1
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Sha CM, Wang J, Dokholyan NV. Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks. Biophys J 2024; 123:2671-2681. [PMID: 37838833 PMCID: PMC11393712 DOI: 10.1016/j.bpj.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/19/2023] [Accepted: 10/12/2023] [Indexed: 10/16/2023] Open
Abstract
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.
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Affiliation(s)
- Congzhou M Sha
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Nikolay V Dokholyan
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Chemistry, Penn State University, State College, Pennsylvania; Department of Biomedical Engineering, Penn State University, State College, Pennsylvania.
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2
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Zhang S, Li J, Chen SJ. Machine learning in RNA structure prediction: Advances and challenges. Biophys J 2024; 123:2647-2657. [PMID: 38297836 PMCID: PMC11393687 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.
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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.
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3
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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.
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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
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4
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He S, Huang R, Townley J, Kretsch RC, Karagianes TG, Cox DBT, Blair H, Penzar D, Vyaltsev V, Aristova E, Zinkevich A, Bakulin A, Sohn H, Krstevski D, Fukui T, Tatematsu F, Uchida Y, Jang D, Lee JS, Shieh R, Ma T, Martynov E, Shugaev MV, Bukhari HST, Fujikawa K, Onodera K, Henkel C, Ron S, Romano J, Nicol JJ, Nye GP, Wu Y, Choe C, Reade W, Das R. Ribonanza: deep learning of RNA structure through dual crowdsourcing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581671. [PMID: 38464325 PMCID: PMC10925082 DOI: 10.1101/2024.02.24.581671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.
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Affiliation(s)
- Shujun He
- Department of Chemical Engineering, Texas A&M University, TX, USA
| | - Rui Huang
- Department of Biochemistry, Stanford CA, USA
| | | | | | | | - David B T Cox
- Department of Biochemistry, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
| | | | - Dmitry Penzar
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Valeriy Vyaltsev
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Elizaveta Aristova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Arsenii Zinkevich
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Hoyeol Sohn
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Daniel Krstevski
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | | | | | | | - Donghoon Jang
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
| | | | - Roger Shieh
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Tom Ma
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Eduard Martynov
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
| | - Maxim V Shugaev
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
| | | | | | | | | | - Shlomo Ron
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Jonathan Romano
- Eterna Massive Open Laboratory
- Howard Hughes Medical Institute
| | | | - Grace P Nye
- Department of Biochemistry, Stanford CA, USA
| | - Yuan Wu
- Department of Biochemistry, Stanford CA, USA
- Howard Hughes Medical Institute
| | | | | | - Rhiju Das
- Department of Biochemistry, Stanford CA, USA
- Biophysics Program, Stanford CA, USA
- Howard Hughes Medical Institute
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5
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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/.
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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
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6
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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.
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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
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7
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Li J, Zhang S, Chen SJ. Advancing RNA 3D structure prediction: Exploring hierarchical and hybrid approaches in CASP15. Proteins 2023; 91:1779-1789. [PMID: 37615235 PMCID: PMC10841231 DOI: 10.1002/prot.26583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 06/19/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023]
Abstract
In CASP15, we used an integrated hierarchical and hybrid approach to predict RNA structures. The approach involves three steps. First, with the use of physics-based methods, Vfold2D-MC and VfoldMCPX, we predict the 2D structures from the sequence. Second, we employ template-based methods, Vfold3D and VfoldLA, to build 3D scaffolds for the predicted 2D structures. Third, using the 3D scaffolds as initial structures and the predicted 2D structures as constraints, we predict the 3D structure from coarse-grained molecular dynamics simulations, IsRNA and RNAJP. Our approach was evaluated on 12 RNA targets in CASP15 and ranked second among all the 34 participating teams. The result demonstrated the reliability of our method in predicting RNA 2D structures with high accuracy and RNA 3D structures with moderate accuracy. Further improvements in RNA structure prediction for the next round of CASP may come from the incorporation of the physics-based method with machine learning techniques.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Sicheng Zhang
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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8
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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.
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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
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9
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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.
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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
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10
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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.
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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
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11
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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.
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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
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12
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Kagaya Y, Zhang Z, Ibtehaz N, Wang X, Nakamura T, Huang D, Kihara D. NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558715. [PMID: 37790488 PMCID: PMC10542152 DOI: 10.1101/2023.09.20.558715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
RNA is not only playing a core role in the central dogma as mRNA between DNA and protein, but also many non-coding RNAs have been discovered to have unique and diverse biological functions. As genome sequences become increasingly available and our knowledge of RNA sequences grows, the study of RNA's structure and function has become more demanding. However, experimental determination of three-dimensional RNA structures is both costly and time-consuming, resulting in a substantial disparity between RNA sequence data and structural insights. In response to this challenge, we propose a novel computational approach that harnesses state-of-the-art deep learning architecture NuFold to accurately predict RNA tertiary structures. This approach aims to offer a cost-effective and efficient means of bridging the gap between RNA sequence information and structural comprehension. NuFold implements a nucleobase center representation, which allows it to reproduce all possible nucleotide conformations accurately.
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Affiliation(s)
- Yuki Kagaya
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Nabil Ibtehaz
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Tsukasa Nakamura
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
| | - David Huang
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
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13
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Lazzeri G, Micheletti C, Pasquali S, Faccioli P. RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias. Biophys J 2023; 122:3089-3098. [PMID: 37355771 PMCID: PMC10432211 DOI: 10.1016/j.bpj.2023.06.012] [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: 02/07/2023] [Revised: 05/03/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023] Open
Abstract
Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence.
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Affiliation(s)
- Gianmarco Lazzeri
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany; Physics Department of Trento University, Povo (Trento), Italy
| | | | - Samuela Pasquali
- Laboratoire Cibles Thérapeutiques et Conception de Médicaments, Université Paris Cité, Paris, France; Laboratoire Biologie Fonctionnelle et Adaptative, Université Paris Cité, Paris, France.
| | - Pietro Faccioli
- Physics Department of Trento University, Povo (Trento), Italy; INFN-TIFPA, Povo (Trento), Italy.
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14
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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.
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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
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15
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Wang J, Sha CM, Dokholyan NV. Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology. Methods Mol Biol 2023; 2709:51-64. [PMID: 37572272 PMCID: PMC10680996 DOI: 10.1007/978-1-0716-3417-2_3] [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: 08/14/2023]
Abstract
Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.
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Affiliation(s)
- Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Congzhou M Sha
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA.
- Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA.
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA.
- Department of Chemistry, Penn State University, State College, PA, USA.
- Department of Biomedical Engineering, Penn State University, State College, PA, USA.
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16
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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.
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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
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17
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Yan S, Ilgu M, Nilsen-Hamilton M, Lamm MH. Computational Modeling of RNA Aptamers: Structure Prediction of the Apo State. J Phys Chem B 2022; 126:7114-7125. [PMID: 36097649 PMCID: PMC9512008 DOI: 10.1021/acs.jpcb.2c04649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/23/2022] [Indexed: 11/28/2022]
Abstract
RNA aptamers are single-stranded oligonucleotides that bind to specific molecular targets with high affinity and specificity. To design aptamers for new applications, it is critical to understand the ligand binding mechanism in terms of the structure and dynamics of the ligand-bound and apo states. The problem is that most of the NMR or X-ray crystal structures available for RNA aptamers are for ligand-bound states. Available apo state structures, mostly characterized by crystallization under nonphysiological conditions or probed by low resolution techniques, might fail to represent the diverse structural variations of the apo state in solution. Here, we develop an approach to obtain a representative ensemble of apo structures that are based on in silico RNA 3D structure prediction and in vitro experiments that characterize base stacking. Using the neomycin-B aptamer as a case study, an ensemble of structures for the aptamer in the apo (unbound) state are validated and then used to investigate the ligand-binding mechanism for the aptamer in complex with neomycin-B.
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Affiliation(s)
- Shuting Yan
- Iowa
State University, Ames, Iowa 50011, United States
| | - Muslum Ilgu
- Iowa
State University, Ames, Iowa 50011, United States
- Ames
National Laboratory, Ames, Iowa 50011, United States
- Aptalogic
Inc., Ames, Iowa 50014, United States
| | - Marit Nilsen-Hamilton
- Iowa
State University, Ames, Iowa 50011, United States
- Ames
National Laboratory, Ames, Iowa 50011, United States
- Aptalogic
Inc., Ames, Iowa 50014, United States
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18
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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
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19
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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.
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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
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20
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Wiedemann J, Kaczor J, Milostan M, Zok T, Blazewicz J, Szachniuk M, Antczak M. RNAloops: a database of RNA multiloops. Bioinformatics 2022; 38:4200-4205. [PMID: 35809063 PMCID: PMC9438955 DOI: 10.1093/bioinformatics/btac484] [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: 04/05/2022] [Revised: 06/26/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Knowledge of the 3D structure of RNA supports discovering its functions and is crucial for designing drugs and modern therapeutic solutions. Thus, much attention is devoted to experimental determination and computational prediction targeting the global fold of RNA and its local substructures. The latter include multi-branched loops-functionally significant elements that highly affect the spatial shape of the entire molecule. Unfortunately, their computational modeling constitutes a weak point of structural bioinformatics. A remedy for this is in collecting these motifs and analyzing their features. RESULTS RNAloops is a self-updating database that stores multi-branched loops identified in the PDB-deposited RNA structures. A description of each loop includes angular data-planar and Euler angles computed between pairs of adjacent helices to allow studying their mutual arrangement in space. The system enables search and analysis of multiloops, presents their structure details numerically and visually, and computes data statistics. AVAILABILITY AND IMPLEMENTATION RNAloops is freely accessible at https://rnaloops.cs.put.poznan.pl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jakub Wiedemann
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Jacek Kaczor
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Maciej Milostan
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland,Poznan Supercomputing and Networking Center, 61-131 Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland,Poznan Supercomputing and Networking Center, 61-131 Poznan, Poland
| | - Jacek Blazewicz
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland,Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
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21
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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.
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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
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22
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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.
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Affiliation(s)
- Marcin Magnus
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
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23
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Przanowska RK, Weidmann CA, Saha S, Cichewicz MA, Jensen KN, Przanowski P, Irving PS, Janes KA, Guertin MJ, Weeks KM, Dutta A. Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors. Cell Rep 2022; 38:110361. [PMID: 35172143 PMCID: PMC8937029 DOI: 10.1016/j.celrep.2022.110361] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/03/2021] [Accepted: 01/19/2022] [Indexed: 12/27/2022] Open
Abstract
Many lncRNAs have been discovered using transcriptomic data; however, it is unclear what fraction of lncRNAs is functional and what structural properties affect their phenotype. MUNC lncRNA (also known as DRReRNA) acts as an enhancer RNA for the Myod1 gene in cis and stimulates the expression of other promyogenic genes in trans by recruiting the cohesin complex. Here, experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by prediction algorithms in the absence of experimental information. We show that these specific and structurally distinct domains are required for induction of promyogenic genes, for binding genomic sites and gene expression regulation, and for binding the cohesin complex. Myod1 induction and cohesin interaction comprise only a subset of MUNC phenotype. Our study reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.
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Affiliation(s)
- Roza K Przanowska
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Chase A Weidmann
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biological Chemistry and Center for RNA Biomedicine, University of Michigan Medical School, Ann Arbor, MI 48103, USA
| | - Shekhar Saha
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Magdalena A Cichewicz
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Kate N Jensen
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Piotr Przanowski
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Patrick S Irving
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kevin A Janes
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Virginia School of Engineering, Charlottesville, VA 22908, USA
| | - Michael J Guertin
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; R. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut, Farmington, CT 06030, USA
| | - Kevin M Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Anindya Dutta
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA; Department of Genetics, University of Alabama, Birmingham, AL 35233, USA.
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24
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Carrascoza F, Antczak M, Miao Z, Westhof E, Szachniuk M. Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions. RNA (NEW YORK, N.Y.) 2022; 28:250-262. [PMID: 34819324 PMCID: PMC8906551 DOI: 10.1261/rna.078685.121] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.
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Affiliation(s)
- Francisco Carrascoza
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China
| | - Eric Westhof
- Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire CNRS, Architecture et Réactivité de l'ARN, 67084 Strasbourg, France
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
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25
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Zerihun MB, Pucci F, Schug A. CoCoNet-boosting RNA contact prediction by convolutional neural networks. Nucleic Acids Res 2021; 49:12661-12672. [PMID: 34871451 PMCID: PMC8682773 DOI: 10.1093/nar/gkab1144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/27/2021] [Accepted: 11/05/2021] [Indexed: 11/24/2022] Open
Abstract
Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins. Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps. We introduce an algorithm called CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the positive predictive value (PPV) of predicted contacts by about 70% with respect to DCA as tested by cross-validation of about eighty RNA structures. However, the direct inclusion of the CoCoNet contacts in 3D modeling tools does not result in a proportional increase of the 3D RNA structure prediction accuracy. Therefore, we suggest that the field develops, in addition to contact PPV, metrics which estimate the expected impact for 3D structure modeling tools better. CoCoNet is freely available and can be found at https://github.com/KIT-MBS/coconet.
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Affiliation(s)
- Mehari B Zerihun
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Fabrizio Pucci
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Computational Biology and Bioinformatics, Université Libre de Bruxelles 1050, Brussels, Belgium
| | - Alexander Schug
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Biology, University of Duisburg-Essen, 45117 Essen, Germany
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26
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De Bisschop G, Allouche D, Frezza E, Masquida B, Ponty Y, Will S, Sargueil B. Progress toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure. Noncoding RNA 2021; 7:71. [PMID: 34842779 PMCID: PMC8628965 DOI: 10.3390/ncrna7040071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/29/2021] [Accepted: 11/02/2021] [Indexed: 01/04/2023] Open
Abstract
As more sequencing data accumulate and novel puzzling genetic regulations are discovered, the need for accurate automated modeling of RNA structure increases. RNA structure modeling from chemical probing experiments has made tremendous progress, however accurately predicting large RNA structures is still challenging for several reasons: RNA are inherently flexible and often adopt many energetically similar structures, which are not reliably distinguished by the available, incomplete thermodynamic model. Moreover, computationally, the problem is aggravated by the relevance of pseudoknots and non-canonical base pairs, which are hardly predicted efficiently. To identify nucleotides involved in pseudoknots and non-canonical interactions, we scrutinized the SHAPE reactivity of each nucleotide of the 188 nt long lariat-capping ribozyme under multiple conditions. Reactivities analyzed in the light of the X-ray structure were shown to report accurately the nucleotide status. Those that seemed paradoxical were rationalized by the nucleotide behavior along molecular dynamic simulations. We show that valuable information on intricate interactions can be deduced from probing with different reagents, and in the presence or absence of Mg2+. Furthermore, probing at increasing temperature was remarkably efficient at pointing to non-canonical interactions and pseudoknot pairings. The possibilities of following such strategies to inform structure modeling software are discussed.
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Affiliation(s)
- Grégoire De Bisschop
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada
| | - Delphine Allouche
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
- Institut Necker-Enfants Malades (INEM), Inserm U1151, 156 rue de Vaugirard, CEDEX 15, 75015 Paris, France
| | - Elisa Frezza
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
| | - Benoît Masquida
- Université de Strasbourg, CNRS UMR7156 GMGM, 67084 Strasbourg, France;
| | - Yann Ponty
- Ecole Polytechnique, CNRS UMR 7161, LIX, 91120 Palaiseau, France; (Y.P.); (S.W.)
| | - Sebastian Will
- Ecole Polytechnique, CNRS UMR 7161, LIX, 91120 Palaiseau, France; (Y.P.); (S.W.)
| | - Bruno Sargueil
- Université de Paris, CNRS, UMR 8038/CiTCoM, F-75006 Paris, France; (G.D.B.); (D.A.); (E.F.)
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27
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Zhang D, Chen SJ, Zhou R. Modeling Noncanonical RNA Base Pairs by a Coarse-Grained IsRNA2 Model. J Phys Chem B 2021; 125:11907-11915. [PMID: 34694128 DOI: 10.1021/acs.jpcb.1c07288] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Noncanonical base pairs contribute crucially to the three-dimensional architecture of large RNA molecules; however, how to accurately model them remains an open challenge in RNA 3D structure prediction. Here, we report a promising coarse-grained (CG) IsRNA2 model to predict noncanonical base pairs in large RNAs through molecular dynamics simulations. By introducing a five-bead per nucleotide CG representation to reserve the three interacting edges of nucleobases, IsRNA2 accurately models various base-pairing interactions, including both canonical and noncanonical base pairs. A benchmark test indicated that IsRNA2 achieves a comparable performance to the atomic model in de novo modeling of noncanonical RNA structures. In addition, IsRNA2 was able to refine the 3D structure predictions for large RNAs in RNA-puzzle challenges. Finally, the graphics processing unit acceleration was introduced to speed up the sampling efficiency in IsRNA2 for very large RNA molecules. Therefore, the CG IsRNA2 model reported here offers a reliable approach to predict the structures and dynamics of large RNAs.
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Affiliation(s)
- Dong Zhang
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou 310058, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Ruhong Zhou
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou 310058, China
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28
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Popenda M, Zok T, Sarzynska J, Korpeta A, Adamiak R, Antczak M, Szachniuk M. Entanglements of structure elements revealed in RNA 3D models. Nucleic Acids Res 2021; 49:9625-9632. [PMID: 34432024 PMCID: PMC8464073 DOI: 10.1093/nar/gkab716] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/02/2021] [Accepted: 08/06/2021] [Indexed: 01/14/2023] Open
Abstract
Computational methods to predict RNA 3D structure have more and more practical applications in molecular biology and medicine. Therefore, it is crucial to intensify efforts to improve the accuracy and quality of predicted three-dimensional structures. A significant role in this is played by the RNA-Puzzles initiative that collects, evaluates, and shares RNAs built computationally within currently nearly 30 challenges. RNA-Puzzles datasets, subjected to multi-criteria analysis, allow revealing the strengths and weaknesses of computer prediction methods. Here, we study the issue of entangled RNA fragments in the predicted RNA 3D structure models. By entanglement, we mean an arrangement of two structural elements such that one of them passes through the other. We propose the classification of entanglements driven by their topology and components. It distinguishes two general classes, interlaces and lassos, and subclasses characterized by element types-loops, dinucleotide steps, open single-stranded fragments-and puncture multiplicity. Our computational pipeline for entanglement detection, applied for 1,017 non-redundant models from RNA-Puzzles, has shown the frequency of different entanglements and allowed identifying 138 structures with intersected assemblies.
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Affiliation(s)
- Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Agnieszka Korpeta
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Ryszard W Adamiak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
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29
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Singh J, Paliwal K, Singh J, Zhou Y. RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks. J Chem Inf Model 2021; 61:2610-2622. [PMID: 34037398 DOI: 10.1021/acs.jcim.1c00153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to paired bases, and the conformational space of the ribose-phosphate backbone is still too large to be sampled efficiently. Here, we employed the dilated convolutional neural network to predict backbone torsion and pseudotorsion angles using a single RNA sequence as input. The method called SPOT-RNA-1D was trained on a high-resolution training data set and tested on three independent, nonredundant, and high-resolution test sets. The proposed method yields substantially smaller mean absolute errors than the baseline predictors based on random predictions and based on helix conformations according to actual angle distributions. The mean absolute errors for three test sets range from 14°-44° for different angles, compared to 17°-62° by random prediction and 14°-58° by helix prediction. The method also accurately recovers the overall patterns of single or pairwise angle distributions. In general, torsion angles further away from the bases and associated with unpaired bases and paired bases involved in tertiary interactions are more difficult to predict. Compared to the best models in RNA-puzzles experiments, SPOT-RNA-1D yielded more accurate dihedral angles and, thus, are potentially useful as model quality indicators and restraints for RNA structure prediction as in protein structure prediction.
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Affiliation(s)
- Jaswinder Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Peking University Shenzhen Graduate School, Shenzhen 518055, P.R. China
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30
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Zhang T, Singh J, Litfin T, Zhan J, Paliwal K, Zhou Y. RNAcmap: A Fully Automatic Pipeline for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis. Bioinformatics 2021; 37:3494-3500. [PMID: 34021744 DOI: 10.1093/bioinformatics/btab391] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/27/2021] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. RESULTS We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. AVAILABILITY RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap_standalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap.
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Affiliation(s)
- Tongchuan Zhang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Thomas Litfin
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jian Zhan
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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31
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Zhu J, Li C, Peng X, Zhang X. RNA architecture influences plant biology. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:4144-4160. [PMID: 33484251 PMCID: PMC8130982 DOI: 10.1093/jxb/erab030] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/18/2021] [Indexed: 05/13/2023]
Abstract
The majority of the genome is transcribed to RNA in living organisms. RNA transcripts can form astonishing arrays of secondary and tertiary structures via Watson-Crick, Hoogsteen, or wobble base pairing. In vivo, RNA folding is not a simple thermodynamic event of minimizing free energy. Instead, the process is constrained by transcription, RNA-binding proteins, steric factors, and the microenvironment. RNA secondary structure (RSS) plays myriad roles in numerous biological processes, such as RNA processing, stability, transportation, and translation in prokaryotes and eukaryotes. Emerging evidence has also implicated RSS in RNA trafficking, liquid-liquid phase separation, and plant responses to environmental variations such as temperature and salinity. At molecular level, RSS is correlated with splicing, polyadenylation, protein synthesis, and miRNA biogenesis and functions. In this review, we summarize newly reported methods for probing RSS in vivo and functions and mechanisms of RSS in plant physiology.
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Affiliation(s)
- Jiaying Zhu
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | - Changhao Li
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | - Xu Peng
- Department of Medical Physiology, College of Medicine, Texas A&M University, College Station, TX 77843, USA
| | - Xiuren Zhang
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
- Correspondence:
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32
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Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement. Nat Commun 2021; 12:2777. [PMID: 33986288 PMCID: PMC8119458 DOI: 10.1038/s41467-021-23100-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/13/2021] [Indexed: 12/04/2022] Open
Abstract
Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.
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33
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Schlick T, Portillo-Ledesma S, Myers CG, Beljak L, Chen J, Dakhel S, Darling D, Ghosh S, Hall J, Jan M, Liang E, Saju S, Vohr M, Wu C, Xu Y, Xue E. Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field. Annu Rev Biophys 2021; 50:267-301. [PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York 10003, USA;
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
| | | | - Christopher G Myers
- Department of Chemistry, New York University, New York, New York 10003, USA;
| | - Lauren Beljak
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Justin Chen
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sami Dakhel
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Daniel Darling
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sayak Ghosh
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Joseph Hall
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mikaeel Jan
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Emily Liang
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sera Saju
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mackenzie Vohr
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Chris Wu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Yifan Xu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Eva Xue
- College of Arts and Science, New York University, New York, New York 10003, USA
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34
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Rangan R, Watkins AM, Chacon J, Kretsch R, Kladwang W, Zheludev IN, Townley J, Rynge M, Thain G, Das R. De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures. Nucleic Acids Res 2021; 49:3092-3108. [PMID: 33693814 PMCID: PMC8034642 DOI: 10.1093/nar/gkab119] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/08/2021] [Accepted: 02/16/2021] [Indexed: 12/12/2022] Open
Abstract
The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5' UTR; the reverse complement of the 5' UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3' UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m and 3' UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets ('FARFAR2-SARS-CoV-2', https://github.com/DasLab/FARFAR2-SARS-CoV-2; and 'FARFAR2-Apo-Riboswitch', at https://github.com/DasLab/FARFAR2-Apo-Riboswitch') include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.
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Affiliation(s)
- Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Andrew M Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford CA 94305, USA
| | - Jose Chacon
- Department of Biochemistry, Stanford University School of Medicine, Stanford CA 94305, USA
| | - Rachael Kretsch
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Wipapat Kladwang
- Department of Biochemistry, Stanford University School of Medicine, Stanford CA 94305, USA
| | - Ivan N Zheludev
- Department of Biochemistry, Stanford University School of Medicine, Stanford CA 94305, USA
| | | | - Mats Rynge
- Information Sciences Institute, University of Southern California, Marina Del Rey, CA 90292, USA
| | - Gregory Thain
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706 USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford CA 94305, USA
- Department of Physics, Stanford University, Stanford, CA 94305, USA
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35
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An RNA-centric historical narrative around the Protein Data Bank. J Biol Chem 2021; 296:100555. [PMID: 33744291 PMCID: PMC8080527 DOI: 10.1016/j.jbc.2021.100555] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 02/17/2021] [Accepted: 03/16/2021] [Indexed: 01/06/2023] Open
Abstract
Some of the amazing contributions brought to the scientific community by the Protein Data Bank (PDB) are described. The focus is on nucleic acid structures with a bias toward RNA. The evolution and key roles in science of the PDB and other structural databases for nucleic acids illustrate how small initial ideas can become huge and indispensable resources with the unflinching willingness of scientists to cooperate globally. The progress in the understanding of the molecular interactions driving RNA architectures followed the rapid increase in RNA structures in the PDB. That increase was consecutive to improvements in chemical synthesis and purification of RNA molecules, as well as in biophysical methods for structure determination and computer technology. The RNA modeling efforts from the early beginnings are also described together with their links to the state of structural knowledge and technological development. Structures of RNA and of its assemblies are physical objects, which, together with genomic data, allow us to integrate present-day biological functions and the historical evolution in all living species on earth.
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36
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Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes. Nat Commun 2021; 12:1576. [PMID: 33707432 PMCID: PMC7952577 DOI: 10.1038/s41467-021-21578-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 01/20/2021] [Indexed: 01/03/2023] Open
Abstract
We apply an oligo-library and machine learning-approach to characterize the sequence and structural determinants of binding of the phage coat proteins (CPs) of bacteriophages MS2 (MCP), PP7 (PCP), and Qβ (QCP) to RNA. Using the oligo library, we generate thousands of candidate binding sites for each CP, and screen for binding using a high-throughput dose-response Sort-seq assay (iSort-seq). We then apply a neural network to expand this space of binding sites, which allowed us to identify the critical structural and sequence features for binding of each CP. To verify our model and experimental findings, we design several non-repetitive binding site cassettes and validate their functionality in mammalian cells. We find that the binding of each CP to RNA is characterized by a unique space of sequence and structural determinants, thus providing a more complete description of CP-RNA interaction as compared with previous low-throughput findings. Finally, based on the binding spaces we demonstrate a computational tool for the successful design and rapid synthesis of functional non-repetitive binding-site cassettes. Phage-coat proteins can be used to build synthetic biology parts. Here the authors use an oligo library and machine learning to predict and verify sequences based on binding scores.
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37
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Abstract
The molecules of the ribonucleic acid (RNA) perform a variety of vital roles in all living cells. Their biological function depends on their structure and dynamics, both of which are difficult to experimentally determine but can be theoretically inferred based on the RNA sequence. SimRNA is one of the computational methods for molecular simulations of RNA 3D structure formation. The method is based on a simplified (coarse-grained) representation of nucleotide chains, a statistically derived model of interactions (statistical potential), and the Monte Carlo method as a conformational sampling scheme.The current version of SimRNA (3.22) is able to predict basic topologies of RNA molecules with sizes up to about 50-70 nucleotides, based on their sequences only, and larger molecules if supplied with appropriate distance restraints. The user can specify various types of restraints, including secondary structure, pairwise atom-atom distances, and positions of atoms. SimRNA can be also used for studying systems composed of several chains of RNA. SimRNA is a folding simulations method, thus it allows for examining folding pathways, getting an approximate view of the energy landscapes.
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38
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Zhang D, Li J, Chen SJ. IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures. J Chem Theory Comput 2021; 17:1842-1857. [PMID: 33560836 DOI: 10.1021/acs.jctc.0c01148] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.
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Affiliation(s)
- Dong Zhang
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Jun Li
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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39
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Abstract
Novel RNA motif design is of great practical importance for technology and medicine. Increasingly, computational design plays an important role in such efforts. Our coarse-grained RAG (RNA-As-Graphs) framework offers strategies for enumerating the universe of RNA 2D folds, selecting "RNA-like" candidates for design, and determining sequences that fold onto these candidates. In RAG, RNA secondary structures are represented as tree or dual graphs. Graphs with known RNA structures are called "existing", and the others are labeled "hypothetical". By using simplified features for RNA graphs, we have clustered the hypothetical graphs into "RNA-like" and "non-RNA-like" groups and proposed RNA-like graphs as candidates for design. Here, we propose a new way of designing graph features by using Fiedler vectors. The new features reflect graph shapes better, and they lead to a more clustered organization of existing graphs. We show significant increases in K-means clustering accuracy by using the new features (e.g., up to 95% and 98% accuracy for tree and dual graphs, respectively). In addition, we propose a scoring model for top graph candidate selection. This scoring model allows users to set a threshold for candidates, and it incorporates weighing of existing graphs based on their corresponding number of known RNAs. We include a list of top scored RNA-like candidates, which we hope will stimulate future novel RNA design.
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Affiliation(s)
- Qiyao Zhu
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, United States
| | - Tamar Schlick
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, United States
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, P. R. China
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40
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Arriola JT, Müller UF. A combinatorial method to isolate short ribozymes from complex ribozyme libraries. Nucleic Acids Res 2020; 48:e116. [PMID: 33035338 PMCID: PMC7672470 DOI: 10.1093/nar/gkaa834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 08/28/2020] [Accepted: 10/01/2020] [Indexed: 11/13/2022] Open
Abstract
In vitro selections are the only known methods to generate catalytic RNAs (ribozymes) that do not exist in nature. Such new ribozymes are used as biochemical tools, or to address questions on early stages of life. In both cases, it is helpful to identify the shortest possible ribozymes since they are easier to deploy as a tool, and because they are more likely to have emerged in a prebiotic environment. One of our previous selection experiments led to a library containing hundreds of different ribozyme clusters that catalyze the triphosphorylation of their 5'-terminus. This selection showed that RNA systems can use the prebiotically plausible molecule cyclic trimetaphosphate as an energy source. From this selected ribozyme library, the shortest ribozyme that was previously identified had a length of 67 nucleotides. Here we describe a combinatorial method to identify short ribozymes from libraries containing many ribozymes. Using this protocol on the library of triphosphorylation ribozymes, we identified a 17-nucleotide sequence motif embedded in a 44-nucleotide pseudoknot structure. The described combinatorial approach can be used to analyze libraries obtained by different in vitro selection experiments.
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Affiliation(s)
- Joshua T Arriola
- Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Ulrich F Müller
- Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, California 92093, USA
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41
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Abstract
The structural and regulatory elements in therapeutically relevant RNAs offer many opportunities for targeting by small molecules, yet fundamental understanding of what drives selectivity in small molecule:RNA recognition has been a recurrent challenge. In particular, RNAs tend to be more dynamic and offer less chemical functionality than proteins, and biologically active ligands must compete with the highly abundant and highly structured RNA of the ribosome. Indeed, the only small molecule drug targeting RNA other than the ribosome was just approved in August 2020, and our recent survey of the literature revealed fewer than 150 reported chemical probes that target non-ribosomal RNA in biological systems. This Feature outlines our efforts to improve small molecule targeting strategies and gain fundamental insights into small molecule:RNA recognition by analyzing patterns in both RNA-biased small molecule chemical space and RNA topological space privileged for differentiation. First, we synthesized libraries based on RNA binding scaffolds that allowed us to reveal general principles in small molecule:recognition and to ask precise chemical questions about drivers of affinity and selectivity. Elaboration of these scaffolds has led to recognition of medicinally relevant RNA targets, including viral and long noncoding RNA structures. More globally, we identified physicochemical, structural, and spatial properties of biologically active RNA ligands that are distinct from those of protein-targeted ligands, and we have provided the dataset and associated analytical tools as part of a publicly available online platform to facilitate RNA ligand discovery. At the same time, we used pattern recognition protocols to identify RNA topologies that can be differentially recognized by small molecules and have elaborated this technique to visualize conformational changes in RNA secondary structure. These fundamental insights into the drivers of RNA recognition in vitro have led to functional targeting of RNA structures in biological systems. We hope that these initial guiding principles, as well as the approaches and assays developed in their pursuit, will enable rapid progress toward the development of RNA-targeted chemical probes and ultimately new therapeutic approaches to a wide range of deadly human diseases.
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Affiliation(s)
- Amanda E Hargrove
- Department of Chemistry, Duke University, 124 Science Drive, Box 90346, Durham, NC 27708, USA.
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42
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Richardson KE, Kirkpatrick CC, Znosko BM. RNA CoSSMos 2.0: an improved searchable database of secondary structure motifs in RNA three-dimensional structures. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5707338. [PMID: 31950189 PMCID: PMC6966092 DOI: 10.1093/database/baz153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/12/2019] [Accepted: 12/13/2019] [Indexed: 01/29/2023]
Abstract
The RNA Characterization of Secondary Structure Motifs, RNA CoSSMos, database is a freely accessible online database that allows users to identify secondary structure motifs among RNA 3D structures and explore their structural features. RNA CoSSMos 2.0 now requires two closing base pairs for all RNA loop motifs to create a less redundant database of secondary structures. Furthermore, RNA CoSSMos 2.0 represents an upgraded database with new features that summarize search findings and aid in the search for 3D structural patterns among RNA secondary structure motifs. Previously, users were limited to viewing search results individually, with no built-in tools to compare search results. RNA CoSSMos 2.0 provides two new features, allowing users to summarize, analyze and compare their search result findings. A function has been added to the website that calculates the average and representative structures of the search results. Additionally, users can now view a summary page of their search results that reports percentages of each structural feature found, including sugar pucker, glycosidic linkage, hydrogen bonding patterns and stacking interactions. Other upgrades include a newly embedded NGL structural viewer, the option to download the clipped structure coordinates in *.pdb format and improved NMR structure results. RNA CoSSMos 2.0 is no longer simply a search engine for a structure database; it now has the capability of analyzing, comparing and summarizing search results. Database URL: http://rnacossmos.com
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Affiliation(s)
- Katherine E Richardson
- Saint Louis University, Department of Chemistry, 3501 Laclede Avenue, St. Louis, MO 63103 USA
| | - Charles C Kirkpatrick
- Saint Louis University, Department of Chemistry, 3501 Laclede Avenue, St. Louis, MO 63103 USA
| | - Brent M Znosko
- Saint Louis University, Department of Chemistry, 3501 Laclede Avenue, St. Louis, MO 63103 USA
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43
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Li B, Cao Y, Westhof E, Miao Z. Advances in RNA 3D Structure Modeling Using Experimental Data. Front Genet 2020; 11:574485. [PMID: 33193680 PMCID: PMC7649352 DOI: 10.3389/fgene.2020.574485] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/02/2020] [Indexed: 12/26/2022] Open
Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
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Affiliation(s)
- Bing Li
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France
| | - Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
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44
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Xu X, Chen SJ. Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction. Nucleic Acids Res 2020; 48:6503-6512. [PMID: 32491164 PMCID: PMC7337929 DOI: 10.1093/nar/gkaa463] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 05/19/2020] [Indexed: 01/23/2023] Open
Abstract
An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.
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Affiliation(s)
- Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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45
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3dRNA: Building RNA 3D structure with improved template library. Comput Struct Biotechnol J 2020; 18:2416-2423. [PMID: 33005304 PMCID: PMC7508704 DOI: 10.1016/j.csbj.2020.08.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/18/2020] [Accepted: 08/21/2020] [Indexed: 11/22/2022] Open
Abstract
Most of computational methods of building RNA tertiary structure are template-based. The template-based methods usually can give more accurate 3D structures due to the use of native 3D templates, but they cannot work if the 3D templates are not available. So, a more complete library of the native 3D templates is very important for this type of methods. 3dRNA is a template-based method for building RNA tertiary structure previously proposed by us. In this paper we report improved 3D template libraries of 3dRNA by using two different schemes that give two libraries 3dRNA_Lib1 and 3dRNA_Lib2. These libraries expand the original one by nearly ten times. Benchmark shows that they can significantly increase the accuracy of 3dRNA, especially in building complex and large RNA 3D structures.
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46
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Yan S, Peck JM, Ilgu M, Nilsen-Hamilton M, Lamm MH. Sampling Performance of Multiple Independent Molecular Dynamics Simulations of an RNA Aptamer. ACS OMEGA 2020; 5:20187-20201. [PMID: 32832772 PMCID: PMC7439393 DOI: 10.1021/acsomega.0c01867] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/22/2020] [Indexed: 06/11/2023]
Abstract
Using multiple independent simulations instead of one long simulation has been shown to improve the sampling performance attained with the molecular dynamics (MD) simulation method. However, it is generally not known how long each independent simulation should be, how many independent simulations should be used, or to what extent either of these factors affects the overall sampling performance achieved for a given system. The goal of the present study was to assess the sampling performance of multiple independent MD simulations, where each independent simulation begins from a different initial molecular conformation. For this purpose, we used an RNA aptamer that is 25 nucleotides long as a case study. The initial conformations of the aptamer are derived from six de novo predicted 3D structures. Each of the six de novo predicted structures is energy minimized in solution and equilibrated with MD simulations at high temperature. Ten conformations from these six high-temperature equilibration runs are selected as initial conformations for further simulations at ambient temperature. In total, we conducted 60 independent MD simulations, each with a duration of 100 ns, to study the conformation and dynamics of the aptamer. For each group of 10 independent simulations that originated from a particular de novo predicted structure, we evaluated the potential energy distribution of the RNA and used recurrence quantification analysis to examine the sampling of RNA conformational transitions. To assess the impact of starting from different de novo predicted structures, we computed the density of structure projection on principal components to compare the regions sampled by the different groups of ten independent simulations. The recurrence rate and dependence of initial conformation among the groups were also compared. We stress the necessity of using different initial configurations as simulation starting points by showing long simulations from different initial structures suffer from being trapped in different states. Finally, we summarized the sampling efficiency for the complete set of 60 independent simulations and determined regions of under-sampling on the potential energy landscape. The results suggest that conducting multiple independent simulations using a diverse set of de novo predicted structures is a promising approach to achieve sufficient sampling. This approach avoids undesirable outcomes, such as the problem of the RNA aptamer being trapped in a local minimum. For others wishing to conduct multiple independent simulations, the analysis protocol presented in this study is a guide for examining overall sampling and determining if more simulations are necessary for sufficient sampling.
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Affiliation(s)
- Shuting Yan
- Department
of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011, United States
| | - Jason M. Peck
- Department
of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011, United States
| | - Muslum Ilgu
- Roy
J Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
- Aptalogic
Inc., Ames, Iowa 50014, United States
- Department
of Biological Sciences, Middle East Technical
University, Ankara, Ankara 06800, Turkey
| | - Marit Nilsen-Hamilton
- Roy
J Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
- Aptalogic
Inc., Ames, Iowa 50014, United States
| | - Monica H. Lamm
- Department
of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011, United States
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47
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Perret G, Boschetti E. Aptamer-Based Affinity Chromatography for Protein Extraction and Purification. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020; 174:93-139. [PMID: 31485702 DOI: 10.1007/10_2019_106] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Aptamers are oligonucleotide molecules able to recognize very specifically proteins. Among the possible applications, aptamers have been used for affinity chromatography with effective results and advantages over most advanced protein separation technologies. This chapter first discusses the context of the affinity chromatography with aptamer ligands. With the adaptation of SELEX, the chemical modifications of aptamers to comply with the covalent coupling and the separation process are then extensively presented. A focus is then made about the most important applications for protein separation with real-life examples and the comparison with immunoaffinity chromatography. In spite of well-advanced demonstrations and the extraordinary potential developments, a significant optimization work is still due to deserve large-scale applications with all necessary validations. Graphical Abstract Aptamer-protein complexes by X-ray crystallography.
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48
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Watkins AM, Rangan R, Das R. FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds. Structure 2020; 28:963-976.e6. [PMID: 32531203 PMCID: PMC7415647 DOI: 10.1016/j.str.2020.05.011] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/27/2020] [Accepted: 05/20/2020] [Indexed: 01/01/2023]
Abstract
Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.
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Affiliation(s)
- Andrew Martin Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Biophysics Program, Stanford University, Stanford, CA 94305, USA.
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Miao Z, Adamiak RW, Antczak M, Boniecki MJ, Bujnicki J, Chen SJ, Cheng CY, Cheng Y, Chou FC, Das R, Dokholyan NV, Ding F, Geniesse C, Jiang Y, Joshi A, Krokhotin A, Magnus M, Mailhot O, Major F, Mann TH, Piątkowski P, Pluta R, Popenda M, Sarzynska J, Sun L, Szachniuk M, Tian S, Wang J, Wang J, Watkins AM, Wiedemann J, Xiao Y, Xu X, Yesselman JD, Zhang D, Zhang Y, Zhang Z, Zhao C, Zhao P, Zhou Y, Zok T, Żyła A, Ren A, Batey RT, Golden BL, Huang L, Lilley DM, Liu Y, Patel DJ, Westhof E. RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers. RNA (NEW YORK, N.Y.) 2020; 26:982-995. [PMID: 32371455 PMCID: PMC7373991 DOI: 10.1261/rna.075341.120] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 04/03/2020] [Indexed: 05/21/2023]
Abstract
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.
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Affiliation(s)
- Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, CB10 1SD, United Kingdom
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - 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
| | - 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
| | - Michał J Boniecki
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Janusz Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Clarence Yu Cheng
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Yi Cheng
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Fang-Chieh Chou
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania, 17033, USA
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania, 17033, USA
| | - Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Yangwei Jiang
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Astha Joshi
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Andrey Krokhotin
- Department of Biochemistry and Biophysics and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Departments of Pathology, Genetics and Developmental Biology, Howard Hughes Medical Institute, Stanford Medical School, Palo Alto, California, 94305, USA
| | - Marcin Magnus
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Olivier Mailhot
- Institute for Research in Immunology and Cancer (IRIC), Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, H3C 3J7, Canada
| | - Francois Major
- Institute for Research in Immunology and Cancer (IRIC), Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, H3C 3J7, Canada
| | - Thomas H Mann
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Paweł Piątkowski
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Radoslaw Pluta
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 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
| | - Lizhen Sun
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - 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
| | - Siqi Tian
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania, 17033, USA
| | - Jun Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Andrew M Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Jakub Wiedemann
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Yi Xiao
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Xiaojun Xu
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Joseph D Yesselman
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Dong Zhang
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Yi Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Zhenzhen Zhang
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Chenhan Zhao
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Peinan Zhao
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Yuanzhe Zhou
- Department of Physics and Astronomy, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Missouri 65211, USA
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland
| | - Adriana Żyła
- International Institute of Molecular and Cell Biology in Warsaw, Księcia Trojdena 4, 02-109 Warsaw, Poland
| | - Aiming Ren
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Robert T Batey
- Department of Biochemistry, University of Colorado at Boulder, Campus Box 596, Boulder, Colorado 80309-0596, USA
| | - Barbara L Golden
- Department of Biochemistry, Purdue University, West Lafayette, Indiana 47907, USA
| | - Lin Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, P. R. China
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, P. R. China
- Cancer Research UK Nucleic Acid Structure Research Group, MSI/WTB Complex, The University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom
| | - David M Lilley
- Cancer Research UK Nucleic Acid Structure Research Group, MSI/WTB Complex, The University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom
| | - Yijin Liu
- Cancer Research UK Nucleic Acid Structure Research Group, MSI/WTB Complex, The University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom
| | - Dinshaw J Patel
- Structural Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Eric Westhof
- Arch et Reactivite de l'ARN, Univ de Strasbourg, Inst de Biol Mol et Cell du CNRS, 67084 Strasbourg, France
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Pucci F, Zerihun MB, Peter EK, Schug A. Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set. RNA (NEW YORK, N.Y.) 2020; 26:794-802. [PMID: 32276988 PMCID: PMC7297115 DOI: 10.1261/rna.073809.119] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.
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Affiliation(s)
- Fabrizio Pucci
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Mehari B Zerihun
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Department of Physics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Emanuel K Peter
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Alexander Schug
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
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