure and to address this in recent years we have seen the emergence of a wide diversity of techniques applied to RNA structural analysis and an accompanying appreciation of its ubiquity and versatility. Viral RNA is a particularly productive area to study in that this economy of function within a single molecule admirably suits the minimalist lifestyle of viruses. Here, we review the major techniques that are being used to elucidate RNA conformational flexibility and exemplify how the structure and function are, as in all biology, tightly linked.
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Affiliation(s)
| | - Andrew M. L. Lever
- Department of Medicine, Cambridge University, Level 5, Addenbrookes’ Hospital (Box 157), Cambridge CB2 0QQ, UK
- Correspondence: (A.M.L.L.); (J.C.K.); Tel.: +44-(0)-1223-747308 (A.M.L.L. & J.C.K.)
| | - Julia C. Kenyon
- Homerton College, University of Cambridge, Cambridge CB2 8PH, UK;
- Department of Medicine, Cambridge University, Level 5, Addenbrookes’ Hospital (Box 157), Cambridge CB2 0QQ, UK
- Correspondence: (A.M.L.L.); (J.C.K.); Tel.: +44-(0)-1223-747308 (A.M.L.L. & J.C.K.)
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62
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Mazzanti L, Alferkh L, Frezza E, Pasquali S. Biasing RNA Coarse-Grained Folding Simulations with Small-Angle X-ray Scattering Data. J Chem Theory Comput 2021; 17:6509-6521. [PMID: 34506136 DOI: 10.1021/acs.jctc.1c00441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
RNA molecules can easily adopt alternative structures in response to different environmental conditions. As a result, a molecule's energy landscape is rough and can exhibit a multitude of deep basins. In the absence of a high-resolution structure, small-angle X-ray scattering data (SAXS) can narrow down the conformational space available to the molecule and be used in conjunction with physical modeling to obtain high-resolution putative structures to be further tested by experiments. Because of the low resolution of these data, it is natural to implement the integration of SAXS data into simulations using a coarse-grained representation of the molecule, allowing for much wider searches and faster evaluation of SAXS theoretical intensity curves than with atomistic models. We present here the theoretical framework and the implementation of a simulation approach based on our coarse-grained model HiRE-RNA combined with SAXS evaluations "on-the-fly" leading the simulation toward conformations agreeing with the scattering data, starting from partially folded structures as the ones that can easily be obtained from secondary structure prediction-based tools. We show on three benchmark systems how our approach can successfully achieve high-resolution structures with remarkable similarity with the native structure recovering not only the overall shape, as imposed by SAXS data, but also the details of initially missing base pairs.
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Affiliation(s)
- Liuba Mazzanti
- Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, 4 Avenue de l'observatoire, 75006 Paris, France
| | - Lina Alferkh
- Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, 4 Avenue de l'observatoire, 75006 Paris, France
| | - Elisa Frezza
- Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, 4 Avenue de l'observatoire, 75006 Paris, France
| | - Samuela Pasquali
- Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, 4 Avenue de l'observatoire, 75006 Paris, France
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63
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Popenda M, Zok T, Sarzynska J, Korpeta A, Adamiak R, Antczak M, Szachniuk M. Entanglements of structure elements revealed in RNA 3D models. Nucleic Acids Res 2021; 49:9625-9632. [PMID: 34432024 PMCID: PMC8464073 DOI: 10.1093/nar/gkab716] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/02/2021] [Accepted: 08/06/2021] [Indexed: 01/14/2023] Open
Abstract
Computational methods to predict RNA 3D structure have more and more practical applications in molecular biology and medicine. Therefore, it is crucial to intensify efforts to improve the accuracy and quality of predicted three-dimensional structures. A significant role in this is played by the RNA-Puzzles initiative that collects, evaluates, and shares RNAs built computationally within currently nearly 30 challenges. RNA-Puzzles datasets, subjected to multi-criteria analysis, allow revealing the strengths and weaknesses of computer prediction methods. Here, we study the issue of entangled RNA fragments in the predicted RNA 3D structure models. By entanglement, we mean an arrangement of two structural elements such that one of them passes through the other. We propose the classification of entanglements driven by their topology and components. It distinguishes two general classes, interlaces and lassos, and subclasses characterized by element types-loops, dinucleotide steps, open single-stranded fragments-and puncture multiplicity. Our computational pipeline for entanglement detection, applied for 1,017 non-redundant models from RNA-Puzzles, has shown the frequency of different entanglements and allowed identifying 138 structures with intersected assemblies.
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Affiliation(s)
- Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Agnieszka Korpeta
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Ryszard W Adamiak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
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64
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Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Geometric deep learning of RNA structure. Science 2021; 373:1047-1051. [PMID: 34446608 PMCID: PMC9829186 DOI: 10.1126/science.abe5650] [Citation(s) in RCA: 155] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 07/14/2021] [Indexed: 01/28/2023]
Abstract
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
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Affiliation(s)
| | - Stephan Eismann
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Andrew M Watkins
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Ramya Rangan
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Masha Karelina
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, CA, USA.
- Department of Physics, Stanford University, Stanford, CA, 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
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65
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Abstract
Machine learning is poised to transform RNA structure and function discovery
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Affiliation(s)
- Kevin M Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA.
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66
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Robin X, Haas J, Gumienny R, Smolinski A, Tauriello G, Schwede T. Continuous Automated Model EvaluatiOn (CAMEO)-Perspectives on the future of fully automated evaluation of structure prediction methods. Proteins 2021; 89:1977-1986. [PMID: 34387007 PMCID: PMC8673552 DOI: 10.1002/prot.26213] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/05/2021] [Accepted: 08/07/2021] [Indexed: 11/18/2022]
Abstract
The Continuous Automated Model EvaluatiOn (CAMEO) platform complements the biennial CASP experiment by conducting fully automated blind evaluations of three‐dimensional protein prediction servers based on the weekly prerelease of sequences of those structures, which are going to be published in the upcoming release of the Protein Data Bank. While in CASP14, significant success was observed in predicting the structures of individual protein chains with high accuracy, significant challenges remain in correctly predicting the structures of complexes. By implementing fully automated evaluation of predictions for protein–protein complexes, as well as for proteins in complex with ligands, peptides, nucleic acids, or proteins containing noncanonical amino acid residues, CAMEO will assist new developments in those challenging areas of active research.
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Affiliation(s)
- Xavier Robin
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Juergen Haas
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Rafal Gumienny
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Smolinski
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland.,Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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67
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Schlick T, Zhu Q, Dey A, Jain S, Yan S, Laederach A. To Knot or Not to Knot: Multiple Conformations of the SARS-CoV-2 Frameshifting RNA Element. J Am Chem Soc 2021; 143:11404-11422. [PMID: 34283611 PMCID: PMC8315264 DOI: 10.1021/jacs.1c03003] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The SARS-CoV-2 frameshifting RNA element (FSE) is an excellent target for therapeutic intervention against Covid-19. This small gene element employs a shifting mechanism to pause and backtrack the ribosome during translation between Open Reading Frames 1a and 1b, which code for viral polyproteins. Any interference with this process has a profound effect on viral replication and propagation. Pinpointing the structures adapted by the FSE and associated structural transformations involved in frameshifting has been a challenge. Using our graph-theory-based modeling tools for representing RNA secondary structures, "RAG" (RNA-As-Graphs), and chemical structure probing experiments, we show that the 3-stem H-type pseudoknot (3_6 dual graph), long assumed to be the dominant structure, has a viable alternative, an HL-type 3-stem pseudoknot (3_3) for longer constructs. In addition, an unknotted 3-way junction RNA (3_5) emerges as a minor conformation. These three conformations share Stems 1 and 3, while the different Stem 2 may be involved in a conformational switch and possibly associations with the ribosome during translation. For full-length genomes, a stem-loop motif (2_2) may compete with these forms. These structural and mechanistic insights advance our understanding of the SARS-CoV-2 frameshifting process and concomitant virus life cycle, and point to three avenues of therapeutic intervention.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, New York 10003, United States
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York 10012, United States
- New York University-East China Normal University Center for Computational Chemistry, New York University-Shanghai, Shanghai 200062, P. R. China
| | - Qiyao Zhu
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York 10012, United States
| | - Abhishek Dey
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Swati Jain
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, New York 10003, United States
| | - Shuting Yan
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, New York 10003, United States
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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68
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Schlick T, Zhu Q, Dey A, Jain S, Yan S, Laederach A. To knot or not to knot: Multiple conformations of the SARS-CoV-2 frameshifting RNA element. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.03.31.437955. [PMID: 33821274 PMCID: PMC8020974 DOI: 10.1101/2021.03.31.437955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The SARS-CoV-2 frameshifting RNA element (FSE) is an excellent target for therapeutic intervention against Covid-19. This small gene element employs a shifting mechanism to pause and backtrack the ribosome during translation between Open Reading Frames 1a and 1b, which code for viral polyproteins. Any interference with this process has profound effect on viral replication and propagation. Pinpointing the structures adapted by the FSE and associated structural transformations involved in frameshifting has been a challenge. Using our graph-theory-based modeling tools for representing RNA secondary structures, "RAG" (RNA-As-Graphs), and chemical structure probing experiments, we show that the 3-stem H-type pseudoknot (3_6 dual graph), long assumed to be the dominant structure has a viable alternative, an HL-type 3-stem pseudoknot (3_3) for longer constructs. In addition, an unknotted 3-way junction RNA (3_5) emerges as a minor conformation. These three conformations share Stems 1 and 3, while the different Stem 2 may be involved in a conformational switch and possibly associations with the ribosome during translation. For full-length genomes, a stem-loop motif (2_2) may compete with these forms. These structural and mechanistic insights advance our understanding of the SARS-CoV-2 frameshifting process and concomitant virus life cycle, and point to three avenues of therapeutic intervention.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003 U.S.A
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012 U.S.A
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, P.R. China
| | - Qiyao Zhu
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012 U.S.A
| | - Abhishek Dey
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Swati Jain
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003 U.S.A
| | - Shuting Yan
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003 U.S.A
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
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69
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Christy TW, Giannetti CA, Houlihan G, Smola MJ, Rice GM, Wang J, Dokholyan NV, Laederach A, Holliger P, Weeks KM. Direct Mapping of Higher-Order RNA Interactions by SHAPE-JuMP. Biochemistry 2021; 60:1971-1982. [PMID: 34121404 PMCID: PMC8256721 DOI: 10.1021/acs.biochem.1c00270] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Higher-order structure governs function for many RNAs. However, discerning this structure for large RNA molecules in solution is an unresolved challenge. Here, we present SHAPE-JuMP (selective 2'-hydroxyl acylation analyzed by primer extension and juxtaposed merged pairs) to interrogate through-space RNA tertiary interactions. A bifunctional small molecule is used to chemically link proximal nucleotides in an RNA structure. The RNA cross-link site is then encoded into complementary DNA (cDNA) in a single, direct step using an engineered reverse transcriptase that "jumps" across cross-linked nucleotides. The resulting cDNAs contain a deletion relative to the native RNA sequence, which can be detected by sequencing, that indicates the sites of cross-linked nucleotides. SHAPE-JuMP measures RNA tertiary structure proximity concisely across large RNA molecules at nanometer resolution. SHAPE-JuMP is especially effective at measuring interactions in multihelix junctions and loop-to-helix packing, enables modeling of the global fold for RNAs up to several hundred nucleotides in length, facilitates ranking of structural models by consistency with through-space restraints, and is poised to enable solution-phase structural interrogation and modeling of complex RNAs.
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Affiliation(s)
- Thomas W. Christy
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Catherine A. Giannetti
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Gillian Houlihan
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Matthew J. Smola
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Greggory M. Rice
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Jian Wang
- Departments of Pharmacology, and Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
| | - Nikolay V. Dokholyan
- Departments of Pharmacology, and Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
- Departments of Chemistry, and Biomedical Engineering, Pennsylvania State University, University Park, PA 16802
| | - Alain Laederach
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Philipp Holliger
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Kevin M. Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
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70
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Singh J, Paliwal K, Singh J, Zhou Y. RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks. J Chem Inf Model 2021; 61:2610-2622. [PMID: 34037398 DOI: 10.1021/acs.jcim.1c00153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to paired bases, and the conformational space of the ribose-phosphate backbone is still too large to be sampled efficiently. Here, we employed the dilated convolutional neural network to predict backbone torsion and pseudotorsion angles using a single RNA sequence as input. The method called SPOT-RNA-1D was trained on a high-resolution training data set and tested on three independent, nonredundant, and high-resolution test sets. The proposed method yields substantially smaller mean absolute errors than the baseline predictors based on random predictions and based on helix conformations according to actual angle distributions. The mean absolute errors for three test sets range from 14°-44° for different angles, compared to 17°-62° by random prediction and 14°-58° by helix prediction. The method also accurately recovers the overall patterns of single or pairwise angle distributions. In general, torsion angles further away from the bases and associated with unpaired bases and paired bases involved in tertiary interactions are more difficult to predict. Compared to the best models in RNA-puzzles experiments, SPOT-RNA-1D yielded more accurate dihedral angles and, thus, are potentially useful as model quality indicators and restraints for RNA structure prediction as in protein structure prediction.
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Affiliation(s)
- Jaswinder Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Peking University Shenzhen Graduate School, Shenzhen 518055, P.R. China
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71
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Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement. Nat Commun 2021; 12:2777. [PMID: 33986288 PMCID: PMC8119458 DOI: 10.1038/s41467-021-23100-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/13/2021] [Indexed: 12/04/2022] Open
Abstract
Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.
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72
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Thavarajah W, Hertz LM, Bushhouse DZ, Archuleta CM, Lucks JB. RNA Engineering for Public Health: Innovations in RNA-Based Diagnostics and Therapeutics. Annu Rev Chem Biomol Eng 2021; 12:263-286. [PMID: 33900805 PMCID: PMC9714562 DOI: 10.1146/annurev-chembioeng-101420-014055] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
RNA is essential for cellular function: From sensing intra- and extracellular signals to controlling gene expression, RNA mediates a diverse and expansive list of molecular processes. A long-standing goal of synthetic biology has been to develop RNA engineering principles that can be used to harness and reprogram these RNA-mediated processes to engineer biological systems to solve pressing global challenges. Recent advances in the field of RNA engineering are bringing this to fruition, enabling the creation of RNA-based tools to combat some of the most urgent public health crises. Specifically, new diagnostics using engineered RNAs are able to detect both pathogens and chemicals while generating an easily detectable fluorescent signal as an indicator. New classes of vaccines and therapeutics are also using engineered RNAs to target a wide range of genetic and pathogenic diseases. Here, we discuss the recent breakthroughs in RNA engineering enabling these innovations and examine how advances in RNA design promise to accelerate the impact of engineered RNA systems.
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Affiliation(s)
- Walter Thavarajah
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA
| | - Laura M Hertz
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, Illinois 60208, USA
| | - David Z Bushhouse
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, Illinois 60208, USA
| | - Chloé M Archuleta
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA
| | - Julius B Lucks
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA.,Center for Engineering Sustainability and Resilience, Northwestern University, Evanston, Illinois 60208, USA
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73
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Ma J, Saikia N, Godar S, Hamilton GL, Ding F, Alper J, Sanabria H. Ensemble Switching Unveils a Kinetic Rheostat Mechanism of the Eukaryotic Thiamine Pyrophosphate Riboswitch. RNA (NEW YORK, N.Y.) 2021; 27:rna.075937.120. [PMID: 33863818 PMCID: PMC8208051 DOI: 10.1261/rna.075937.120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 04/13/2021] [Indexed: 05/05/2023]
Abstract
Thiamine pyrophosphate (TPP) riboswitches regulate thiamine metabolism by inhibiting the translation of enzymes essential to thiamine synthesis pathways upon binding to thiamine pyrophosphate in cells across all domains of life. Recent work on the Arabidopsis thaliana TPP riboswitch suggests a multi-step TPP binding process involving multiple riboswitch configurational ensembles and that Mg2+ dependence underlies the mechanism of TPP recognition and subsequent transition to the expression-inhibiting state of the aptamer domain followed by changes in the expression platform. However, details of the relationship between TPP riboswitch conformational changes and interactions with TPP and Mg2+ ¬¬in the aptamer domain constituting this mechanism are unknown. Therefore, we integrated single-molecule multiparameter fluorescence and force spectroscopy with atomistic molecular dynamics simulations and found that conformational transitions within the aptamer domain's sensor helices associated with TPP and Mg2+ ligand binding occurred between at least five different ensembles on timescales ranging from µs to ms. These dynamics are orders of magnitude faster than the 10 second-timescale folding kinetics associated with expression-state switching in the switch sequence. Together, our results show that a TPP and Mg2+ dependent mechanism determines dynamic configurational state ensemble switching of the aptamer domain's sensor helices that regulates the stability of the switch helix, which ultimately may lead to the expression-inhibiting state of the riboswitch. Additionally, we propose that two pathways exist for ligand recognition and that this mechanism underlies a kinetic rheostat-like behavior of the Arabidopsis thaliana TPP riboswitch.
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Affiliation(s)
- Junyan Ma
- Department of Chemistry, Clemson University
| | | | - Subash Godar
- Department of Physics and Astronomy, Clemson University
| | | | - Feng Ding
- Department of Physics and Astronomy, Clemson University
| | - Joshua Alper
- Department of Physics and Astronomy, Clemson University
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University
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74
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An RNA-centric historical narrative around the Protein Data Bank. J Biol Chem 2021; 296:100555. [PMID: 33744291 PMCID: PMC8080527 DOI: 10.1016/j.jbc.2021.100555] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 02/17/2021] [Accepted: 03/16/2021] [Indexed: 01/06/2023] Open
Abstract
Some of the amazing contributions brought to the scientific community by the Protein Data Bank (PDB) are described. The focus is on nucleic acid structures with a bias toward RNA. The evolution and key roles in science of the PDB and other structural databases for nucleic acids illustrate how small initial ideas can become huge and indispensable resources with the unflinching willingness of scientists to cooperate globally. The progress in the understanding of the molecular interactions driving RNA architectures followed the rapid increase in RNA structures in the PDB. That increase was consecutive to improvements in chemical synthesis and purification of RNA molecules, as well as in biophysical methods for structure determination and computer technology. The RNA modeling efforts from the early beginnings are also described together with their links to the state of structural knowledge and technological development. Structures of RNA and of its assemblies are physical objects, which, together with genomic data, allow us to integrate present-day biological functions and the historical evolution in all living species on earth.
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75
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Abdelsattar AS, Mansour Y, Aboul-Ela F. The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding. Chembiochem 2021; 22:1499-1516. [PMID: 33351206 DOI: 10.1002/cbic.202000695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Indexed: 12/24/2022]
Abstract
The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Youssef Mansour
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Fareed Aboul-Ela
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
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76
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Zhang D, Li J, Chen SJ. IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures. J Chem Theory Comput 2021; 17:1842-1857. [PMID: 33560836 DOI: 10.1021/acs.jctc.0c01148] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.
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Affiliation(s)
- Dong Zhang
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Jun Li
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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77
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Gomez-Casati DF, Busi MV, Barchiesi J, Pagani MA, Marchetti-Acosta NS, Terenzi A. Fe-S Protein Synthesis in Green Algae Mitochondria. PLANTS 2021; 10:plants10020200. [PMID: 33494487 PMCID: PMC7911964 DOI: 10.3390/plants10020200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/13/2021] [Accepted: 01/18/2021] [Indexed: 12/28/2022]
Abstract
Iron and sulfur are two essential elements for all organisms. These elements form the Fe-S clusters that are present as cofactors in numerous proteins and protein complexes related to key processes in cells, such as respiration and photosynthesis, and participate in numerous enzymatic reactions. In photosynthetic organisms, the ISC and SUF Fe-S cluster synthesis pathways are located in organelles, mitochondria, and chloroplasts, respectively. There is also a third biosynthetic machinery in the cytosol (CIA) that is dependent on the mitochondria for its function. The genes and proteins that participate in these assembly pathways have been described mainly in bacteria, yeasts, humans, and recently in higher plants. However, little is known about the proteins that participate in these processes in algae. This review work is mainly focused on releasing the information on the existence of genes and proteins of green algae (chlorophytes) that could participate in the assembly process of Fe-S groups, especially in the mitochondrial ISC and CIA pathways.
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Affiliation(s)
- Diego F. Gomez-Casati
- Correspondence: (D.F.G.-C.); (M.V.B.); Tel.: +54-341-4391955 (ext. 113) (D.F.G.-C. & M.V.B.)
| | - Maria V. Busi
- Correspondence: (D.F.G.-C.); (M.V.B.); Tel.: +54-341-4391955 (ext. 113) (D.F.G.-C. & M.V.B.)
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78
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Arriola JT, Müller UF. A combinatorial method to isolate short ribozymes from complex ribozyme libraries. Nucleic Acids Res 2020; 48:e116. [PMID: 33035338 PMCID: PMC7672470 DOI: 10.1093/nar/gkaa834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 08/28/2020] [Accepted: 10/01/2020] [Indexed: 11/13/2022] Open
Abstract
In vitro selections are the only known methods to generate catalytic RNAs (ribozymes) that do not exist in nature. Such new ribozymes are used as biochemical tools, or to address questions on early stages of life. In both cases, it is helpful to identify the shortest possible ribozymes since they are easier to deploy as a tool, and because they are more likely to have emerged in a prebiotic environment. One of our previous selection experiments led to a library containing hundreds of different ribozyme clusters that catalyze the triphosphorylation of their 5'-terminus. This selection showed that RNA systems can use the prebiotically plausible molecule cyclic trimetaphosphate as an energy source. From this selected ribozyme library, the shortest ribozyme that was previously identified had a length of 67 nucleotides. Here we describe a combinatorial method to identify short ribozymes from libraries containing many ribozymes. Using this protocol on the library of triphosphorylation ribozymes, we identified a 17-nucleotide sequence motif embedded in a 44-nucleotide pseudoknot structure. The described combinatorial approach can be used to analyze libraries obtained by different in vitro selection experiments.
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Affiliation(s)
- Joshua T Arriola
- Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Ulrich F Müller
- Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, USA
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79
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Girodat D, Pati AK, Terry DS, Blanchard SC, Sanbonmatsu KY. Quantitative comparison between sub-millisecond time resolution single-molecule FRET measurements and 10-second molecular simulations of a biosensor protein. PLoS Comput Biol 2020; 16:e1008293. [PMID: 33151943 PMCID: PMC7643941 DOI: 10.1371/journal.pcbi.1008293] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/27/2020] [Indexed: 12/15/2022] Open
Abstract
Molecular Dynamics (MD) simulations seek to provide atomic-level insights into conformationally dynamic biological systems at experimentally relevant time resolutions, such as those afforded by single-molecule fluorescence measurements. However, limitations in the time scales of MD simulations and the time resolution of single-molecule measurements have challenged efforts to obtain overlapping temporal regimes required for close quantitative comparisons. Achieving such overlap has the potential to provide novel theories, hypotheses, and interpretations that can inform idealized experimental designs that maximize the detection of the desired reaction coordinate. Here, we report MD simulations at time scales overlapping with in vitro single-molecule Förster (fluorescence) resonance energy transfer (smFRET) measurements of the amino acid binding protein LIV-BPSS at sub-millisecond resolution. Computationally efficient all-atom structure-based simulations, calibrated against explicit solvent simulations, were employed for sampling multiple cycles of LIV-BPSS clamshell-like conformational changes on the time scale of seconds, examining the relationship between these events and those observed by smFRET. The MD simulations agree with the smFRET measurements and provide valuable information on local dynamics of fluorophores at their sites of attachment on LIV-BPSS and the correlations between fluorophore motions and large-scale conformational changes between LIV-BPSS domains. We further utilize the MD simulations to inform the interpretation of smFRET data, including Förster radius (R0) and fluorophore orientation factor (κ2) determinations. The approach we describe can be readily extended to distinct biochemical systems, allowing for the interpretation of any FRET system conjugated to protein or ribonucleoprotein complexes, including those with more conformational processes, as well as those implementing multi-color smFRET. Förster (fluorescence) resonance energy transfer (FRET) has been used extensively by biophysicists as a molecular-scale ruler that yields fundamental structural and kinetic insights into transient processes including complex formation and conformational rearrangements required for biological function. FRET techniques require the identification of informative fluorophore labeling sites, spaced at defined distances to inform on a reaction coordinate of interest and consideration of noise sources that have the potential to obscure quantitative interpretations. Here, we describe an approach to leverage advancements in computationally efficient all-atom structure-based molecular dynamics simulations in which structural dynamics observed via FRET can be interpreted in full atomistic detail on commensurate time scales. We demonstrate the potential of this approach using a model FRET system, the amino acid binding protein LIV-BPSS conjugated to self-healing organic fluorophores. LIV-BPSS exhibits large scale, sub-millisecond clamshell-like conformational changes between open and closed conformations associated with ligand unbinding and binding, respectively. Our findings inform on the molecular basis of the dynamics observed by smFRET and on strategies to optimize fluorophore labeling sites, the manner of fluorophore attachment, and fluorophore composition.
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Affiliation(s)
- Dylan Girodat
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Avik K Pati
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Daniel S Terry
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Karissa Y Sanbonmatsu
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.,New Mexico Consortium, Los Alamos, New Mexico, United States of America
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80
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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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81
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Watkins AM, Rangan R, Das R. FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds. Structure 2020; 28:963-976.e6. [PMID: 32531203 PMCID: PMC7415647 DOI: 10.1016/j.str.2020.05.011] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/27/2020] [Accepted: 05/20/2020] [Indexed: 01/01/2023]
Abstract
Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.
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Affiliation(s)
- Andrew Martin Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Biophysics Program, Stanford University, Stanford, CA 94305, USA.
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