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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; 52:e73. [PMID: 39021350 PMCID: PMC11381333 DOI: 10.1093/nar/gkae602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/05/2024] [Indexed: 07/20/2024] Open
Abstract
The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.
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Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, via Bonomea 265, Trieste 34136, Italy
| | - Simón Poblete
- Centro BASAL Ciencia & Vida, Avenida del Valle Norte 725, Santiago 8580702, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
- Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna 1090, Austria
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2
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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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3
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Schlick T, Portillo-Ledesma S. Biomolecular modeling thrives in the age of technology. NATURE COMPUTATIONAL SCIENCE 2021; 1:321-331. [PMID: 34423314 PMCID: PMC8378674 DOI: 10.1038/s43588-021-00060-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022]
Abstract
The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
- New York University–East China Normal University Center for Computational Chemistry at New York University Shanghai, Shanghai, China
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4
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Meng G, Tariq M, Jain S, Elmetwaly S, Schlick T. RAG-Web: RNA structure prediction/design using RNA-As-Graphs. Bioinformatics 2019; 36:647-648. [PMID: 31373604 PMCID: PMC7999136 DOI: 10.1093/bioinformatics/btz611] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/11/2019] [Accepted: 08/01/2019] [Indexed: 01/31/2023] Open
Abstract
SUMMARY We launch a webserver for RNA structure prediction and design corresponding to tools developed using our RNA-As-Graphs (RAG) approach. RAG uses coarse-grained tree graphs to represent RNA secondary structure, allowing the application of graph theory to analyze and advance RNA structure discovery. Our webserver consists of three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to predict corresponding tertiary topologies, (b) RAG Builder: builds three-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: designs sequences that fold onto novel RNA motifs (described by tree graph topologies). Results analyses are performed for further assessment/selection. The Results page provides links to download results and indicates possible errors encountered. RAG-Web offers a user-friendly interface to utilize our RAG software suite to predict and design RNA structures and sequences. AVAILABILITY AND IMPLEMENTATION The webserver is freely available online at: http://www.biomath.nyu.edu/ragtop/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Grace Meng
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Marva Tariq
- Department of Chemistry, Smith College, Northampton, MA 01063, USA
| | - Swati Jain
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Shereef Elmetwaly
- Department of Chemistry, New York University, New York, NY 10003, USA
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5
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Jain S, Saju S, Petingi L, Schlick T. An extended dual graph library and partitioning algorithm applicable to pseudoknotted RNA structures. Methods 2019; 162-163:74-84. [PMID: 30928508 DOI: 10.1016/j.ymeth.2019.03.022] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 02/28/2019] [Accepted: 03/22/2019] [Indexed: 12/18/2022] Open
Abstract
Exploring novel RNA topologies is imperative for understanding RNA structure and pursuing its design. Our RNA-As-Graphs (RAG) approach exploits graph theory tools and uses coarse-grained tree and dual graphs to represent RNA helices and loops by vertices and edges. Only dual graphs represent pseudoknotted RNAs fully. Here we develop a dual graph enumeration algorithm to generate an expanded library of dual graph topologies for 2-9 vertices, and extend our dual graph partitioning algorithm to identify all possible RNA subgraphs. Our enumeration algorithm connects smaller-vertex graphs, using all possible edge combinations, to build larger-vertex graphs and retain all non-isomorphic graph topologies, thereby more than doubling the size of our prior library to a total of 110,667 dual graph topologies. We apply our dual graph partitioning algorithm, which keeps pseudoknots and junctions intact, to all existing RNA structures to identify all possible substructures up to 9 vertices. In addition, our expanded dual graph library assigns graph topologies to all RNA graphs and subgraphs, rectifying prior inconsistencies. We update our RAG-3Dual database of RNA atomic fragments with all newly identified substructures and their graph IDs, increasing its size by more than 50 times. The enlarged dual graph library and RAG-3Dual database provide a comprehensive repertoire of graph topologies and atomic fragments to study yet undiscovered RNA molecules and design RNA sequences with novel topologies, including a variety of pseudoknotted RNAs.
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Affiliation(s)
- Swati Jain
- Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY 10003, USA
| | - Sera Saju
- Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY 10003, USA
| | - Louis Petingi
- Computer Science Department, College of Staten Island, City University of New York, Staten Island, New York, NY 10314, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 1021 Silver, 100 Washington Square East, New York, NY 10003, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA; NYU-East China Normal University Center for Computational Chemistry at New York University Shanghai, Room 340, Geography Building, North Zhongshan Road, 3663 Shanghai, China.
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6
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Zaharia A, Labedan B, Froidevaux C, Denise A. CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts. BMC Bioinformatics 2019; 20:19. [PMID: 30630411 PMCID: PMC6327494 DOI: 10.1186/s12859-018-2542-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/22/2018] [Indexed: 02/07/2023] Open
Abstract
Background In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life. Results We present CoMetGeNe (COnserved METabolic and GEnomic NEighborhoods), a novel method that identifies metabolic and genomic patterns consisting in maximal trails of reactions being catalyzed by products of neighboring genes. Patterns determined by CoMetGeNe in one species are subsequently employed in order to reflect their degree of conservation across multiple prokaryotic species. These interspecies comparisons help to improve genome annotation and can reveal putative alternative metabolic routes as well as unexpected gene ordering occurrences. Conclusions CoMetGeNe is an exploratory tool at both the genomic and the metabolic levels, leading to insights into the conservation of functionally related clusters of neighboring enzyme-coding genes. The open-source CoMetGeNe pipeline is freely available at https://cometgene.lri.fr. Electronic supplementary material The online version of this article (10.1186/s12859-018-2542-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandra Zaharia
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Bernard Labedan
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Christine Froidevaux
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Alain Denise
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France. .,Institut de Biologie Intégrative de la Cellule (I2BC), CEA, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France.
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7
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Abstract
The structure of RNA has been a natural subject for mathematical modeling, inviting many innovative computational frameworks. This single-stranded polynucleotide chain can fold upon itself in numerous ways to form hydrogen-bonded segments, imperfect with single-stranded loops. Illustrating these paired and non-paired interaction networks, known as RNA's secondary (2D) structure, using mathematical graph objects has been illuminating for RNA structure analysis. Building upon such seminal work from the 1970s and 1980s, graph models are now used to study not only RNA structure but also describe RNA's recurring modular units, sample the conformational space accessible to RNAs, predict RNA's three-dimensional folds, and apply the combined aspects to novel RNA design. In this article, we outline the development of the RNA-As-Graphs (or RAG) approach and highlight current applications to RNA structure prediction and design.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012, USA; New York University ECNU - Center for Computational Chemistry at NYU Shanghai, 3663 North Zhongshan Road, Shanghai, 200062, China.
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8
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Héliou A, Budday D, Fonseca R, van den Bedem H. Fast, clash-free RNA conformational morphing using molecular junctions. Bioinformatics 2018; 33:2114-2122. [PMID: 28334257 DOI: 10.1093/bioinformatics/btx127] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 03/11/2017] [Indexed: 12/20/2022] Open
Abstract
Motivation Non-coding ribonucleic acids (ncRNA) are functional RNA molecules that are not translated into protein. They are extremely dynamic, adopting diverse conformational substates, which enables them to modulate their interaction with a large number of other molecules. The flexibility of ncRNA provides a challenge for probing their complex 3D conformational landscape, both experimentally and computationally. Results Despite their conformational diversity, ncRNAs mostly preserve their secondary structure throughout the dynamic ensemble. Here we present a kinematics-based procedure to morph an RNA molecule between conformational substates, while avoiding inter-atomic clashes. We represent an RNA as a kinematic linkage, with fixed groups of atoms as rigid bodies and rotatable bonds as degrees of freedom. Our procedure maintains RNA secondary structure by treating hydrogen bonds between base pairs as constraints. The constraints define a lower-dimensional, secondary-structure constraint manifold in conformation space, where motions are largely governed by molecular junctions of unpaired nucleotides. On a large benchmark set, we show that our morphing procedure compares favorably to peer algorithms, and can approach goal conformations to within a low all-atom RMSD by directing fewer than 1% of its atoms. Our results suggest that molecular junctions can modulate 3D structural rearrangements, while secondary structure elements guide large parts of the molecule along the transition to the correct final conformation. Availability and Implementation The source code, binaries and data are available at https://simtk.org/home/kgs . Contact amelie.heliou@polytechnique.edu or vdbedem@stanford.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Amélie Héliou
- LIX, Ecole Polytechnique, CNRS, Inria, Université Paris-Saclay, Palaiseau, France
| | - Dominik Budday
- Chair of Applied Dynamics, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Rasmus Fonseca
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA.,Biosciences Division, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
| | - Henry van den Bedem
- Biosciences Division, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
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9
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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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10
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Jain S, Schlick T. F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly. J Mol Biol 2017; 429:3587-3605. [PMID: 28988954 PMCID: PMC5693719 DOI: 10.1016/j.jmb.2017.09.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/12/2017] [Accepted: 09/22/2017] [Indexed: 10/18/2022]
Abstract
Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
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Affiliation(s)
- Swati Jain
- Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA; New York University-East China Normal University Center for Computational Chemistry at New York University Shanghai, Room 340, Geography Building, North Zhongshan Road, 3663 Shanghai, China.
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11
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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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12
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Schlick T, Pyle AM. Opportunities and Challenges in RNA Structural Modeling and Design. Biophys J 2017; 113:225-234. [PMID: 28162235 PMCID: PMC5529161 DOI: 10.1016/j.bpj.2016.12.037] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 12/08/2016] [Accepted: 12/19/2016] [Indexed: 01/27/2023] Open
Abstract
We describe opportunities and challenges in RNA structural modeling and design, as recently discussed during the second Telluride Science Research Center workshop organized in June 2016. Topics include fundamental processes of RNA, such as structural assemblies (hierarchical folding, multiple conformational states and their clustering), RNA motifs, and chemical reactivity of RNA, as used for structural prediction and functional inference. We also highlight the software and database issues associated with RNA structures, such as the multiple approaches for motif annotation, the need for frequent database updating, and the importance of quality control of RNA structures. We discuss various modeling approaches for structure prediction, mechanistic analysis of RNA reactions, and RNA design, and the complementary roles that both atomistic and coarse-grained approaches play in such simulations. Collectively, as scientists from varied disciplines become familiar and drawn into these unique challenges, new approaches and collaborative efforts will undoubtedly be catalyzed.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York; Courant Institute of Mathematical Sciences, New York University, New York, New York.
| | - Anna Marie Pyle
- Department of Molecular and Cellular and Developmental Biology and Department of Chemistry, Yale University; Howard Hughes Medical Institute, New Haven, Connecticut.
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13
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Hoppe T, Petrone A. Integer sequence discovery from small graphs. DISCRETE APPLIED MATHEMATICS (AMSTERDAM, NETHERLANDS : 1988) 2016; 201:172-181. [PMID: 27034526 PMCID: PMC4809059 DOI: 10.1016/j.dam.2015.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We have exhaustively enumerated all simple, connected graphs of a finite order and have computed a selection of invariants over this set. Integer sequences were constructed from these invariants and checked against the Online Encyclopedia of Integer Sequences (OEIS). 141 new sequences were added and six sequences were extended. From the graph database, we were able to programmatically suggest relationships among the invariants. It will be shown that we can readily visualize any sequence of graphs with a given criteria. The code has been released as an open-source framework for further analysis and the database was constructed to be extensible to invariants not considered in this work.
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14
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Hua L, Song Y, Kim N, Laing C, Wang JTL, Schlick T. CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking. PLoS One 2016; 11:e0147097. [PMID: 26789998 PMCID: PMC4720362 DOI: 10.1371/journal.pone.0147097] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 12/29/2015] [Indexed: 01/01/2023] Open
Abstract
RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.
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Affiliation(s)
- Lei Hua
- Bioinformatics Laboratory, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Yang Song
- Bioinformatics Laboratory, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Namhee Kim
- Department of Chemistry, New York University, New York, New York, United States of America
| | - Christian Laing
- Bioinformatics Laboratory, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America
| | - Jason T. L. Wang
- Bioinformatics Laboratory, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, United States of America
- * E-mail: (JW); (TS)
| | - Tamar Schlick
- Department of Chemistry, New York University, New York, New York, United States of America
- Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
- * E-mail: (JW); (TS)
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15
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Baba N, Elmetwaly S, Kim N, Schlick T. Predicting Large RNA-Like Topologies by a Knowledge-Based Clustering Approach. J Mol Biol 2015; 428:811-821. [PMID: 26478223 DOI: 10.1016/j.jmb.2015.10.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 10/06/2015] [Indexed: 11/19/2022]
Abstract
An analysis and expansion of our resource for classifying, predicting, and designing RNA structures, RAG (RNA-As-Graphs), is presented, with the goal of understanding features of RNA-like and non-RNA-like motifs and exploiting this information for RNA design. RAG was first reported in 2004 for cataloging RNA secondary structure motifs using graph representations. In 2011, the RAG resource was updated with the increased availability of RNA structures and was improved by utilities for analyzing RNA structures, including substructuring and search tools. We also classified RNA structures as graphs up to 10 vertices (~200 nucleotides) into three classes: existing, RNA-like, and non-RNA-like using clustering approaches. Here, we focus on the tree graphs and evaluate the newly founded RNAs since 2011, which also support our refined predictions of RNA-like motifs. We expand the RAG resource for large tree graphs up to 13 vertices (~260 nucleotides), thereby cataloging more than 10 times as many secondary structures. We apply clustering algorithms based on features of RNA secondary structures translated from known tertiary structures to suggest which hypothetical large RNA motifs can be considered "RNA-like". The results by the PAM (Partitioning Around Medoids) approach, in particular, reveal good accuracy, with small error for the largest cases. The RAG update here up to 13 vertices offers a useful graph-based tool for exploring RNA motifs and suggesting large RNA motifs for design.
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Affiliation(s)
- Naoto Baba
- Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA; Department of Chemistry, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Shereef Elmetwaly
- 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; NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China.
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16
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Li H, Lee T, Dziubla T, Pi F, Guo S, Xu J, Li C, Haque F, Liang XJ, Guo P. RNA as a stable polymer to build controllable and defined nanostructures for material and biomedical applications. NANO TODAY 2015; 10:631-655. [PMID: 26770259 PMCID: PMC4707685 DOI: 10.1016/j.nantod.2015.09.003] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The value of polymers is manifested in their vital use as building blocks in material and life sciences. Ribonucleic acid (RNA) is a polynucleic acid, but its polymeric nature in materials and technological applications is often overlooked due to an impression that RNA is seemingly unstable. Recent findings that certain modifications can make RNA resistant to RNase degradation while retaining its authentic folding property and biological function, and the discovery of ultra-thermostable RNA motifs have adequately addressed the concerns of RNA unstability. RNA can serve as a unique polymeric material to build varieties of nanostructures including nanoparticles, polygons, arrays, bundles, membrane, and microsponges that have potential applications in biomedical and material sciences. Since 2005, more than a thousand publications on RNA nanostructures have been published in diverse fields, indicating a remarkable increase of interest in the emerging field of RNA nanotechnology. In this review, we aim to: delineate the physical and chemical properties of polymers that can be applied to RNA; introduce the unique properties of RNA as a polymer; review the current methods for the construction of RNA nanostructures; describe its applications in material, biomedical and computer sciences; and, discuss the challenges and future prospects in this field.
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Affiliation(s)
- Hui Li
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
| | - Taek Lee
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, Republic of Korea
| | - Thomas Dziubla
- Department of Chemical and Materials Engineering, University of Kentucky, Lexington, KY 40506, USA
| | - Fengmei Pi
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
| | - Sijin Guo
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
- Department of Chemistry, University of Kentucky, Lexington, KY 40506, USA
| | - Jing Xu
- Laboratory of Nanomedicine and Nanosafety, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Chan Li
- Laboratory of Nanomedicine and Nanosafety, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Farzin Haque
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
| | - Xing-Jie Liang
- Laboratory of Nanomedicine and Nanosafety, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Peixuan Guo
- Nanobiotechnology Center, Markey Cancer Center, and Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY 40536, USA
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17
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Boudard M, Bernauer J, Barth D, Cohen J, Denise A. GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies. PLoS One 2015; 10:e0136444. [PMID: 26313379 PMCID: PMC4551674 DOI: 10.1371/journal.pone.0136444] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 08/03/2015] [Indexed: 11/19/2022] Open
Abstract
Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.
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Affiliation(s)
- Mélanie Boudard
- PRiSM, CNRS UMR 8144, Université de Versailles-St-Quentin-en-Yvelines, 78000 Versailles, France
- LRI, CNRS UMR 8623, Université Paris-Sud, 91405 Orsay, France
- * E-mail: (MB); (JC)
| | - Julie Bernauer
- AMIB, Inria Saclay-Ile de France, 91120 Palaiseau, France
- LIX, CNRS UMR 7161, Ecole Polytechnique, 91120 Palaiseau, France
| | - Dominique Barth
- PRiSM, CNRS UMR 8144, Université de Versailles-St-Quentin-en-Yvelines, 78000 Versailles, France
| | - Johanne Cohen
- LRI, CNRS UMR 8623, Université Paris-Sud, 91405 Orsay, France
- * E-mail: (MB); (JC)
| | - Alain Denise
- LRI, CNRS UMR 8623, Université Paris-Sud, 91405 Orsay, France
- AMIB, Inria Saclay-Ile de France, 91120 Palaiseau, France
- I2BC, CNRS, Université Paris-Sud, 91405 Orsay, France
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18
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Zahran M, Sevim Bayrak C, Elmetwaly S, Schlick T. RAG-3D: a search tool for RNA 3D substructures. Nucleic Acids Res 2015; 43:9474-88. [PMID: 26304547 PMCID: PMC4627073 DOI: 10.1093/nar/gkv823] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 08/03/2015] [Indexed: 01/23/2023] Open
Abstract
To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.
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Affiliation(s)
- Mai Zahran
- Biological Sciences Department, New York City College of Technology, City University of New York, Brooklyn, NY 11201, USA
| | | | - Shereef Elmetwaly
- Department of Chemistry, New York University, New York, NY 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, New York, NY 10003, USA Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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19
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Cragnolini T, Laurin Y, Derreumaux P, Pasquali S. Coarse-Grained HiRE-RNA Model for ab Initio RNA Folding beyond Simple Molecules, Including Noncanonical and Multiple Base Pairings. J Chem Theory Comput 2015; 11:3510-22. [PMID: 26575783 DOI: 10.1021/acs.jctc.5b00200] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
HiRE-RNA is a coarse-grained model for RNA structure prediction and the dynamical study of RNA folding. Using a reduced set of particles and detailed interactions accounting for base-pairing and stacking, we show that noncanonical and multiple base interactions are necessary to capture the full physical behavior of complex RNAs. In this paper, we give a full account of the model and present results on the folding, stability, and free energy surfaces of 16 systems with 12 to 76 nucleotides of increasingly complex architectures, ranging from monomers to dimers, using a total of 850 μs of simulation time.
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Affiliation(s)
- Tristan Cragnolini
- Laboratoire de Biochimie Théorique UPR 9080 CNRS, Université Paris Diderot , Sorbonne, Paris Cité, IBPC 13 rue Pierre et Marie Curie, 75005 Paris, France
| | - Yoann Laurin
- Laboratoire de Biochimie Théorique UPR 9080 CNRS, Université Paris Diderot , Sorbonne, Paris Cité, IBPC 13 rue Pierre et Marie Curie, 75005 Paris, France
| | - Philippe Derreumaux
- Laboratoire de Biochimie Théorique UPR 9080 CNRS, Université Paris Diderot , Sorbonne, Paris Cité, IBPC 13 rue Pierre et Marie Curie, 75005 Paris, France.,Institut Universitaire de France , Boulevard Saint-Michel, 75005 Paris, France
| | - Samuela Pasquali
- Laboratoire de Biochimie Théorique UPR 9080 CNRS, Université Paris Diderot , Sorbonne, Paris Cité, IBPC 13 rue Pierre et Marie Curie, 75005 Paris, France
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20
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Ding L, Xue X, LaMarca S, Mohebbi M, Samad A, Malmberg RL, Cai L. Accurate prediction of RNA nucleotide interactions with backbone k-tree model. Bioinformatics 2015; 31:2660-7. [PMID: 25886978 DOI: 10.1093/bioinformatics/btv210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 04/12/2015] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. RESULTS This paper presents a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucleotide interactions that constitute the desired 3D structure. The research is based on a novel graph model, called a backbone k-tree, to tightly constrain the nucleotide interaction relationships considered for RNA 3D structures. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions (including the non-canonical interactions in all recently revealed families) from the query sequence along with known or predicted canonical basepairs. The preliminary results indicate that in most cases the new method can predict with a high accuracy the nucleotide interactions that constitute the 3D structure of the query sequence. It thus provides a useful tool for the accurate prediction of RNA 3D structure. AVAILABILITY AND IMPLEMENTATION The source package for BkTree is available at http://rna-informatics.uga.edu/index.php?f=software&p=BkTree. CONTACT lding@uga.edu or cai@cs.uga.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | | | - Abdul Samad
- Department of Computer Science, BUITEMS, Pakistan
| | - Russell L Malmberg
- Institute of Bioinformatics and Department of Plant Biology, University of Georgia, GA 30602, USA and
| | - Liming Cai
- Department of Computer Science, Institute of Bioinformatics and
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21
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Kim N, Zahran M, Schlick T. Computational Prediction of Riboswitch Tertiary Structures Including Pseudoknots by RAGTOP. Methods Enzymol 2015; 553:115-35. [DOI: 10.1016/bs.mie.2014.10.054] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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22
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Shi YZ, Wang FH, Wu YY, Tan ZJ. A coarse-grained model with implicit salt for RNAs: Predicting 3D structure, stability and salt effect. J Chem Phys 2014; 141:105102. [DOI: 10.1063/1.4894752] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- Ya-Zhou Shi
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Feng-Hua Wang
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Yuan-Yan Wu
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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23
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RNA graph partitioning for the discovery of RNA modularity: a novel application of graph partition algorithm to biology. PLoS One 2014; 9:e106074. [PMID: 25188578 PMCID: PMC4154854 DOI: 10.1371/journal.pone.0106074] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2014] [Accepted: 07/31/2014] [Indexed: 11/19/2022] Open
Abstract
Graph representations have been widely used to analyze and design various economic, social, military, political, and biological networks. In systems biology, networks of cells and organs are useful for understanding disease and medical treatments and, in structural biology, structures of molecules can be described, including RNA structures. In our RNA-As-Graphs (RAG) framework, we represent RNA structures as tree graphs by translating unpaired regions into vertices and helices into edges. Here we explore the modularity of RNA structures by applying graph partitioning known in graph theory to divide an RNA graph into subgraphs. To our knowledge, this is the first application of graph partitioning to biology, and the results suggest a systematic approach for modular design in general. The graph partitioning algorithms utilize mathematical properties of the Laplacian eigenvector (µ2) corresponding to the second eigenvalues (λ2) associated with the topology matrix defining the graph: λ2 describes the overall topology, and the sum of µ2's components is zero. The three types of algorithms, termed median, sign, and gap cuts, divide a graph by determining nodes of cut by median, zero, and largest gap of µ2's components, respectively. We apply these algorithms to 45 graphs corresponding to all solved RNA structures up through 11 vertices (∼ 220 nucleotides). While we observe that the median cut divides a graph into two similar-sized subgraphs, the sign and gap cuts partition a graph into two topologically-distinct subgraphs. We find that the gap cut produces the best biologically-relevant partitioning for RNA because it divides RNAs at less stable connections while maintaining junctions intact. The iterative gap cuts suggest basic modules and assembly protocols to design large RNA structures. Our graph substructuring thus suggests a systematic approach to explore the modularity of biological networks. In our applications to RNA structures, subgraphs also suggest design strategies for novel RNA motifs.
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24
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Sterpone F, Melchionna S, Tuffery P, Pasquali S, Mousseau N, Cragnolini T, Chebaro Y, St-Pierre JF, Kalimeri M, Barducci A, Laurin Y, Tek A, Baaden M, Nguyen PH, Derreumaux P. The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems. Chem Soc Rev 2014; 43:4871-93. [PMID: 24759934 PMCID: PMC4426487 DOI: 10.1039/c4cs00048j] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.
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Affiliation(s)
- Fabio Sterpone
- Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 rue Pierre et Marie Curie, 75005, Paris, France.
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25
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Hyun S, Han A, Jo MH, Hohng S, Yu J. Dicer Nuclease-Promoted Production of Let7a-1 MicroRNA Is Enhanced in the Presence of Tryptophan-Containing Amphiphilic Peptides. Chembiochem 2014; 15:1651-9. [DOI: 10.1002/cbic.201402126] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Indexed: 11/07/2022]
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26
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Graph-based sampling for approximating global helical topologies of RNA. Proc Natl Acad Sci U S A 2014; 111:4079-84. [PMID: 24591615 DOI: 10.1073/pnas.1318893111] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A current challenge in RNA structure prediction is the description of global helical arrangements compatible with a given secondary structure. Here we address this problem by developing a hierarchical graph sampling/data mining approach to reduce conformational space and accelerate global sampling of candidate topologies. Starting from a 2D structure, we construct an initial graph from size measures deduced from solved RNAs and junction topologies predicted by our data-mining algorithm RNAJAG trained on known RNAs. We sample these graphs in 3D space guided by knowledge-based statistical potentials derived from bending and torsion measures of internal loops as well as radii of gyration for known RNAs. Graph sampling results for 30 representative RNAs are analyzed and compared with reference graphs from both solved structures and predicted structures by available programs. This comparison indicates promise for our graph-based sampling approach for characterizing global helical arrangements in large RNAs: graph rmsds range from 2.52 to 28.24 Å for RNAs of size 25-158 nucleotides, and more than half of our graph predictions improve upon other programs. The efficiency in graph sampling, however, implies an additional step of translating candidate graphs into atomic models. Such models can be built with the same idea of graph partitioning and build-up procedures we used for RNA design.
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27
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Kim N, Petingi L, Schlick T. Network Theory Tools for RNA Modeling. WSEAS TRANSACTIONS ON MATHEMATICS 2013; 9:941-955. [PMID: 25414570 PMCID: PMC4235620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology.
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Affiliation(s)
- Namhee Kim
- New York University Department of Chemistry Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY 10012, USA
| | - Louis Petingi
- College of Staten Island City University of New York Department of Computer Science 2800 Victory Boulevard Staten Island, NY 10314, USA
| | - Tamar Schlick
- New York University Department of Chemistry Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY 10012, USA
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