1
|
Bussi G, Bonomi M, Gkeka P, Sattler M, Al-Hashimi HM, Auffinger P, Duca M, Foricher Y, Incarnato D, Jones AN, Kirmizialtin S, Krepl M, Orozco M, Palermo G, Pasquali S, Salmon L, Schwalbe H, Westhof E, Zacharias M. RNA dynamics from experimental and computational approaches. Structure 2024; 32:1281-1287. [PMID: 39241758 DOI: 10.1016/j.str.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 06/21/2024] [Accepted: 07/29/2024] [Indexed: 09/09/2024]
Abstract
Conformational dynamics is crucial for the biological function of RNA molecules and for their potential as therapeutic targets. This meeting report outlines key "take-home" messages that emerged from the presentations and discussions during the CECAM workshop "RNA dynamics from experimental and computational approaches" in Paris, June 26-28, 2023.
Collapse
Affiliation(s)
- Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy.
| | - Massimiliano Bonomi
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Computational Structural Biology Unit, Paris, France.
| | - Paraskevi Gkeka
- Integrated Drug Discovery, Molecular Design Sciences, Sanofi, Vitry-sur-Seine, France.
| | - Michael Sattler
- Technical University of Munich, Munich, Germany; Helmholtz Munich, Munich, Germany.
| | - Hashim M Al-Hashimi
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Pascal Auffinger
- Université de Strasbourg, Architecture et Réactivité de l'ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, 2 Allée Konrad Roentgen, 67084 Strasbourg, France
| | - Maria Duca
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France
| | - Yann Foricher
- Integrated Drug Discovery, Small Molecules Medicinal Chemistry, Sanofi, Vitry-sur-Seine, France
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands
| | - Alisha N Jones
- Department of Chemistry, New York University, New York, NY, USA
| | - Serdal Kirmizialtin
- Department of Chemistry, New York University, New York, NY, USA; Chemistry Program, Science Division, New York University, Abu Dhabi, United Arab Emirates
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, Brno 612 00, Czech Republic
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, and Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, The University of California, Riverside, Riverside, CA, USA
| | - Samuela Pasquali
- Laboratoire Biologie Fonctionnelle et Adaptative, CNRS UMR 8251 INSERM ERL 1133, Université Paris Cité, 35 rue Hélène Brion, 75013 Paris, France
| | - Loïc Salmon
- Centre de RMN à Très Hauts Champs, UMR 5082 (CNRS, École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1), University of Lyon, 69100 Villeurbanne, France
| | - Harald Schwalbe
- Institute for Organic Chemistry and Chemical Biology, Center for Biomolecular Magnetic Resonance, Goethe-University Frankfurt, 60438 Frankfurt/Main, Germany
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 67084 Strasbourg, France
| | - Martin Zacharias
- Physics Department and Center of Protein Assemblies, Technical University of Munich, Munich, Germany
| |
Collapse
|
2
|
Zhang S, Li J, Chen SJ. Machine learning in RNA structure prediction: Advances and challenges. Biophys J 2024; 123:2647-2657. [PMID: 38297836 DOI: 10.1016/j.bpj.2024.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.
Collapse
Affiliation(s)
- Sicheng Zhang
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Jun Li
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Shi-Jie Chen
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri; Department of Biochemistry, University of Missouri, Columbia, Missouri.
| |
Collapse
|
3
|
Fallah A, Havaei SA, Sedighian H, Kachuei R, Fooladi AAI. Prediction of aptamer affinity using an artificial intelligence approach. J Mater Chem B 2024. [PMID: 39158322 DOI: 10.1039/d4tb00909f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.
Collapse
Affiliation(s)
- Arezoo Fallah
- Department of Bacteriology and Virology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Seyed Asghar Havaei
- Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Hamid Sedighian
- Applied Microbiology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Reza Kachuei
- Molecular Biology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Abbas Ali Imani Fooladi
- Applied Microbiology Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
4
|
Linzer JT, Aminov E, Abdullah AS, Kirkup CE, Diaz Ventura RI, Bijoor VR, Jung J, Huang S, Tse CG, Álvarez Toucet E, Onghai HP, Ghosh AP, Grodzki AC, Haines ER, Iyer AS, Khalil MK, Leong AP, Neuhaus MA, Park J, Shahid A, Xie M, Ziembicki JM, Simmerling C, Nagan MC. Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment. J Chem Inf Model 2024; 64:6092-6104. [PMID: 39002142 DOI: 10.1021/acs.jcim.4c00756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.
Collapse
Affiliation(s)
- Jason T Linzer
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Ethan Aminov
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Aalim S Abdullah
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Colleen E Kirkup
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Rebeca I Diaz Ventura
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Vinay R Bijoor
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Jiyun Jung
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Sophie Huang
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Chi Gee Tse
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Emily Álvarez Toucet
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Hugo P Onghai
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Arghya P Ghosh
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alex C Grodzki
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Emilee R Haines
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Aditya S Iyer
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Mark K Khalil
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alexander P Leong
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Michael A Neuhaus
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Joseph Park
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Asir Shahid
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Matthew Xie
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Jan M Ziembicki
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Maria C Nagan
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| |
Collapse
|
5
|
Genna V, Reyes-Fraile L, Iglesias-Fernandez J, Orozco M. Nucleic acids in modern molecular therapies: A realm of opportunities for strategic drug design. Curr Opin Struct Biol 2024; 87:102838. [PMID: 38759298 DOI: 10.1016/j.sbi.2024.102838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/10/2024] [Accepted: 04/23/2024] [Indexed: 05/19/2024]
Abstract
RNA vaccines have made evident to society what was already known by the scientific community: nucleic acids will be the "drugs of the future." By modifying the genome, interfering in transcription or translation, and by introducing new catalysts into the cell or by mimicking antibody effects, nucleic acids can generate therapeutic activities that are not accessible by any other therapeutic agents. There are, however, challenges that need to be solved in the next few years to make nucleic acids usable in a wide range of therapeutic scenarios. This review illustrates how simulation methods can help achieve this goal.
Collapse
Affiliation(s)
- Vito Genna
- NBD|Nostrum Biodiscovery, Josep Tarradellas 8-10, Barcelona 08019, Spain. https://twitter.com/_VitoGenna_
| | - Laura Reyes-Fraile
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, Barcelona 08028, Spain; Sixfold Bioscience Ltd, Translational & Innovation Hub, 84 Wood Ln, London W12 0BZ, United Kingdom
| | | | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, Barcelona 08028, Spain; Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona 08028, Spain.
| |
Collapse
|
6
|
Nithin C, Kmiecik S, Błaszczyk R, Nowicka J, Tuszyńska I. Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions. Nucleic Acids Res 2024; 52:7465-7486. [PMID: 38917327 PMCID: PMC11260495 DOI: 10.1093/nar/gkae541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/23/2024] [Accepted: 06/16/2024] [Indexed: 06/27/2024] Open
Abstract
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.
Collapse
Affiliation(s)
- Chandran Nithin
- Molecure SA, 02-089 Warsaw, Poland
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, 02-089 Warsaw, Poland
| | - Sebastian Kmiecik
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, 02-089 Warsaw, Poland
| | | | | | | |
Collapse
|
7
|
Steffen FD, Cunha RA, Sigel RKO, Börner R. FRET-guided modeling of nucleic acids. Nucleic Acids Res 2024; 52:e59. [PMID: 38869063 PMCID: PMC11260485 DOI: 10.1093/nar/gkae496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/29/2024] [Indexed: 06/14/2024] Open
Abstract
The functional diversity of RNAs is encoded in their innate conformational heterogeneity. The combination of single-molecule spectroscopy and computational modeling offers new attractive opportunities to map structural transitions within nucleic acid ensembles. Here, we describe a framework to harmonize single-molecule Förster resonance energy transfer (FRET) measurements with molecular dynamics simulations and de novo structure prediction. Using either all-atom or implicit fluorophore modeling, we recreate FRET experiments in silico, visualize the underlying structural dynamics and quantify the reaction coordinates. Using multiple accessible-contact volumes as a post hoc scoring method for fragment assembly in Rosetta, we demonstrate that FRET can be used to filter a de novo RNA structure prediction ensemble by refuting models that are not compatible with in vitro FRET measurement. We benchmark our FRET-assisted modeling approach on double-labeled DNA strands and validate it against an intrinsically dynamic manganese(II)-binding riboswitch. We show that a FRET coordinate describing the assembly of a four-way junction allows our pipeline to recapitulate the global fold of the riboswitch displayed by the crystal structure. We conclude that computational fluorescence spectroscopy facilitates the interpretability of dynamic structural ensembles and improves the mechanistic understanding of nucleic acid interactions.
Collapse
Affiliation(s)
- Fabio D Steffen
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Richard A Cunha
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Roland K O Sigel
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Richard Börner
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| |
Collapse
|
8
|
Tarafder S, Bhattacharya D. lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.04.565599. [PMID: 37961488 PMCID: PMC10635153 DOI: 10.1101/2023.11.04.565599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.
Collapse
Affiliation(s)
- Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, 24061, USA
| | | |
Collapse
|
9
|
Moafinejad SN, de Aquino BRH, Boniecki M, Pandaranadar Jeyeram IN, Nikolaev G, Magnus M, Farsani M, Badepally N, Wirecki T, Stefaniak F, Bujnicki J. SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories. Nucleic Acids Res 2024; 52:W368-W373. [PMID: 38738621 PMCID: PMC11223799 DOI: 10.1093/nar/gkae356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/07/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024] Open
Abstract
Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.
Collapse
Affiliation(s)
- S Naeim Moafinejad
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Belisa R H de Aquino
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Michał J Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Iswarya P N Pandaranadar Jeyeram
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Grigory Nikolaev
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Marcin Magnus
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St, Cambridge, MA 02138, USA
| | - Masoud Amiri Farsani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Nagendar Goud Badepally
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| |
Collapse
|
10
|
Bernard C, Postic G, Ghannay S, Tahi F. State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction. NAR Genom Bioinform 2024; 6:lqae048. [PMID: 38745991 PMCID: PMC11091930 DOI: 10.1093/nargab/lqae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/05/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.
Collapse
Affiliation(s)
- Clément Bernard
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
- LISN - CNRS/Université Paris-Saclay, 91400 Orsay, France
| | - Guillaume Postic
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Sahar Ghannay
- LISN - CNRS/Université Paris-Saclay, 91400 Orsay, France
| | - Fariza Tahi
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
| |
Collapse
|
11
|
Peterson JM, Becker ST, O'Leary CA, Juneja P, Yang Y, Moss WN. Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop. Biochemistry 2024; 63:1287-1296. [PMID: 38727003 DOI: 10.1021/acs.biochem.3c00716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) frameshift stimulatory element (FSE) is necessary for programmed -1 ribosomal frameshifting (-1 PRF) and optimized viral efficacy. The FSE has an abundance of context-dependent alternate conformations, but two of the structures most crucial to -1 PRF are an attenuator hairpin and a three-stem H-type pseudoknot structure. A crystal structure of the pseudoknot alone features three RNA stems in a helically stacked linear structure, whereas a 6.9 Å cryo-EM structure including the upstream heptameric slippery site resulted in a bend between two stems. Our previous research alluded to an extended upstream multibranch loop that includes both the attenuator hairpin and the slippery site-a conformation not previously modeled. We aim to provide further context to the SARS-CoV-2 FSE via computational and medium resolution cryo-EM approaches, by presenting a 6.1 Å cryo-EM structure featuring a linear pseudoknot structure and a dynamic upstream multibranch loop.
Collapse
Affiliation(s)
- Jake M Peterson
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Scott T Becker
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Collin A O'Leary
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Puneet Juneja
- Cryo-EM Facility, Iowa State University, Ames, Iowa 50011, United States
| | - Yang Yang
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Walter N Moss
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| |
Collapse
|
12
|
Ramakers J, Blum CF, König S, Harmeling S, Kollmann M. De novo prediction of RNA 3D structures with deep generative models. PLoS One 2024; 19:e0297105. [PMID: 38358972 PMCID: PMC10868834 DOI: 10.1371/journal.pone.0297105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/24/2023] [Indexed: 02/17/2024] Open
Abstract
We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We show that RNA de novo structure prediction by deep learning is possible at atom resolution, despite the low number of experimentally measured structures that can be used for training. We confirm the predictive power of our approach by achieving competitive results in a retrospective evaluation of the RNA-Puzzles prediction challenges, without using structural contact information from multiple sequence alignments or additional data from chemical probing experiments. Blind predictions for recent RNA-Puzzle challenges under the name "Dfold" further support the competitive performance of our approach.
Collapse
Affiliation(s)
- Julius Ramakers
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | | | - Sabrina König
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Stefan Harmeling
- Department of Computer Science, Technical University Dortmund, Dortmund, Germany
| | - Markus Kollmann
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| |
Collapse
|
13
|
Loyer G, Reinharz V. Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework. Bioinformatics 2024; 40:btae022. [PMID: 38230755 PMCID: PMC10868335 DOI: 10.1093/bioinformatics/btae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/30/2023] [Accepted: 01/12/2024] [Indexed: 01/18/2024] Open
Abstract
MOTIVATION The prediction of RNA structure canonical base pairs from a single sequence, especially pseudoknotted ones, remains challenging in a thermodynamic models that approximates the energy of the local 3D motifs joining canonical stems. It has become more and more apparent in recent years that the structural motifs in the loops, composed of noncanonical interactions, are essential for the final shape of the molecule enabling its multiple functions. Our capacity to predict accurate 3D structures is also limited when it comes to the organization of the large intricate network of interactions that form inside those loops. RESULTS We previously developed the integer programming framework RNA Motifs over Integer Programming (RNAMoIP) to reconcile RNA secondary structure and local 3D motif information available in databases. We further develop our model to now simultaneously predict the canonical base pairs (with pseudoknots) from base pair probability matrices with or without alignment. We benchmarked our new method over the all nonredundant RNAs below 150 nucleotides. We show that the joined prediction of canonical base pairs structure and local conserved motifs (i) improves the ratio of well-predicted interactions in the secondary structure, (ii) predicts well canonical and Wobble pairs at the location where motifs are inserted, (iii) is greatly improved with evolutionary information, and (iv) noncanonical motifs at kink-turn locations. AVAILABILITY AND IMPLEMENTATION The source code of the framework is available at https://gitlab.info.uqam.ca/cbe/RNAMoIP and an interactive web server at https://rnamoip.cbe.uqam.ca/.
Collapse
Affiliation(s)
- Gabriel Loyer
- Department of Computer Science, Université du Québec à Montréal, Montréal, QC H2X 3Y7, Canada
| | - Vladimir Reinharz
- Department of Computer Science, Université du Québec à Montréal, Montréal, QC H2X 3Y7, Canada
| |
Collapse
|
14
|
Thiel BC, Poblete S, Hofacker IL. The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline. Methods Mol Biol 2024; 2726:377-399. [PMID: 38780739 DOI: 10.1007/978-1-0716-3519-3_15] [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] [Indexed: 05/25/2024]
Abstract
Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.
Collapse
Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Simón Poblete
- Instituto de Ciencias Físicas y Matemáticas, Universidad Austral de Chile, Valdivia, Chile
- Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad SanSebastián, Santiago, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
- Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria.
| |
Collapse
|
15
|
Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 2023; 91:1747-1770. [PMID: 37876231 PMCID: PMC10841292 DOI: 10.1002/prot.26602] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/26/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
Collapse
Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
| |
Collapse
|
16
|
Peterson JM, O'Leary CA, Coppenbarger EC, Tompkins VS, Moss WN. Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis. MethodsX 2023; 11:102275. [PMID: 37448951 PMCID: PMC10336498 DOI: 10.1016/j.mex.2023.102275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 06/28/2023] [Indexed: 07/18/2023] Open
Abstract
Major advances in RNA secondary structural motif prediction have been achieved in the last few years; however, few methods harness the predictive power of multiple approaches to deliver in-depth characterizations of local RNA motifs and their potential functionality. Additionally, most available methods do not predict RNA pseudoknots. This work combines complementary bioinformatic systems into one robust discovery pipeline where: •RNA sequences are folded to search for thermodynamically favorable motifs utilizing ScanFold.•Motifs are expanded and refolded into alternate pseudoknot conformations by Knotty/Iterative HFold.•All conformations are evaluated for covariance via the cm-builder pipeline (Infernal and R-scape).
Collapse
|
17
|
Sarzynska J, Popenda M, Antczak M, Szachniuk M. RNA tertiary structure prediction using RNAComposer in CASP15. Proteins 2023; 91:1790-1799. [PMID: 37615316 DOI: 10.1002/prot.26578] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/14/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023]
Abstract
As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.
Collapse
Affiliation(s)
- Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| |
Collapse
|
18
|
Kretsch RC, Andersen ES, Bujnicki JM, Chiu W, Das R, Luo B, Masquida B, McRae EK, Schroeder GM, Su Z, Wedekind JE, Xu L, Zhang K, Zheludev IN, Moult J, Kryshtafovych A. RNA target highlights in CASP15: Evaluation of predicted models by structure providers. Proteins 2023; 91:1600-1615. [PMID: 37466021 PMCID: PMC10792523 DOI: 10.1002/prot.26550] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/20/2023]
Abstract
The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.
Collapse
Affiliation(s)
- Rachael C. Kretsch
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Ebbe S. Andersen
- Interdisciplinary Nanoscience Center and Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Janusz M. Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Wah Chiu
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Bioengineering and James H. Clark Center, Stanford University, Stanford, CA, USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Rhiju Das
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Benoît Masquida
- UMR 7156, CNRS – Universite de Strasbourg, Strasbourg, France
| | - Ewan K.S. McRae
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Griffin M. Schroeder
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Joseph E. Wedekind
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Lily Xu
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kaiming Zhang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Ivan N. Zheludev
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - John Moult
- Department of Cell Biology and Molecular Genetics, Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, USA
| | | |
Collapse
|
19
|
Baulin EF, Mukherjee S, Moafinejad SN, Wirecki TK, Badepally NG, Jaryani F, Stefaniak F, Amiri Farsani M, Ray A, Rocha de Moura T, Bujnicki JM. RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints. Proteins 2023; 91:1800-1810. [PMID: 37622458 DOI: 10.1002/prot.26575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023]
Abstract
Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.
Collapse
Affiliation(s)
- Eugene F Baulin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - S Naeim Moafinejad
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Nagendar Goud Badepally
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Farhang Jaryani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Masoud Amiri Farsani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Angana Ray
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Tales Rocha de Moura
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| |
Collapse
|
20
|
Wang W, Feng C, Han R, Wang Z, Ye L, Du Z, Wei H, Zhang F, Peng Z, Yang J. trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Nat Commun 2023; 14:7266. [PMID: 37945552 PMCID: PMC10636060 DOI: 10.1038/s41467-023-42528-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.
Collapse
Affiliation(s)
- Wenkai Wang
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Chenjie Feng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
- School of Science, Ningxia Medical University, Yinchuan, 750004, China
| | - Renmin Han
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Ziyi Wang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Lisha Ye
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Zongyang Du
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Hong Wei
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Fa Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Zhenling Peng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
| |
Collapse
|
21
|
Zhang S, Liu Y, Xie L. A universal framework for accurate and efficient geometric deep learning of molecular systems. Sci Rep 2023; 13:19171. [PMID: 37932352 PMCID: PMC10628308 DOI: 10.1038/s41598-023-46382-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023] Open
Abstract
Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications.
Collapse
Affiliation(s)
- Shuo Zhang
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10065, USA
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA.
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10065, USA.
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
| |
Collapse
|
22
|
Malhotra S, Mulvaney T, Cragnolini T, Sidhu H, Joseph A, Beton J, Topf M. RIBFIND2: Identifying rigid bodies in protein and nucleic acid structures. Nucleic Acids Res 2023; 51:9567-9575. [PMID: 37670532 PMCID: PMC10570027 DOI: 10.1093/nar/gkad721] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 08/10/2023] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
Molecular structures are often fitted into cryo-EM maps by flexible fitting. When this requires large conformational changes, identifying rigid bodies can help optimize the model-map fit. Tools for identifying rigid bodies in protein structures exist, however an equivalent for nucleic acid structures is lacking. With the increase in cryo-EM maps containing RNA and progress in RNA structure prediction, there is a need for such tools. We previously developed RIBFIND, a program for clustering protein secondary structures into rigid bodies. In RIBFIND2, this approach is extended to nucleic acid structures. RIBFIND2 can identify biologically relevant rigid bodies in important groups of complex RNA structures, capturing a wide range of dynamics, including large rigid-body movements. The usefulness of RIBFIND2-assigned rigid bodies in cryo-EM model refinement was demonstrated on three examples, with two conformations each: Group II Intron complexed IEP, Internal Ribosome Entry Site and the Processome, using cryo-EM maps at 2.7-5 Å resolution. A hierarchical refinement approach, performed on progressively smaller sets of RIBFIND2 rigid bodies, was clearly shown to have an advantage over classical all-atom refinement. RIBFIND2 is available via a web server with structure visualization and as a standalone tool.
Collapse
Affiliation(s)
- Sony Malhotra
- Science and Technology Facilities Council, Scientific Computing, Research Complex at Harwell, Didcot OX11 0FA, UK
| | - Thomas Mulvaney
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
- Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg 20246, Germany
| | - Tristan Cragnolini
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London WC1E 7HX, UK
| | - Haneesh Sidhu
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London WC1E 7HX, UK
| | - Agnel P Joseph
- Science and Technology Facilities Council, Scientific Computing, Research Complex at Harwell, Didcot OX11 0FA, UK
| | - Joseph G Beton
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
| | - Maya Topf
- Leibniz Institute of Virology, Hamburg 20251, Germany
- Centre for Structural Systems Biology, Hamburg D-22607, Germany
- Universitätsklinikum Hamburg Eppendorf (UKE), Hamburg 20246, Germany
| |
Collapse
|
23
|
Schneider B, Sweeney BA, Bateman A, Cerny J, Zok T, Szachniuk M. When will RNA get its AlphaFold moment? Nucleic Acids Res 2023; 51:9522-9532. [PMID: 37702120 PMCID: PMC10570031 DOI: 10.1093/nar/gkad726] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023] Open
Abstract
The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.
Collapse
Affiliation(s)
- Bohdan Schneider
- Institute of Biotechnology of the Czech Academy of Sciences, Prumyslova 595, CZ-252 50 Vestec, Czech Republic
| | - Blake Alexander Sweeney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Jiri Cerny
- Institute of Biotechnology of the Czech Academy of Sciences, Prumyslova 595, CZ-252 50 Vestec, Czech Republic
| | - Tomasz Zok
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| |
Collapse
|
24
|
Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538330. [PMID: 37162955 PMCID: PMC10168427 DOI: 10.1101/2023.04.25.538330] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
Collapse
Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine,University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
| |
Collapse
|
25
|
Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, Zhang Y. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nat Commun 2023; 14:5745. [PMID: 37717036 PMCID: PMC10505173 DOI: 10.1038/s41467-023-41303-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023] Open
Abstract
RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.
Collapse
Affiliation(s)
- Yang Li
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore, Singapore
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA
| | - Chenjie Feng
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- School of Science, Ningxia Medical University, Yinchuan, 750004, China
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore, Singapore
| | - P Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - Yang Zhang
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore, Singapore.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore, Singapore.
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117596, Singapore, Singapore.
| |
Collapse
|
26
|
Taubert O, von der Lehr F, Bazarova A, Faber C, Knechtges P, Weiel M, Debus C, Coquelin D, Basermann A, Streit A, Kesselheim S, Götz M, Schug A. RNA contact prediction by data efficient deep learning. Commun Biol 2023; 6:913. [PMID: 37674020 PMCID: PMC10482910 DOI: 10.1038/s42003-023-05244-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023] Open
Abstract
On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.
Collapse
Affiliation(s)
- Oskar Taubert
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
| | - Fabrice von der Lehr
- Institute for Software Technology (SC), German Aerospace Centre (DLR), 51147, Köln, Germany
| | - Alina Bazarova
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428, Jülich, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Christian Faber
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Philipp Knechtges
- Institute for Software Technology (SC), German Aerospace Centre (DLR), 51147, Köln, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Marie Weiel
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Charlotte Debus
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Daniel Coquelin
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Achim Basermann
- Institute for Software Technology (SC), German Aerospace Centre (DLR), 51147, Köln, Germany
| | - Achim Streit
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
| | - Stefan Kesselheim
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428, Jülich, Germany
- Helmholtz AI, 81675, Munich, Germany
| | - Markus Götz
- Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany.
- Helmholtz AI, 81675, Munich, Germany.
| | - Alexander Schug
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428, Jülich, Germany.
- Faculty of Biology, University of Duisburg-Essen, 45117, Essen, Germany.
| |
Collapse
|
27
|
Chojnowski G, Zaborowski R, Magnus M, Mukherjee S, Bujnicki JM. RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints. Bioinformatics 2023; 39:btad527. [PMID: 37647627 PMCID: PMC10474949 DOI: 10.1093/bioinformatics/btad527] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/14/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023] Open
Abstract
SUMMARY Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints. AVAILABILITY AND IMPLEMENTATION The program web server is available at http://iimcb.genesilico.pl/rnamasonry. The source code is available at https://gitlab.com/gchojnowski/rnamasonry.
Collapse
Affiliation(s)
- Grzegorz Chojnowski
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- European Molecular Biology Laboratory, Hamburg Unit, Hamburg 22607, Germany
| | - Rafał Zaborowski
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
| | - Marcin Magnus
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
| | - Sunandan Mukherjee
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
| | - Janusz M Bujnicki
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
Wang X, Yu S, Lou E, Tan YL, Tan ZJ. RNA 3D Structure Prediction: Progress and Perspective. Molecules 2023; 28:5532. [PMID: 37513407 PMCID: PMC10386116 DOI: 10.3390/molecules28145532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.
Collapse
Affiliation(s)
- Xunxun Wang
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - En Lou
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China
| | - Zhi-Jie Tan
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| |
Collapse
|
30
|
Wu KE, Zou JY, Chang H. Machine learning modeling of RNA structures: methods, challenges and future perspectives. Brief Bioinform 2023; 24:bbad210. [PMID: 37280185 DOI: 10.1093/bib/bbad210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 06/08/2023] Open
Abstract
The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.
Collapse
Affiliation(s)
- Kevin E Wu
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - James Y Zou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Howard Chang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
31
|
Zhang D, Qiao L, Lei X, Dong X, Tong Y, Wang J, Wang Z, Zhou R. Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein. Nat Commun 2023; 14:3715. [PMID: 37349329 PMCID: PMC10287707 DOI: 10.1038/s41467-023-39410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 06/12/2023] [Indexed: 06/24/2023] Open
Abstract
Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.
Collapse
Affiliation(s)
- Dong Zhang
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Lulu Qiao
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Xiaobo Lei
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Xiaojing Dong
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Yunguang Tong
- College of Life Sciences, China Jiliang University, Hangzhou, Zhejiang, 310018, China
- Department of Pharmacy, China Jiliang University, Hangzhou, Zhejiang, 310018, China
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
| | - Zhiye Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - Ruhong Zhou
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| |
Collapse
|
32
|
Watson ZL, Knudson IJ, Ward FR, Miller SJ, Cate JHD, Schepartz A, Abramyan AM. Atomistic simulations of the Escherichia coli ribosome provide selection criteria for translationally active substrates. Nat Chem 2023:10.1038/s41557-023-01226-w. [PMID: 37308707 DOI: 10.1038/s41557-023-01226-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/28/2023] [Indexed: 06/14/2023]
Abstract
As genetic code expansion advances beyond L-α-amino acids to backbone modifications and new polymerization chemistries, delineating what substrates the ribosome can accommodate remains a challenge. The Escherichia coli ribosome tolerates non-L-α-amino acids in vitro, but few structural insights that explain how are available, and the boundary conditions for efficient bond formation are so far unknown. Here we determine a high-resolution cryogenic electron microscopy structure of the E. coli ribosome containing α-amino acid monomers and use metadynamics simulations to define energy surface minima and understand incorporation efficiencies. Reactive monomers across diverse structural classes favour a conformational space where the aminoacyl-tRNA nucleophile is <4 Å from the peptidyl-tRNA carbonyl with a Bürgi-Dunitz angle of 76-115°. Monomers with free energy minima that fall outside this conformational space do not react efficiently. This insight should accelerate the in vivo and in vitro ribosomal synthesis of sequence-defined, non-peptide heterooligomers.
Collapse
Affiliation(s)
- Zoe L Watson
- Department of Chemistry, University of California, Berkeley, CA, USA
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA, USA
| | - Isaac J Knudson
- Department of Chemistry, University of California, Berkeley, CA, USA
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA
| | - Fred R Ward
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA
- Department of Molecular and Cellular Biology, University of California, Berkeley, CA, USA
| | - Scott J Miller
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA.
- Department of Chemistry, Yale University, New Haven, CT, USA.
| | - Jamie H D Cate
- Department of Chemistry, University of California, Berkeley, CA, USA.
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Alanna Schepartz
- Department of Chemistry, University of California, Berkeley, CA, USA.
- Center for Genetically Encoded Materials, University of California, Berkeley, CA, USA.
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
| | | |
Collapse
|
33
|
Gao W, Yang A, Rivas E. Thirteen dubious ways to detect conserved structural RNAs. IUBMB Life 2023; 75:471-492. [PMID: 36495545 PMCID: PMC11234323 DOI: 10.1002/iub.2694] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 10/24/2022] [Indexed: 12/14/2022]
Abstract
Covariation induced by compensatory base substitutions in RNA alignments is a great way to deduce conserved RNA structure, in principle. In practice, success depends on many factors, importantly the quality and depth of the alignment and the choice of covariation statistic. Measuring covariation between pairs of aligned positions is easy. However, using covariation to infer evolutionarily conserved RNA structure is complicated by other extraneous sources of covariation such as that resulting from homologous sequences having evolved from a common ancestor. In order to provide evidence of evolutionarily conserved RNA structure, a method to distinguish covariation due to sources other than RNA structure is necessary. Moreover, there are several sorts of artifactually generated covariation signals that can further confound the analysis. Additionally, some covariation signal is difficult to detect due to incomplete comparative data. Here, we investigate and critically discuss the practice of inferring conserved RNA structure by comparative sequence analysis. We provide new methods on how to approach and decide which of the numerous long non-coding RNAs (lncRNAs) have biologically relevant structures.
Collapse
Affiliation(s)
- William Gao
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ann Yang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Elena Rivas
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| |
Collapse
|
34
|
Tan YL, Wang X, Yu S, Zhang B, Tan ZJ. cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation. NAR Genom Bioinform 2023; 5:lqad016. [PMID: 36879898 PMCID: PMC9985339 DOI: 10.1093/nargab/lqad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/21/2023] [Accepted: 02/03/2023] [Indexed: 03/07/2023] Open
Abstract
Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.
Collapse
Affiliation(s)
- Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China.,Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xunxun Wang
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| |
Collapse
|
35
|
Moafinejad SN, Pandaranadar Jeyeram IPN, Jaryani F, Shirvanizadeh N, Baulin EF, Bujnicki JM. 1D2DSimScore: A novel method for comparing contacts in biomacromolecules and their complexes. Protein Sci 2023; 32:e4503. [PMID: 36369832 PMCID: PMC9795538 DOI: 10.1002/pro.4503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/28/2022] [Accepted: 11/01/2022] [Indexed: 11/13/2022]
Abstract
The biologically relevant structures of proteins and nucleic acids and their complexes are dynamic. They include a combination of regions ranging from rigid structural segments to structural switches to regions that are almost always disordered, which interact with each other in various ways. Comparing conformational changes and variation in contacts between different conformational states is essential to understand the biological functions of proteins, nucleic acids, and their complexes. Here, we describe a new computational tool, 1D2DSimScore, for comparing contacts and contact interfaces in all kinds of macromolecules and macromolecular complexes, including proteins, nucleic acids, and other molecules. 1D2DSimScore can be used to compare structural features of macromolecular models between alternative structures obtained in a particular experiment or to score various predictions against a defined "ideal" reference structure. Comparisons at the level of contacts are particularly useful for flexible molecules, for which comparisons in 3D that require rigid-body superpositions are difficult, and in biological systems where the formation of specific inter-residue contacts is more relevant for the biological function than the maintenance of a specific global 3D structure. Similarity/dissimilarity scores calculated by 1D2DSimScore can be used to complement scores describing 3D structural similarity measures calculated by the existing tools.
Collapse
Affiliation(s)
- S. Naeim Moafinejad
- Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell Biology in WarsawWarsawPoland
| | | | - Farhang Jaryani
- Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell Biology in WarsawWarsawPoland
| | - Niloofar Shirvanizadeh
- Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell Biology in WarsawWarsawPoland
| | - Eugene F. Baulin
- Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell Biology in WarsawWarsawPoland
| | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein EngineeringInternational Institute of Molecular and Cell Biology in WarsawWarsawPoland
| |
Collapse
|
36
|
Ma H, Pham P, Luo B, Rangan R, Kappel K, Su Z, Das R. Auto-DRRAFTER: Automated RNA Modeling Based on Cryo-EM Density. Methods Mol Biol 2023; 2568:193-211. [PMID: 36227570 DOI: 10.1007/978-1-0716-2687-0_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .
Collapse
Affiliation(s)
- Haiyun Ma
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Phillip Pham
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA, USA.
| |
Collapse
|
37
|
Paloncýová M, Pykal M, Kührová P, Banáš P, Šponer J, Otyepka M. Computer Aided Development of Nucleic Acid Applications in Nanotechnologies. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204408. [PMID: 36216589 DOI: 10.1002/smll.202204408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.
Collapse
Affiliation(s)
- Markéta Paloncýová
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Martin Pykal
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Petra Kührová
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Pavel Banáš
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Jiří Šponer
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
- Institute of Biophysics of the Czech Academy of Sciences, v. v. i., Královopolská 135, Brno, 612 65, Czech Republic
| | - Michal Otyepka
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
- IT4Innovations, VŠB - Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba, 708 00, Czech Republic
| |
Collapse
|
38
|
Rolband L, Beasock D, Wang Y, Shu YG, Dinman JD, Schlick T, Zhou Y, Kieft JS, Chen SJ, Bussi G, Oukhaled A, Gao X, Šulc P, Binzel D, Bhullar AS, Liang C, Guo P, Afonin KA. Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions. Comput Struct Biotechnol J 2022; 20:6120-6137. [PMID: 36420155 PMCID: PMC9672130 DOI: 10.1016/j.csbj.2022.11.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.
Collapse
Affiliation(s)
- Lewis Rolband
- University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Damian Beasock
- University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Yang Wang
- Wenzhou Institute, University of China Academy of Sciences, 1st, Jinlian Road, Longwan District, Wenzhou, Zhjiang 325001, China
| | - Yao-Gen Shu
- Wenzhou Institute, University of China Academy of Sciences, 1st, Jinlian Road, Longwan District, Wenzhou, Zhjiang 325001, China
| | | | - Tamar Schlick
- New York University, Department of Chemistry and Courant Institute of Mathematical Sciences, Simons Center for Computational Physical Chemistry, New York, NY 10012, USA
| | - Yaoqi Zhou
- Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518107, China
| | - Jeffrey S. Kieft
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Shi-Jie Chen
- University of Missouri at Columbia, Columbia, MO 65211, USA
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
| | | | - Xingfa Gao
- National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Petr Šulc
- Arizona State University, Tempe, AZ, USA
| | | | | | - Chenxi Liang
- The Ohio State University, Columbus, OH 43210, USA
| | - Peixuan Guo
- The Ohio State University, Columbus, OH 43210, USA
| | - Kirill A. Afonin
- University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| |
Collapse
|
39
|
Fukunaga T, Hamada M. LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods. BIOINFORMATICS ADVANCES 2022; 2:vbac078. [PMID: 36699418 PMCID: PMC9710674 DOI: 10.1093/bioadv/vbac078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/13/2022] [Accepted: 10/21/2022] [Indexed: 11/05/2022]
Abstract
Motivation RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time. Results In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results. Availability and implementation The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 1698555, Japan,Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo 1698555, Japan
| |
Collapse
|
40
|
Zhou L, Wang X, Yu S, Tan YL, Tan ZJ. FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures. Biophys J 2022; 121:3381-3392. [PMID: 35978551 PMCID: PMC9515226 DOI: 10.1016/j.bpj.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 11/23/2022] Open
Abstract
Knowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.
Collapse
Affiliation(s)
- Li Zhou
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xunxun Wang
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China.
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China.
| |
Collapse
|
41
|
Matarrese MAG, Loppini A, Nicoletti M, Filippi S, Chiodo L. Assessment of tools for RNA secondary structure prediction and extraction: a final-user perspective. J Biomol Struct Dyn 2022:1-20. [DOI: 10.1080/07391102.2022.2116110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Margherita A. G. Matarrese
- Engineering Department, Campus Bio-Medico University of Rome, Rome, Italy
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Engineering Department, Campus Bio-Medico University of Rome, Rome, Italy
- Center for Life Nano & Neuroscience, Italian Institute of Technology, Rome, Italy
| | - Martina Nicoletti
- Engineering Department, Campus Bio-Medico University of Rome, Rome, Italy
- Center for Life Nano & Neuroscience, Italian Institute of Technology, Rome, Italy
| | - Simonetta Filippi
- Engineering Department, Campus Bio-Medico University of Rome, Rome, Italy
| | - Letizia Chiodo
- Engineering Department, Campus Bio-Medico University of Rome, Rome, Italy
| |
Collapse
|
42
|
Gumna J, Antczak M, Adamiak RW, Bujnicki JM, Chen SJ, Ding F, Ghosh P, Li J, Mukherjee S, Nithin C, Pachulska-Wieczorek K, Ponce-Salvatierra A, Popenda M, Sarzynska J, Wirecki T, Zhang D, Zhang S, Zok T, Westhof E, Miao Z, Szachniuk M, Rybarczyk A. Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models. Int J Mol Sci 2022; 23:ijms23179630. [PMID: 36077037 PMCID: PMC9455975 DOI: 10.3390/ijms23179630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 01/19/2023] Open
Abstract
RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.
Collapse
Affiliation(s)
- Julita Gumna
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Ryszard W. Adamiak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Jun Li
- Department of Physics, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
- Laboratory of Computational Biology, Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-089 Warsaw, Poland
| | | | - Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Tomasz Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Dong Zhang
- Department of Physics, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Sicheng Zhang
- Department of Physics, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, 67084 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 200081, China
- Correspondence: (Z.M.); (A.R.)
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Agnieszka Rybarczyk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
- Correspondence: (Z.M.); (A.R.)
| |
Collapse
|
43
|
Kallert E, Fischer TR, Schneider S, Grimm M, Helm M, Kersten C. Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ 1-Riboswitch. J Chem Inf Model 2022; 62:4134-4148. [PMID: 35994617 DOI: 10.1021/acs.jcim.2c00751] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ1-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.
Collapse
Affiliation(s)
- Elisabeth Kallert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Simon Schneider
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Maike Grimm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| |
Collapse
|
44
|
Szikszai M, Wise M, Datta A, Ward M, Mathews DH. Deep learning models for RNA secondary structure prediction (probably) do not generalize across families. Bioinformatics 2022; 38:3892-3899. [PMID: 35748706 PMCID: PMC9364374 DOI: 10.1093/bioinformatics/btac415] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Marcell Szikszai
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Michael Wise
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
- The Marshall Centre for Infectious Diseases Research and Training, The University of Western Australia, Perth, WA 6009, Australia
| | - Amitava Datta
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Max Ward
- Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA 6009, Australia
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - David H Mathews
- Department of Biochemistry & Biophysics, Center for RNA Biology, and Department of Biostatistics & Computational Biology, University of Rochester, Rochester, NY 14642, USA
| |
Collapse
|
45
|
Nishima W, Girodat D, Holm M, Rundlet EJ, Alejo JL, Fischer K, Blanchard SC, Sanbonmatsu KY. Hyper-swivel head domain motions are required for complete mRNA-tRNA translocation and ribosome resetting. Nucleic Acids Res 2022; 50:8302-8320. [PMID: 35808938 DOI: 10.1093/nar/gkac597] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 11/14/2022] Open
Abstract
Translocation of messenger RNA (mRNA) and transfer RNA (tRNA) substrates through the ribosome during protein synthesis, an exemplar of directional molecular movement in biology, entails a complex interplay of conformational, compositional, and chemical changes. The molecular determinants of early translocation steps have been investigated rigorously. However, the elements enabling the ribosome to complete translocation and reset for subsequent protein synthesis reactions remain poorly understood. Here, we have combined molecular simulations with single-molecule fluorescence resonance energy transfer imaging to gain insights into the rate-limiting events of the translocation mechanism. We find that diffusive motions of the ribosomal small subunit head domain to hyper-swivelled positions, governed by universally conserved rRNA, can maneuver the mRNA and tRNAs to their fully translocated positions. Subsequent engagement of peptidyl-tRNA and disengagement of deacyl-tRNA from mRNA, within their respective small subunit binding sites, facilitate the ribosome resetting mechanism after translocation has occurred to enable protein synthesis to resume.
Collapse
Affiliation(s)
- Wataru Nishima
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
| | - Dylan Girodat
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
| | - Mikael Holm
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Emily J Rundlet
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jose L Alejo
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Kara Fischer
- New Mexico Consortium, Los Alamos, NM 87544, USA
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Karissa Y Sanbonmatsu
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
| |
Collapse
|
46
|
Luwanski K, Hlushchenko V, Popenda M, Zok T, Sarzynska J, Martsich D, Szachniuk M, Antczak M. RNAspider: a webserver to analyze entanglements in RNA 3D structures. Nucleic Acids Res 2022; 50:W663-W669. [PMID: 35349710 PMCID: PMC9252836 DOI: 10.1093/nar/gkac218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 12/12/2022] Open
Abstract
Advances in experimental and computational techniques enable the exploration of large and complex RNA 3D structures. These, in turn, reveal previously unstudied properties and motifs not characteristic for small molecules with simple architectures. Examples include entanglements of structural elements in RNA molecules and knot-like folds discovered, among others, in the genomes of RNA viruses. Recently, we presented the first classification of entanglements, determined by their topology and the type of entangled structural elements. Here, we introduce RNAspider - a web server to automatically identify, classify, and visualize primary and higher-order entanglements in RNA tertiary structures. The program applies to evaluate RNA 3D models obtained experimentally or by computational prediction. It supports the analysis of uncommon topologies in the pseudoknotted RNA structures. RNAspider is implemented as a publicly available tool with a user-friendly interface and can be freely accessed at https://rnaspider.cs.put.poznan.pl/.
Collapse
Affiliation(s)
- Kamil Luwanski
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Vladyslav Hlushchenko
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science and 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
| | - Daniil Martsich
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| |
Collapse
|
47
|
Singh J, Paliwal K, Litfin T, Singh J, Zhou Y. Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling. Bioinformatics 2022; 38:3900-3910. [PMID: 35751593 PMCID: PMC9364379 DOI: 10.1093/bioinformatics/btac421] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 04/30/2022] [Accepted: 06/28/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins. RESULTS Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction. AVAILABILITY AND IMPLEMENTATION SPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | - Thomas Litfin
- Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- To whom correspondence should be addressed. or or
| |
Collapse
|
48
|
Giarimoglou N, Kouvela A, Patsi I, Zhang J, Stamatopoulou V, Stathopoulos C. Lineage-specific insertions in T-box riboswitches modulate antibiotic binding and action. Nucleic Acids Res 2022; 50:5834-5849. [PMID: 35580054 PMCID: PMC9177973 DOI: 10.1093/nar/gkac359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/21/2022] [Accepted: 05/11/2022] [Indexed: 01/27/2023] Open
Abstract
T-box riboswitches (T-boxes) are essential RNA regulatory elements with a remarkable structural diversity, especially among bacterial pathogens. In staphylococci, all glyS T-boxes synchronize glycine supply during synthesis of nascent polypeptides and cell wall formation and are characterized by a conserved and unique insertion in their antiterminator/terminator domain, termed stem Sa. Interestingly, in Staphylococcus aureus the stem Sa can accommodate binding of specific antibiotics, which in turn induce robust and diverse effects on T-box-mediated transcription. In the present study, domain swap mutagenesis and probing analysis were performed to decipher the role of stem Sa. Deletion of stem Sa significantly reduces both the S. aureus glyS T-box-mediated transcription readthrough levels and the ability to discriminate among tRNAGly isoacceptors, both in vitro and in vivo. Moreover, the deletion inverted the previously reported stimulatory effects of specific antibiotics. Interestingly, stem Sa insertion in the terminator/antiterminator domain of Geobacillus kaustophilus glyS T-box, which lacks this domain, resulted in elevated transcription in the presence of tigecycline and facilitated discrimination among proteinogenic and nonproteinogenic tRNAGly isoacceptors. Overall, stem Sa represents a lineage-specific structural feature required for efficient staphylococcal glyS T-box-mediated transcription and it could serve as a species-selective druggable target through its ability to modulate antibiotic binding.
Collapse
Affiliation(s)
- Nikoleta Giarimoglou
- Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece
| | - Adamantia Kouvela
- Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece
| | - Ioanna Patsi
- Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece
| | - Jinwei Zhang
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | | | | |
Collapse
|
49
|
fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions. PLoS Comput Biol 2022; 18:e1009783. [PMID: 35653385 PMCID: PMC9197077 DOI: 10.1371/journal.pcbi.1009783] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/14/2022] [Accepted: 05/06/2022] [Indexed: 11/19/2022] Open
Abstract
Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.
Collapse
|
50
|
Magnus M. rna-tools.online: a Swiss army knife for RNA 3D structure modeling workflow. Nucleic Acids Res 2022; 50:W657-W662. [PMID: 35580057 PMCID: PMC9252763 DOI: 10.1093/nar/gkac372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/20/2022] [Accepted: 05/02/2022] [Indexed: 11/15/2022] Open
Abstract
Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods in recent years; however, many tools developed in the field stay exclusive to only a few bioinformatic groups. To perform a complete RNA 3D structure modeling analysis as proposed by the RNA-Puzzles community, researchers must familiarize themselves with a quite complex set of tools. In order to facilitate the processing of RNA sequences and structures, we previously developed the rna-tools package. However, using rna-tools requires the installation of a mixture of libraries and tools, basic knowledge of the command line and the Python programming language. To provide an opportunity for the broader community of biologists to take advantage of the new developments in RNA structural biology, we developed rna-tools.online. The web server provides a user-friendly platform to perform many standard analyses required for the typical modeling workflow: 3D structure manipulation and editing, structure minimization, structure analysis, quality assessment, and comparison. rna-tools.online supports biologists to start benefiting from the maturing field of RNA 3D structural bioinformatics and can be used for educational purposes. The web server is available at https://rna-tools.online.
Collapse
Affiliation(s)
- Marcin Magnus
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
| |
Collapse
|