1
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Sun JW, Thomas JS, Monkovic JM, Gibson H, Nagapurkar A, Frezzo JA, Katyal P, Punia K, Mahmoudinobar F, Renfrew PD, Montclare JK. Supercharged coiled-coil protein with N-terminal decahistidine tag boosts siRNA complexation and delivery efficiency of a lipoproteoplex. J Pept Sci 2024; 30:e3594. [PMID: 38499991 DOI: 10.1002/psc.3594] [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: 11/30/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/20/2024]
Abstract
Short interfering RNA (siRNA) therapeutics have soared in popularity due to their highly selective and potent targeting of faulty genes, providing a non-palliative approach to address diseases. Despite their potential, effective transfection of siRNA into cells requires the assistance of an accompanying vector. Vectors constructed from non-viral materials, while offering safer and non-cytotoxic profiles, often grapple with lackluster loading and delivery efficiencies, necessitating substantial milligram quantities of expensive siRNA to confer the desired downstream effects. We detail the recombinant synthesis of a diverse series of coiled-coil supercharged protein (CSP) biomaterials systematically designed to investigate the impact of two arginine point mutations (Q39R and N61R) and decahistidine tags on liposomal siRNA delivery. The most efficacious variant, N8, exhibits a twofold increase in its affinity to siRNA and achieves a twofold enhancement in transfection activity with minimal cytotoxicity in vitro. Subsequent analysis unveils the destabilizing effect of the Q39R and N61R supercharging mutations and the incorporation of C-terminal decahistidine tags on α-helical secondary structure. Cross-correlational regression analyses reveal that the amount of helical character in these mutants is key in N8's enhanced siRNA complexation and downstream delivery efficiency.
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Affiliation(s)
- Jonathan W Sun
- Department of Chemistry, New York University, New York, New York, USA
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Joseph S Thomas
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Julia M Monkovic
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Halle Gibson
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Akash Nagapurkar
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Joseph A Frezzo
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Priya Katyal
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Kamia Punia
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
| | - Farbod Mahmoudinobar
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Jin Kim Montclare
- Department of Chemistry, New York University, New York, New York, USA
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, New York, USA
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Biomaterials, NYU College of Dentistry, New York, New York, USA
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2
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Thiel BC, Bussi G, Poblete S, Hofacker IL. Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data. Nucleic Acids Res 2024:gkae602. [PMID: 39021350 DOI: 10.1093/nar/gkae602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/05/2024] [Indexed: 07/20/2024] Open
Abstract
The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.
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Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, via Bonomea 265, Trieste 34136, Italy
| | - Simón Poblete
- Centro BASAL Ciencia & Vida, Avenida del Valle Norte 725, Santiago 8580702, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
- Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna 1090, Austria
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3
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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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4
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Beckmann IK, Waldl M, Will S, Hofacker IL. 3D feasibility of 2D RNA-RNA interaction paths by stepwise folding simulations. RNA (NEW YORK, N.Y.) 2024; 30:113-123. [PMID: 38071473 PMCID: PMC10798244 DOI: 10.1261/rna.079756.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/16/2023] [Indexed: 01/18/2024]
Abstract
The structure of an RNA, and even more so its interactions with other RNAs, provide valuable information about its function. Secondary structure-based tools for RNA-RNA interaction predictions provide a quick way to identify possible interaction targets and structures. However, these tools ignore the effect of steric hindrance on the tertiary (3D) structure level, and do not consider whether a suitable folding pathway exists to form the interaction. As a consequence, these tools often predict interactions that are unrealistically long and could be formed (in three dimensions) only by going through highly entangled intermediates. Here, we present a computational pipeline to assess whether a proposed secondary (2D) structure interaction is sterically feasible and reachable along a plausible folding pathway. To this end, we simulate the folding of a series of 3D structures along a given 2D folding path. To avoid the complexity of large-scale atomic resolution simulations, our pipeline uses coarse-grained 3D modeling and breaks up the folding path into small steps, each corresponding to the extension of the interaction by 1 or 2 bp. We apply our pipeline to analyze RNA-RNA interaction formation for three selected RNA-RNA complexes. We find that kissing hairpins, in contrast to interactions in the exterior loop, are difficult to extend and tend to get stuck at an interaction length of 6 bp. Our tool, including source code, documentation, and sample data, is available at www.github.com/irenekb/RRI-3D.
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Affiliation(s)
- Irene K Beckmann
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Wien, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, A-1030 Vienna, Austria
| | - Maria Waldl
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Wien, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, 1090 Vienna, Austria
- Center for Anatomy and Cell Biology, Medical University of Vienna, 1090 Vienna, Austria
| | - Sebastian Will
- LIX - Batiment Turing, Ecole Polytechnique, 91120 Palaiseau, France
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Wien, Austria
- Faculty of Computer Science, Research Group Bioinformatics and Computational Biology, University of Vienna, 1090 Vienna, Austria
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5
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Thiel BC, Poblete S, Hofacker IL. The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline. Methods Mol Biol 2024; 2726:377-399. [PMID: 38780739 DOI: 10.1007/978-1-0716-3519-3_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.
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Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Simón Poblete
- Instituto de Ciencias Físicas y Matemáticas, Universidad Austral de Chile, Valdivia, Chile
- Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad SanSebastián, Santiago, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
- Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria.
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6
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Wang F, Xia R, Su Y, Cai P, Xu X. Quantifying RNA structures and interactions with a unified reduced chain representation model. Int J Biol Macromol 2023; 253:127181. [PMID: 37793523 DOI: 10.1016/j.ijbiomac.2023.127181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/30/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023]
Abstract
RNA is a pivotal molecule that plays critical roles in various cellular processes. Quantifying RNA structures and interactions is essential to understanding RNA function and developing RNA-based therapeutics. Using a unified five-bead model and a non-redundant database, this paper investigates the structural features and interactions of five commonly occurring RNA motifs, i.e., double-stranded helices, hairpin loops, internal/bulge loops, multi-branched junctions, and single-stranded terminal tails. Analyzing detailed distributions of RNA local structural features and base-base interactions reveals a preference for helical structures in both local backbone structures and base orientations. The interactions between adjacent bases exhibit motif-specific and sequence-dependent characteristics, reflecting the distinct topological constraints imposed by different loop-helix connection modes and the varying pairing and stacking interactions among different sequences. These findings shed light on the stability of RNA helices, emphasizing their significance in providing dominant base pairing and stacking interactions for RNA structures and stability. The four non-helix motifs encompass unpaired nucleotide loops and exhibit diverse base-base interactions, contributing to the structural diversity observed in RNA. Overall, the complexity of RNA structure arises from the intricate interplay of base-base interactions.
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Affiliation(s)
- Fengfei Wang
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Renjie Xia
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Yangyang Su
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Pinggen Cai
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou 310023, China.
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China.
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7
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Rocca R, Grillone K, Citriniti EL, Gualtieri G, Artese A, Tagliaferri P, Tassone P, Alcaro S. Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches. Eur J Med Chem 2023; 261:115850. [PMID: 37839343 DOI: 10.1016/j.ejmech.2023.115850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023]
Abstract
The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.
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Affiliation(s)
- Roberta Rocca
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy
| | - Katia Grillone
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | | | | | - Anna Artese
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy.
| | | | - Pierfrancesco Tassone
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Stefano Alcaro
- Department of Health Science, Magna Graecia University, Catanzaro, Italy; Net4Science srl, Academic Spinoff, Magna Græcia University, Catanzaro, Italy
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8
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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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9
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Zhang D, Gong L, Weng J, Li Y, Wang A, Li G. RNA Folding Based on 5 Beads Model and Multiscale Simulation. Interdiscip Sci 2023:10.1007/s12539-023-00561-3. [PMID: 37115389 DOI: 10.1007/s12539-023-00561-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 04/29/2023]
Abstract
RNA folding prediction is very meaningful and challenging. The molecular dynamics simulation (MDS) of all atoms (AA) is limited to the folding of small RNA molecules. At present, most of the practical models are coarse grained (CG) model, and the coarse-grained force field (CGFF) parameters usually depend on known RNA structures. However, the limitation of the CGFF is obvious that it is difficult to study the modified RNA. Based on the 3 beads model (AIMS_RNA_B3), we proposed the AIMS_RNA_B5 model with three beads representing a base and two beads representing the main chain (sugar group and phosphate group). We first run the all atom molecular dynamic simulation (AAMDS), and fit the CGFF parameter with the AA trajectory. Then perform the coarse-grained molecular dynamic simulation (CGMDS). AAMDS is the foundation of CGMDS. CGMDS is mainly to carry out the conformation sampling based on the current AAMDS state and improve the folding speed. We simulated the folding of three RNAs, which belong to hairpin, pseudoknot and tRNA respectively. Compared to the AIMS_RNA_B3 model, the AIMS_RNA_B5 model is more reasonable and performs better.
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Affiliation(s)
- Dinglin Zhang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lidong Gong
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, China
| | - Junben Weng
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Anhui Wang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Guohui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
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10
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Li J, Chen SJ. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling. Nucleic Acids Res 2023; 51:3341-3356. [PMID: 36864729 PMCID: PMC10123122 DOI: 10.1093/nar/gkad122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/14/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
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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, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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11
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Abstract
For more than three decades, RNA has been known to be a relevant and attractive macromolecule to target but figuring out which RNA should be targeted and how remains challenging. Recent years have seen the confluence of approaches for screening, drug optimization, and target validation that have led to the approval of a few RNA-targeting therapeutics for clinical applications. This focused perspective aims to highlight - but not exhaustively review - key factors accounting for these successes while pointing at crucial aspects worth considering for further breakthroughs.
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Affiliation(s)
- Quentin Vicens
- Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO 80045, USA
| | - Eric Westhof
- Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire, Architecture et Réactivité de l’ARN, CNRS UPR 9002, 2, allée Konrad Roentgen, F-67084 Strasbourg, France
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12
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Kumar J, Lackey L, Waldern JM, Dey A, Mustoe AM, Weeks KM, Mathews DH, Laederach A. Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing. eLife 2022; 11:73888. [PMID: 35695373 PMCID: PMC9236610 DOI: 10.7554/elife.73888] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 06/12/2022] [Indexed: 11/29/2022] Open
Abstract
Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.
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Affiliation(s)
- Jayashree Kumar
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Lela Lackey
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States.,Department of Genetics and Biochemistry, Center for Human Genetics, Clemson University, Greenwood, United States
| | - Justin M Waldern
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Abhishek Dey
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Anthony M Mustoe
- Verna and Marrs McClean Department of Biochemistry and Molecular Biology, Therapeutic Innovation Center (THINC), and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
| | - Kevin M Weeks
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - David H Mathews
- Department of Biochemistry & Biophysics and Center for RNA Biology, School of Medicine and Dentistry, University of Rochester, Rochester, United States
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
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13
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Gosavi D, Wower I, Beckmann IK, Hofacker IL, Wower J, Wolfinger MT, Sztuba-Solinska J. Insights into the secondary and tertiary structure of the Bovine Viral Diarrhea Virus Internal Ribosome Entry Site. RNA Biol 2022; 19:496-506. [PMID: 35380920 PMCID: PMC8986297 DOI: 10.1080/15476286.2022.2058818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
The internal ribosome entry site (IRES) RNA of bovine viral diarrhoea virus (BVDV), an economically significant Pestivirus, is required for the cap-independent translation of viral genomic RNA. Thus, it is essential for viral replication and pathogenesis. We applied a combination of high-throughput biochemical RNA structure probing (SHAPE-MaP) and in silico modelling approaches to gain insight into the secondary and tertiary structures of BVDV IRES RNA. Our study demonstrated that BVDV IRES RNA in solution forms a modular architecture composed of three distinct structural domains (I-III). Two regions within domain III are represented in tertiary interactions to form an H-type pseudoknot. Computational modelling of the pseudoknot motif provided a fine-grained picture of the tertiary structure and local arrangement of helices in the BVDV IRES. Furthermore, comparative genomics and consensus structure predictions revealed that the pseudoknot is evolutionarily conserved among many Pestivirus species. These studies provide detailed insight into the structural arrangement of BVDV IRES RNA H-type pseudoknot and encompassing motifs that likely contribute to the optimal functionality of viral cap-independent translation element.
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Affiliation(s)
- Devadatta Gosavi
- Department of Biological Sciences, Auburn University, 120 W. Samford Ave, Rouse Life Sciences Building, Auburn, AL, United States
| | - Iwona Wower
- Department of Animal and Dairy Sciences, Auburn University, Auburn, AL, United States
| | - Irene K Beckmann
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Jacek Wower
- Department of Animal and Dairy Sciences, Auburn University, Auburn, AL, United States
| | - Michael T Wolfinger
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria.,Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Joanna Sztuba-Solinska
- Department of Biological Sciences, Auburn University, 120 W. Samford Ave, Rouse Life Sciences Building, Auburn, AL, United States.,Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
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14
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Poblete S, Guzman HV. Structural 3D Domain Reconstruction of the RNA Genome from Viruses with Secondary Structure Models. Viruses 2021; 13:1555. [PMID: 34452420 PMCID: PMC8402887 DOI: 10.3390/v13081555] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/28/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023] Open
Abstract
Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches.
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Affiliation(s)
- Simón Poblete
- Instituto de Ciencias Físicas y Matemáticas, Universidad Austral de Chile, Valdivia 5091000, Chile
- Chile and Computational Biology Lab, Fundación Ciencia & Vida, Santiago 7780272, Chile
| | - Horacio V. Guzman
- Department of Theoretical Physics, Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia
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15
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Li J, Chen SJ. RNA 3D Structure Prediction Using Coarse-Grained Models. Front Mol Biosci 2021; 8:720937. [PMID: 34277713 PMCID: PMC8283274 DOI: 10.3389/fmolb.2021.720937] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022] Open
Abstract
The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.
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Affiliation(s)
| | - Shi-Jie Chen
- Departments of Physics and Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, MO, United States
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16
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Christy TW, Giannetti CA, Houlihan G, Smola MJ, Rice GM, Wang J, Dokholyan NV, Laederach A, Holliger P, Weeks KM. Direct Mapping of Higher-Order RNA Interactions by SHAPE-JuMP. Biochemistry 2021; 60:1971-1982. [PMID: 34121404 PMCID: PMC8256721 DOI: 10.1021/acs.biochem.1c00270] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Higher-order structure governs function for many RNAs. However, discerning this structure for large RNA molecules in solution is an unresolved challenge. Here, we present SHAPE-JuMP (selective 2'-hydroxyl acylation analyzed by primer extension and juxtaposed merged pairs) to interrogate through-space RNA tertiary interactions. A bifunctional small molecule is used to chemically link proximal nucleotides in an RNA structure. The RNA cross-link site is then encoded into complementary DNA (cDNA) in a single, direct step using an engineered reverse transcriptase that "jumps" across cross-linked nucleotides. The resulting cDNAs contain a deletion relative to the native RNA sequence, which can be detected by sequencing, that indicates the sites of cross-linked nucleotides. SHAPE-JuMP measures RNA tertiary structure proximity concisely across large RNA molecules at nanometer resolution. SHAPE-JuMP is especially effective at measuring interactions in multihelix junctions and loop-to-helix packing, enables modeling of the global fold for RNAs up to several hundred nucleotides in length, facilitates ranking of structural models by consistency with through-space restraints, and is poised to enable solution-phase structural interrogation and modeling of complex RNAs.
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Affiliation(s)
- Thomas W. Christy
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Catherine A. Giannetti
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Gillian Houlihan
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Matthew J. Smola
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Greggory M. Rice
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
| | - Jian Wang
- Departments of Pharmacology, and Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
| | - Nikolay V. Dokholyan
- Departments of Pharmacology, and Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
- Departments of Chemistry, and Biomedical Engineering, Pennsylvania State University, University Park, PA 16802
| | - Alain Laederach
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Philipp Holliger
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Kevin M. Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290
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17
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Yan Z, Hamilton WL, Blanchette M. Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions. Bioinformatics 2021; 36:i276-i284. [PMID: 32657407 PMCID: PMC7355240 DOI: 10.1093/bioinformatics/btaa456] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Motivation RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor. Results In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs. Availability and implementation Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zichao Yan
- School of Computer Science, McGill University, Montreal, QC H3A 2B2, Canada.,MILA, Quebec AI Institute, Montreal, QC H2S 3H1, Canada
| | - William L Hamilton
- School of Computer Science, McGill University, Montreal, QC H3A 2B2, Canada.,MILA, Quebec AI Institute, Montreal, QC H2S 3H1, Canada
| | - Mathieu Blanchette
- School of Computer Science, McGill University, Montreal, QC H3A 2B2, Canada
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18
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Liu Z, Liu Q, Yang X, Zhang Y, Norris M, Chen X, Cheema J, Zhang H, Ding Y. In vivo nuclear RNA structurome reveals RNA-structure regulation of mRNA processing in plants. Genome Biol 2021; 22:11. [PMID: 33397430 PMCID: PMC7784297 DOI: 10.1186/s13059-020-02236-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 12/11/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND mRNA processing is critical for gene expression. A challenge in regulating mRNA processing is how to recognize the actual mRNA processing sites, such as splice and polyadenylation sites, when the sequence content is insufficient for this purpose. Previous studies suggested that RNA structure affects mRNA processing. However, the regulatory role of RNA structure in mRNA processing remains unclear. RESULTS Here, we perform in vivo selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical profiling on Arabidopsis and generate the in vivo nuclear RNA structure landscape. We find that nuclear mRNAs fold differently from cytosolic mRNAs across translation start and stop sites. Notably, we discover a two-nucleotide single-stranded RNA structure feature upstream of 5' splice sites that is strongly associated with splicing and the selection of alternative 5' splice sites. The regulatory role of this RNA structure feature is further confirmed by experimental validation. Moreover, we find the single-strandedness of branch sites is also associated with 3' splice site recognition. We also identify an RNA structure feature comprising two close-by single-stranded regions that is specifically associated with both polyadenylation and alternative polyadenylation events. CONCLUSIONS We successfully identify pre-mRNA structure features associated with splicing and polyadenylation at whole-genome scale and validate an RNA structure feature which can regulate splicing. Our study unveils a new RNA structure regulatory mechanism for mRNA processing.
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Affiliation(s)
- Zhenshan Liu
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Qi Liu
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Xiaofei Yang
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Yueying Zhang
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Matthew Norris
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Xiaoxi Chen
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Jitender Cheema
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
| | - Huakun Zhang
- Key Laboratory of Molecular Epigenetics of the Ministry of Education, Northeast Normal University, Changchun, 130024 China
| | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
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19
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Schmidt M, Hamacher K, Reinhardt F, Lotz TS, Groher F, Suess B, Jager S. SICOR: Subgraph Isomorphism Comparison of RNA Secondary Structures. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2189-2195. [PMID: 31295116 DOI: 10.1109/tcbb.2019.2926711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
RNA aptamer selection during SELEX experiments builds on secondary structural diversity. Advanced structural comparison methods can focus this diversity. We develop SICOR, which uses probabilistic subgraph isomorphisms for graph distances between RNA secondary structure graphs. SICOR outperforms other comparison methods and is applicable to many structural comparisons in experimental design.
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20
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Chillón I, Marcia M. The molecular structure of long non-coding RNAs: emerging patterns and functional implications. Crit Rev Biochem Mol Biol 2020; 55:662-690. [PMID: 33043695 DOI: 10.1080/10409238.2020.1828259] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.
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Affiliation(s)
- Isabel Chillón
- European Molecular Biology Laboratory (EMBL) Grenoble, Grenoble, France
| | - Marco Marcia
- European Molecular Biology Laboratory (EMBL) Grenoble, Grenoble, France
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21
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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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22
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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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23
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Badu S, Melnik R, Singh S. Mathematical and computational models of RNA nanoclusters and their applications in data-driven environments. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1804564] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Shyam Badu
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
- BCAM-Basque Center for Applied Mathematics, Bilbao, Spain
| | - Sundeep Singh
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
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24
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Yan Z, Lécuyer E, Blanchette M. Prediction of mRNA subcellular localization using deep recurrent neural networks. Bioinformatics 2020; 35:i333-i342. [PMID: 31510698 PMCID: PMC6612824 DOI: 10.1093/bioinformatics/btz337] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood. RESULTS We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker's strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts. AVAILABILITY AND IMPLEMENTATION Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zichao Yan
- School of Computer Science, McGill University, Montreal, QC, Canada
| | - Eric Lécuyer
- Department of Biochemistry, University of Montreal, Montreal, QC, Canada.,Institut de Recherches Clinique de Montréal (IRCM), Montreal, QC, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada
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25
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Magnus M, Antczak M, Zok T, Wiedemann J, Lukasiak P, Cao Y, Bujnicki JM, Westhof E, Szachniuk M, Miao Z. RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools. Nucleic Acids Res 2020; 48:576-588. [PMID: 31799609 PMCID: PMC7145511 DOI: 10.1093/nar/gkz1108] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/06/2019] [Accepted: 11/15/2019] [Indexed: 12/12/2022] Open
Abstract
Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.
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Affiliation(s)
- Marcin Magnus
- International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
- ReMedy-International Research Agenda Unit, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Maciej Antczak
- Institute of Computing Science & 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
| | - Tomasz Zok
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
| | - Jakub Wiedemann
- Institute of Computing Science & 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
| | - Piotr Lukasiak
- Institute of Computing Science & 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
| | - 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 610065, PR China
| | - Janusz M Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
- Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 12 allée Konrad Roentgen, 67084 Strasbourg, France
| | - Marta Szachniuk
- Institute of Computing Science & 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
- 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, Cambridge CB10 1SD, UK
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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26
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Poblete S, Pérez-Acle T. Brief Comparison between Experimental and Computationally Generated Ensembles of RNA Dinucleotides. J Chem Inf Model 2020; 60:989-994. [PMID: 31891267 DOI: 10.1021/acs.jcim.9b00913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The computational modeling of RNA and its interactions is of crucial importance for the understanding of the wide variety of biological functions it performs. Among these approaches, several coarse-grained models employ structural databases to derive their energy functions or to define scoring functions for structure prediction purposes. In many cases, the parametrization is done by using as a reference a set of experimentally determined structures or data obtained from Molecular Dynamics simulations. Since the two choices are clearly different, we present here a brief comparison of the essential spaces of a set of conformations of two topologically connected nucleotides generated by these means. We find that when the nucleotides are embedded into a duplex, the essential spaces of both ensembles are quite restricted and do not differ substantially. Nevertheless, when the conformational space of a free dinucleoside monophosphate simulation is compared against a similar set obtained from the structural database, the differences of the essential spaces are considerable. In addition, we show a brief comparison of a specific distance between the nucleotides which correlates with the sugar pucker of the first nucleotide and analyze its distribution under similar conditions.
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Affiliation(s)
- Simón Poblete
- Instituto de Ciencias Físicas y Matemáticas , Universidad Austral de Chile , Casilla 567 , Valdivia 5090000 , Chile.,Computational Biology Lab , Fundación Ciencia & Vida , Avenida Zañartu 1482 , Ñuñoa, Santiago 7780272 , Chile
| | - Tomás Pérez-Acle
- Computational Biology Lab , Fundación Ciencia & Vida , Avenida Zañartu 1482 , Ñuñoa, Santiago 7780272 , Chile.,Centro Interdisciplinario de Neurociencia de Valparaı́so , Universidad de Valparaı́so , Pasaje Harrington 287 , Playa Ancha , Valparaı́so , Chile.,Universidad San Sebastian , Carmen Sylva 2444 , Santiago 7510156 , Chile
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27
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Kim DN, Thiel BC, Mrozowich T, Hennelly SP, Hofacker IL, Patel TR, Sanbonmatsu KY. Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution. Nat Commun 2020; 11:148. [PMID: 31919376 PMCID: PMC6952434 DOI: 10.1038/s41467-019-13942-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 12/09/2019] [Indexed: 02/08/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) constitute a significant fraction of the transcriptome, playing important roles in development and disease. However, our understanding of structure-function relationships for this emerging class of RNAs has been limited to secondary structures. Here, we report the 3-D atomistic structural study of epigenetic lncRNA, Braveheart (Bvht), and its complex with CNBP (Cellular Nucleic acid Binding Protein). Using small angle X-ray scattering (SAXS), we elucidate the ensemble of Bvht RNA conformations in solution, revealing that Bvht lncRNA has a well-defined, albeit flexible 3-D structure that is remodeled upon CNBP binding. Our study suggests that CNBP binding requires multiple domains of Bvht and the RHT/AGIL RNA motif. We show that RHT/AGIL, previously shown to interact with CNBP, contains a highly flexible loop surrounded by more ordered helices. As one of the largest RNA-only 3-D studies, the work lays the foundation for future structural studies of lncRNA-protein complexes.
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Affiliation(s)
- Doo Nam Kim
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Bernhard C Thiel
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Tyler Mrozowich
- Alberta RNA Research & Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Scott P Hennelly
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
- New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Vienna, Austria
- Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Trushar R Patel
- Alberta RNA Research & Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada.
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alberta, Canada.
| | - Karissa Y Sanbonmatsu
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
- New Mexico Consortium, Los Alamos, New Mexico, USA.
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mRNA structure determines modification by pseudouridine synthase 1. Nat Chem Biol 2019; 15:966-974. [PMID: 31477916 PMCID: PMC6764900 DOI: 10.1038/s41589-019-0353-z] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 07/22/2019] [Indexed: 01/28/2023]
Abstract
Pseudouridine (Ψ) is a post-transcriptional RNA modification that alters RNA-RNA and RNA-protein interactions that affect gene expression. mRNA pseudouridylation was recently discovered as a widespread and conserved phenomenon, but the mechanisms responsible for selective, regulated pseudouridylation of specific sequences within mRNAs were unknown. Here, we have revealed new mRNA targets for five pseudouridine synthases and probed the determinants of mRNA target recognition by the predominant mRNA pseudouridylating enzyme, Pus1, by developing high-throughput kinetic analysis of pseudouridylation in vitro. Combining computational prediction and rational mutational analysis revealed an RNA structural motif that is both necessary and sufficient for mRNA pseudouridylation. Applying this structural context information predicted hundreds of additional mRNA targets, that we showed were pseudouridylated in vivo. These results demonstrate a structure-dependent mode of mRNA target recognition by a conserved pseudouridine synthase and implicate modulation of RNA structure as the likely mechanism to regulate mRNA pseudouridylation.
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Xu X, Zhao C, Chen SJ. VfoldLA: A web server for loop assembly-based prediction of putative 3D RNA structures. J Struct Biol 2019; 207:235-240. [PMID: 31173857 PMCID: PMC6711797 DOI: 10.1016/j.jsb.2019.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 05/30/2019] [Accepted: 06/03/2019] [Indexed: 11/19/2022]
Abstract
RNA three-dimensional (3D) structures are critical for RNA cellular functions. However, structure prediction for large and complex RNAs remains a challenge, which hampers our understanding of RNA structure-function relationship. We here report a new web server, the VfoldLA server (http://rna.physics.missouri.edu/vfoldLA), for the prediction of RNA 3D structures from nucleotide sequences and base-pair information (2D structure). This server is based on the recently developed VfoldLA, a model that classifies the single-stranded loops (junctions) into four different types and according to the loop-helix connections, assembles RNA 3D structures from the loop/junction templates. The VfoldLA web server provides a user-friendly online interface for a fully automated prediction of putative 3D RNA structures using VfoldLA. With a single-RNA or RNA-RNA complex sequence and 2D structure as input, the server generates structure(s) with the JSmol visualization along with a downloadable PDB file. The output result may serve as useful scaffolds for future structure refinement studies.
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Affiliation(s)
- Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
| | - Chenhan Zhao
- Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
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30
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Wang J, Williams B, Chirasani VR, Krokhotin A, Das R, Dokholyan NV. Limits in accuracy and a strategy of RNA structure prediction using experimental information. Nucleic Acids Res 2019; 47:5563-5572. [PMID: 31106330 PMCID: PMC6582333 DOI: 10.1093/nar/gkz427] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/03/2019] [Accepted: 05/08/2019] [Indexed: 01/22/2023] Open
Abstract
RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.
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Affiliation(s)
- Jian Wang
- Department of Pharmacology, Penn State University College of Medicine, Hershey, PA 17033, USA
| | - Benfeard Williams
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Venkata R Chirasani
- Department of Pharmacology, Penn State University College of Medicine, Hershey, PA 17033, USA
| | - Andrey Krokhotin
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Rajeshree Das
- Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State University College of Medicine, Hershey, PA 17033, USA
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
- Department of Chemistry, Penn State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, Penn State University, University Park, PA 16802, USA
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Thiel BC, Beckmann IK, Kerpedjiev P, Hofacker IL. 3D based on 2D: Calculating helix angles and stacking patterns using forgi 2.0, an RNA Python library centered on secondary structure elements. F1000Res 2019; 8:ISCB Comm J-287. [PMID: 31069053 PMCID: PMC6480952 DOI: 10.12688/f1000research.18458.2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2019] [Indexed: 01/01/2023] Open
Abstract
We present forgi, a Python library to analyze the tertiary structure of RNA secondary structure elements. Our representation of an RNA molecule is centered on secondary structure elements (stems, bulges and loops). By fitting a cylinder to the helix axis, these elements are carried over into a coarse-grained 3D structure representation. Integration with Biopython allows for handling of all-atom 3D information. forgi can deal with a variety of file formats including dotbracket strings, PDB and MMCIF files. We can handle modified residues, missing residues, cofold and multifold structures as well as nucleotide numbers starting at arbitrary positions. We apply this library to the study of stacking helices in junctions and pseudoknots and investigate how far stacking helices in solved experimental structures can divert from coaxial geometries.
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Affiliation(s)
- Bernhard C. Thiel
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Irene K. Beckmann
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Peter Kerpedjiev
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Ivo L. Hofacker
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
- Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, 1090, Austria
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Thiel BC, Beckmann IK, Kerpedjiev P, Hofacker IL. 3D based on 2D: Calculating helix angles and stacking patterns using forgi 2.0, an RNA Python library centered on secondary structure elements. F1000Res 2019; 8:ISCB Comm J-287. [PMID: 31069053 PMCID: PMC6480952 DOI: 10.12688/f1000research.18458.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/06/2019] [Indexed: 10/12/2023] Open
Abstract
We present forgi, a Python library to analyze the tertiary structure of RNA secondary structure elements. Our representation of an RNA molecule is centered on secondary structure elements (stems, bulges and loops). By fitting a cylinder to the helix axis, these elements are carried over into a coarse-grained 3D structure representation. Integration with Biopython allows for handling of all-atom 3D information. forgi can deal with a variety of file formats including dotbracket strings, PDB and MMCIF files. We can handle modified residues, missing residues, cofold and multifold structures as well as nucleotide numbers starting at arbitrary positions. We apply this library to the study of stacking helices in junctions and pseudo knots and investigate how far stacking helices in solved experimental structures can divert from coaxial geometries.
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Affiliation(s)
- Bernhard C. Thiel
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Irene K. Beckmann
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Peter Kerpedjiev
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Ivo L. Hofacker
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, 1090, Austria
- Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, 1090, Austria
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Ponce-Salvatierra A, Astha, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM. Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 2019; 39:BSR20180430. [PMID: 30670629 PMCID: PMC6367127 DOI: 10.1042/bsr20180430] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 01/02/2023] Open
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
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Affiliation(s)
- Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Astha
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Katarzyna Merdas
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, Poznan PL-61-614, Poland
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Dans PD, Gallego D, Balaceanu A, Darré L, Gómez H, Orozco M. Modeling, Simulations, and Bioinformatics at the Service of RNA Structure. Chem 2019. [DOI: 10.1016/j.chempr.2018.09.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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35
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Sieradzan AK, Golon Ł, Liwo A. Prediction of DNA and RNA structure with the NARES-2P force field and conformational space annealing. Phys Chem Chem Phys 2018; 20:19656-19663. [PMID: 30014063 DOI: 10.1039/c8cp03018a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A physics-based method for the prediction of the structures of nucleic acids, which is based on the physics-based 2-bead NARES-2P model of polynucleotides and global-optimization Conformational Space Annealing (CSA) algorithm has been proposed. The target structure is sought as the global-energy-minimum structure, which ignores the entropy component of the free energy but spares expensive multicanonical simulations necessary to find the conformational ensemble with the lowest free energy. The CSA algorithm has been modified to optimize its performance when treating both single and multi-chain nucleic acids. It was shown that the method finds the native fold for simple RNA molecules and DNA duplexes and with limited distance restraints, which can easily be obtained from the secondary-structure-prediction servers, complex RNA folds can be treated with using moderate computer resources.
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Affiliation(s)
- Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, 80-308 Gdańsk, Poland.
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36
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Dominguez D, Freese P, Alexis MS, Su A, Hochman M, Palden T, Bazile C, Lambert NJ, Van Nostrand EL, Pratt GA, Yeo GW, Graveley BR, Burge CB. Sequence, Structure, and Context Preferences of Human RNA Binding Proteins. Mol Cell 2018; 70:854-867.e9. [PMID: 29883606 PMCID: PMC6062212 DOI: 10.1016/j.molcel.2018.05.001] [Citation(s) in RCA: 298] [Impact Index Per Article: 49.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 03/20/2018] [Accepted: 05/01/2018] [Indexed: 01/03/2023]
Abstract
RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity toward a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, however, we observed extensive preferences for contextual features distinct from short linear RNA motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. Our results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif.
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Affiliation(s)
| | - Peter Freese
- Program in Computational and Systems Biology, MIT, Cambridge, MA, USA
| | - Maria S Alexis
- Program in Computational and Systems Biology, MIT, Cambridge, MA, USA
| | - Amanda Su
- Department of Biology, MIT, Cambridge, MA, USA
| | | | | | | | | | - Eric L Van Nostrand
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California at San Diego, La Jolla, CA, USA
| | - Gabriel A Pratt
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California at San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Graduate Program, University of California at San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California at San Diego, La Jolla, CA, USA; Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Molecular Engineering Laboratory, A(∗)STAR, Singapore, Singapore
| | - Brenton R Graveley
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health, Farmington, CT, USA
| | - Christopher B Burge
- Department of Biology, MIT, Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA.
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Šponer J, Bussi G, Krepl M, Banáš P, Bottaro S, Cunha RA, Gil-Ley A, Pinamonti G, Poblete S, Jurečka P, Walter NG, Otyepka M. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. Chem Rev 2018; 118:4177-4338. [PMID: 29297679 PMCID: PMC5920944 DOI: 10.1021/acs.chemrev.7b00427] [Citation(s) in RCA: 336] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Indexed: 12/14/2022]
Abstract
With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.
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Affiliation(s)
- Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory, Department of Biology , University of Copenhagen , Copenhagen 2200 , Denmark
| | - Richard A Cunha
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Alejandro Gil-Ley
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Giovanni Pinamonti
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Simón Poblete
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Petr Jurečka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Nils G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
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Dotu I, Adamson SI, Coleman B, Fournier C, Ricart-Altimiras E, Eyras E, Chuang JH. SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data. PLoS Comput Biol 2018; 14:e1006078. [PMID: 29596423 PMCID: PMC5892938 DOI: 10.1371/journal.pcbi.1006078] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/10/2018] [Accepted: 03/05/2018] [Indexed: 12/02/2022] Open
Abstract
RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC. RNA-protein binding is critical to gene regulation, and aberrant RNA-protein interactions play a role in a wide variety of diseases. However, molecular understanding of these interactions remains limited because of the difficulty of ascertaining the motifs that bind each protein. To address this challenge, we have developed a novel algorithm, SARNAclust, to computationally identify combined structure/sequence motifs from immunoprecipitation data. SARNAclust can deconvolve multiple motifs simultaneously and determine the importance of specific features through a graph kernel and bulge graph formalism. We have verified SARNAclust to be effective on synthetic motif data and also tested it on ENCODE eCLIP datasets, identifying known motifs and novel predictions. We have experimentally validated SARNAclust for two proteins, SLBP and ILF3, using RNA Bind-n-Seq measurements. Applying SARNAclust to ENCODE data provides new evidence for previously unknown regulatory interactions, notably splicing co-regulation by ILF3 and the splicing factor hnRNPC.
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Affiliation(s)
- Ivan Dotu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)–Pompeu Fabra University (UPF), Barcelona, Spain
| | - Scott I. Adamson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- UCONN Health, Department of Genetics and Genome Sciences, Farmington, CT, United States of America
| | - Benjamin Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Cyril Fournier
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Emma Ricart-Altimiras
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)–Pompeu Fabra University (UPF), Barcelona, Spain
| | - Eduardo Eyras
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)–Pompeu Fabra University (UPF), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Jeffrey H. Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- UCONN Health, Department of Genetics and Genome Sciences, Farmington, CT, United States of America
- * E-mail:
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Sherman M, Contreras L. Computational approaches in design of nucleic acid-based therapeutics. Curr Opin Biotechnol 2018; 53:232-239. [PMID: 29562215 DOI: 10.1016/j.copbio.2017.12.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 11/29/2017] [Accepted: 12/01/2017] [Indexed: 12/17/2022]
Abstract
Recent advances in computational and experimental methods have led to novel avenues for therapeutic development. Utilization of nucleic acids as therapeutic agents and/or targets has been recently gaining attention due to their potential as high-affinity, selective molecular building blocks for various therapies. Notably, development of computational algorithms for predicting accessible RNA binding sites, identifying therapeutic target sequences, modeling delivery into tissues, and designing binding aptamers have enhanced therapeutic potential for this new drug category. Here, we review trends in drug development within the pharmaceutical industry and ways by which nucleic acid-based drugs have arisen as effective therapeutic candidates. In particular, we focus on computational and experimental approaches to nucleic acid-based drug design, commenting on challenges and outlooks for future applications.
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Affiliation(s)
- Mark Sherman
- Cell and Molecular Biology Graduate Program, University of Texas at Austin, 100 E. 24th Street, A6500, Austin, TX 78712, USA
| | - Lydia Contreras
- McKetta Department of Chemical Engineering, University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
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40
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Poblete S, Bottaro S, Bussi G. A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs. Nucleic Acids Res 2018; 46:1674-1683. [PMID: 29272539 PMCID: PMC5829650 DOI: 10.1093/nar/gkx1269] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 12/05/2017] [Accepted: 12/07/2017] [Indexed: 01/30/2023] Open
Abstract
We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.
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Affiliation(s)
- Simón Poblete
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
| | - Sandro Bottaro
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, 265, Via Bonomea I-34136 Trieste, Italy
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41
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Xu X, Chen SJ. Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates. J Phys Chem B 2018; 122:5327-5335. [PMID: 29258305 DOI: 10.1021/acs.jpcb.7b10102] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The current RNA structure prediction methods cannot keep up the pace of the rapidly increasing number of sequences and the emerging new functions of RNAs. Template-based RNA three-dimensional structure prediction methods are restricted by the limited number of known RNA structures, and traditional motif-based search for the templates does not always lead to successful results. Here we report a new template search and assembly algorithm, the hierarchical loop template-assembly method (VfoldLA). The method searches for templates for single strand loop/junctions instead of the whole motifs, which often renders no available templates, or short fragments (several nucleotides), which requires a long computational time to assemble and refine. The VfoldLA method has the advantage of accounting for local and nonlocal interloop interactions. Benchmark tests indicate that this new method can provide low-resolution predictions for RNA conformations at different levels of structural complexities. Furthermore, the VfoldLA-predicted conformations may also serve as reliable putative models for further structure prediction and refinements. VfoldLA is accessible at http://rna.physics.missouri.edu/vfoldLA .
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Affiliation(s)
- Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering , Jiangsu University of Technology , Changzhou , Jiangsu 213001 , China.,Department of Physics, Department of Biochemistry, and Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
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42
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Sloma MF, Mathews DH. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs. PLoS Comput Biol 2017; 13:e1005827. [PMID: 29107980 PMCID: PMC5690697 DOI: 10.1371/journal.pcbi.1005827] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 11/16/2017] [Accepted: 10/17/2017] [Indexed: 12/21/2022] Open
Abstract
Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.
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Affiliation(s)
- Michael F. Sloma
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, NY, United States of America
| | - David H. Mathews
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, NY, United States of America
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States of America
- * E-mail:
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43
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Wang J, Mao K, Zhao Y, Zeng C, Xiang J, Zhang Y, Xiao Y. Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis. Nucleic Acids Res 2017; 45:6299-6309. [PMID: 28482022 PMCID: PMC5499770 DOI: 10.1093/nar/gkx386] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 04/27/2017] [Indexed: 01/01/2023] Open
Abstract
Direct coupling analysis of nucleotide coevolution provides a novel approach to identify which nucleotides in an RNA molecule are likely in direct contact, and this information obtained from sequence only can be used to predict RNA 3D structures with much improved accuracy. Here we present an efficient method that incorporates this information into current RNA 3D structure prediction methods, specifically 3dRNA. Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as other existing prediction methods that used the direct coupling analysis. In particular our method demonstrates a significant improvement in predicting multi-branch junction conformations, a major bottleneck for RNA 3D structure prediction. We also show that our method can be used to optimize the predictions by other methods. These results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis offers an efficient way for accurate RNA tertiary structure predictions.
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Affiliation(s)
- Jian Wang
- Institute of Biophysics, School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Kangkun Mao
- Institute of Biophysics, School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC 20052, USA.,School of Life Sciences, Jianghan University, Wuhan 430056, China
| | - Jianjin Xiang
- Institute of Biophysics, School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Zhang
- Institute of Biophysics, School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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44
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Bayrak CS, Kim N, Schlick T. Using sequence signatures and kink-turn motifs in knowledge-based statistical potentials for RNA structure prediction. Nucleic Acids Res 2017; 45:5414-5422. [PMID: 28158755 PMCID: PMC5435971 DOI: 10.1093/nar/gkx045] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/22/2017] [Indexed: 12/15/2022] Open
Abstract
Kink turns are widely occurring motifs in RNA, located in internal loops and associated with many biological functions including translation, regulation and splicing. The associated sequence pattern, a 3-nt bulge and G-A, A-G base-pairs, generates an angle of ∼50° along the helical axis due to A-minor interactions. The conserved sequence and distinct secondary structures of kink-turns (k-turn) suggest computational folding rules to predict k-turn-like topologies from sequence. Here, we annotate observed k-turn motifs within a non-redundant RNA dataset based on sequence signatures and geometrical features, analyze bending and torsion angles, and determine distinct knowledge-based potentials with and without k-turn motifs. We apply these scoring potentials to our RAGTOP (RNA-As-Graph-Topologies) graph sampling protocol to construct and sample coarse-grained graph representations of RNAs from a given secondary structure. We present graph-sampling results for 35 RNAs, including 12 k-turn and 23 non k-turn internal loops, and compare the results to solved structures and to RAGTOP results without special k-turn potentials. Significant improvements are observed with the updated scoring potentials compared to the k-turn-free potentials. Because k-turns represent a classic example of sequence/structure motif, our study suggests that other such motifs with sequence signatures and unique geometrical features can similarly be utilized for RNA structure prediction and design.
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Affiliation(s)
- Cigdem Sevim Bayrak
- Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
| | - Namhee Kim
- Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
| | - Tamar Schlick
- Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
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45
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RNA structure prediction: from 2D to 3D. Emerg Top Life Sci 2017; 1:275-285. [DOI: 10.1042/etls20160027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2017] [Revised: 07/27/2017] [Accepted: 08/10/2017] [Indexed: 11/17/2022]
Abstract
We summarize different levels of RNA structure prediction, from classical 2D structure to extended secondary structure and motif-based research toward 3D structure prediction of RNA. We outline the importance of classical secondary structure during all those levels of structure prediction.
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46
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Vangaveti S, D’Esposito RJ, Lippens JL, Fabris D, Ranganathan SV. A coarse-grained model for assisting the investigation of structure and dynamics of large nucleic acids by ion mobility spectrometry-mass spectrometry. Phys Chem Chem Phys 2017; 19:14937-14946. [PMID: 28374022 PMCID: PMC6958515 DOI: 10.1039/c7cp00717e] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) is a rapidly emerging tool for the investigation of nucleic acid structure and dynamics. IMS-MS determinations can provide valuable information regarding alternative topologies, folding intermediates, and conformational heterogeneities, which are not readily accessible to other analytical techniques. The leading strategies for data interpretation rely on computational and experimental approaches to correctly assign experimental observations to putative structures. A very effective strategy involves the application of molecular dynamics (MD) simulations to predict the structure of the analyte molecule, calculate its collision cross section (CCS), and then compare this computational value with the corresponding experimental data. While this approach works well for small nucleic acid species, analyzing larger nucleic acids of biological interest is hampered by the computational cost associated with capturing their extensive structure and dynamics in all-atom detail. In this report, we describe the implementation of a coarse graining (CG) approach to reduce the cost of the computational methods employed in the data interpretation workflow. Our framework employs a five-bead model to accurately represent each nucleotide in the nucleic acid structure. The beads are appropriately parameterized to enable the direct calculation of CCS values from CG models, thus affording the ability to pursue the analysis of larger, highly dynamic constructs. The validity of this approach was successfully confirmed by the excellent correlation between the CCS values obtained in parallel by all-atom and CG workflows.
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Affiliation(s)
| | | | - J. L. Lippens
- Discovery Analytical Sciences, Amgen, Thousand Oaks, CA
| | - D. Fabris
- The RNA Institute, University at Albany, NY
- Department of Chemistry, University at Albany, NY
- Department of Biological Sciences, University at Albany, NY
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47
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Abstract
In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.
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Affiliation(s)
- Li-Zhen Sun
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Dong Zhang
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
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48
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Bell DR, Cheng SY, Salazar H, Ren P. Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations. Sci Rep 2017; 7:45812. [PMID: 28393861 PMCID: PMC5385882 DOI: 10.1038/srep45812] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 03/06/2017] [Indexed: 01/25/2023] Open
Abstract
We introduce a coarse-grained RNA model for molecular dynamics simulations, RACER (RnA CoarsE-gRained). RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 Å) and the sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2 = 0.93). Using RACER, we identified hydrogen-bonding (or base pairing), base stacking, and electrostatic interactions as essential driving forces for RNA folding. Also, we found that separating pairing vs. stacking interactions allowed RACER to distinguish folded vs. unfolded states. In RACER, base pairing and stacking interactions each provide an approximate stability of 3-4 kcal/mol for an A-form helix. RACER was developed based on PDB structural statistics and experimental thermodynamic data. In contrast with previous work, RACER implements a novel effective vdW potential energy function, which led us to re-parameterize hydrogen bond and electrostatic potential energy functions. Further, RACER is validated and optimized using a simulated annealing protocol to generate potential energy vs. RMSD landscapes. Finally, RACER is tested using extensive equilibrium pulling simulations (0.86 ms total) on eleven RNA sequences (hairpins and duplexes).
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Affiliation(s)
- David R. Bell
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Sara Y. Cheng
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, United States
| | - Heber Salazar
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
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49
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Taylor WR, Hamilton RS. Exploring RNA conformational space under sparse distance restraints. Sci Rep 2017; 7:44074. [PMID: 28281575 PMCID: PMC5345030 DOI: 10.1038/srep44074] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 02/01/2017] [Indexed: 11/21/2022] Open
Abstract
We show that the application of a small number of restraints predicted by coevolution analysis can provide a powerful restriction on the conformational freedom of an RNA molecule. The greatest degree of restriction occurs when a contact is predicted between the distal ends of a pair of adjacent stemloops but even with this location additional flexibilities in the molecule can mask the contribution. Multiple cross-links, especially those including a pseudoknot provided the strongest restraint on conformational freedom with the effect being most apparent in topologically simple folds and less so if the fold is more topologically entwined. Little was expected for large structures (over 300 bases) and although a few strong localised restrictions were observed, they contributed little to the restraint of the overall fold. Although contacts predicted using a correlated mutation analysis can provide some powerful restrictions on the conformational freedom of RNA molecules, they are too erratic in their occurrence and distribution to provide a general approach to the problem of RNA 3D structure prediction from sequence.
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Affiliation(s)
- William R. Taylor
- Computational Cell and Molecular Biology, Francis Crick Institute, London, NW1 1AT, UK
| | - Russell S. Hamilton
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3DY, UK
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50
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Coarse-grained modeling of RNA 3D structure. Methods 2016; 103:138-56. [PMID: 27125734 DOI: 10.1016/j.ymeth.2016.04.026] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 04/21/2016] [Accepted: 04/22/2016] [Indexed: 12/21/2022] Open
Abstract
Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.
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