1
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Jiang H, Xu Y, Tong Y, Zhang D, Zhou R. IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation. PLoS Comput Biol 2024; 20:e1012293. [PMID: 39466881 PMCID: PMC11542809 DOI: 10.1371/journal.pcbi.1012293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 11/07/2024] [Accepted: 10/12/2024] [Indexed: 10/30/2024] Open
Abstract
As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs.
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Affiliation(s)
- Haolin Jiang
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yulian Xu
- College of Life Sciences, China Jiliang University, Hangzhou, China
- China Jiliang University—Aoming (Hangzhou) Biomedical Co., Ltd. Joint Laboratory, Hangzhou, China
| | - Yunguang Tong
- College of Life Sciences, China Jiliang University, Hangzhou, China
- Aoming (Hangzhou) Biomedical Co., Ltd., Hangzhou, China
| | - Dong Zhang
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruhong Zhou
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou, Zhejiang, China
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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2
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Sha CM, Wang J, Dokholyan NV. Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks. Biophys J 2024; 123:2671-2681. [PMID: 37838833 PMCID: PMC11393712 DOI: 10.1016/j.bpj.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/19/2023] [Accepted: 10/12/2023] [Indexed: 10/16/2023] Open
Abstract
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.
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Affiliation(s)
- Congzhou M Sha
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Nikolay V Dokholyan
- Department of Engineering Science and Mechanics, Penn State University, State College, Pennsylvania; Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania; Department of Chemistry, Penn State University, State College, Pennsylvania; Department of Biomedical Engineering, Penn State University, State College, Pennsylvania.
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3
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Zhang S, Li J, Chen SJ. Machine learning in RNA structure prediction: Advances and challenges. Biophys J 2024; 123:2647-2657. [PMID: 38297836 PMCID: PMC11393687 DOI: 10.1016/j.bpj.2024.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.
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Affiliation(s)
- Sicheng Zhang
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Jun Li
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Shi-Jie Chen
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri; Department of Biochemistry, University of Missouri, Columbia, Missouri.
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4
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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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5
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Wu J, Zhang Y, Nie Y, Yan F, Zirbel CL, Bisaro DM. RNA three-dimensional structure drives the sequence organization of potato spindle tuber viroid quasispecies. PLoS Pathog 2024; 20:e1012142. [PMID: 38574111 PMCID: PMC11020406 DOI: 10.1371/journal.ppat.1012142] [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: 10/24/2023] [Revised: 04/16/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024] Open
Abstract
RNA viruses and viroids exist and evolve as quasispecies due to error-prone replication. Quasispecies consist of a few dominant master sequences alongside numerous variants that contribute to genetic diversity. Upon environmental changes, certain variants within quasispecies have the potential to become the dominant sequences, leading to the emergence of novel infectious strains. However, the emergence of new infectious variants remains unpredictable. Using mutant pools prepared by saturation mutagenesis of selected stem and loop regions, our study of potato spindle tuber viroid (PSTVd) demonstrates that mutants forming local three-dimensional (3D) structures similar to the wild type (WT) are more likely to accumulate in PSTVd quasispecies. The selection mechanisms underlying this biased accumulation are likely associated with cell-to-cell movement and long-distance trafficking. Moreover, certain trafficking-defective PSTVd mutants can be spread by functional sister genomes in the quasispecies. Our study reveals that the RNA 3D structure of stems and loops constrains the evolution of viroid quasispecies. Mutants with a structure similar to WT have a higher likelihood of being maintained within the quasispecies and can potentially give rise to novel infectious variants. These findings emphasize the potential of targeting RNA 3D structure as a more robust approach to defend against viroid infections.
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Affiliation(s)
- Jian Wu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agroproducts, Institute of Plant Virology, Ningbo University, Ningbo, China
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, China
- Department of Molecular Genetics, Center for Applied Plant Sciences, Center for RNA Biology, and Infectious Diseases Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Yuhong Zhang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agroproducts, Institute of Plant Virology, Ningbo University, Ningbo, China
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, China
| | - Yuxin Nie
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agroproducts, Institute of Plant Virology, Ningbo University, Ningbo, China
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, China
| | - Fei Yan
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agroproducts, Institute of Plant Virology, Ningbo University, Ningbo, China
- Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, China
| | - Craig L. Zirbel
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio, United States of America
| | - David M. Bisaro
- Department of Molecular Genetics, Center for Applied Plant Sciences, Center for RNA Biology, and Infectious Diseases Institute, The Ohio State University, Columbus, Ohio, United States of America
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6
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Shi YZ, Wu H, Li SS, Li HZ, Zhang BG, Tan YL. ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures. Molecules 2024; 29:1244. [PMID: 38542881 PMCID: PMC10974898 DOI: 10.3390/molecules29061244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 11/12/2024] Open
Abstract
RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable. In this study, we introduced a straightforward method called ABC2A for reconstructing all-atom structures from RNA CG models. ABC2A utilizes diverse nucleotide fragments from known structures to assemble full atomic structures based on the CG atoms. The diversification of assembly fragments beyond standard A-form ones, commonly used in other programs, combined with a highly simplified structure refinement process, ensures that ABC2A achieves both high accuracy and rapid speed. Tests on a recent large dataset of 361 RNA experimental structures (30-692 nt) indicate that ABC2A can reconstruct full atomic structures from three-bead CG models with a mean RMSD of ~0.34 Å from experimental structures and an average runtime of ~0.5 s (maximum runtime < 2.5 s). Compared to the state-of-the-art Arena, ABC2A achieves a ~25% improvement in accuracy and is five times faster in speed.
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Affiliation(s)
- Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
| | - Hao Wu
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
| | - Sha-Sha Li
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
| | - Hui-Zhen Li
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
| | - Ben-Gong Zhang
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430200, China; (Y.-Z.S.); (H.W.); (S.-S.L.); (H.-Z.L.)
- School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China
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7
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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8
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Bernard C, Postic G, Ghannay S, Tahi F. RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality. Brief Bioinform 2024; 25:bbae064. [PMID: 38436560 PMCID: PMC10939302 DOI: 10.1093/bib/bbae064] [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: 11/29/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.
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Affiliation(s)
- Clement Bernard
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Guillaume Postic
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Sahar Ghannay
- LISN - CNRS/Université Paris-Saclay, France, 91400 Orsay, France
| | - Fariza Tahi
- Université Paris Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
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9
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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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10
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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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11
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Chen K, Zhou Y, Wang S, Xiong P. RNA tertiary structure modeling with BRiQ potential in CASP15. Proteins 2023; 91:1771-1778. [PMID: 37638558 DOI: 10.1002/prot.26574] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 06/22/2023] [Accepted: 08/08/2023] [Indexed: 08/29/2023]
Abstract
We describe the modeling method for RNA tertiary structures employed by team AIchemy_RNA2 in the 15th Critical Assessment of Structure Prediction (CASP15). The method consists of the following steps. Firstly, secondary structure information was derived from various manually-verified sources. With this information, the full length RNA was fragmented into structural modules. The structures of each module were predicted and then assembled into the full structure. To reduce the searching conformational space, an RNA structure was organized into an optimal base folding tree. And to further improve the sampling efficiency, the energy surface was smoothed at high temperatures during the Monte Carlo sampling to make it easier to move across the energy barrier. The statistical potential energy function BRiQ was employed during Monte Carlo energy optimization.
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Affiliation(s)
- Ke Chen
- University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
| | - Yaoqi Zhou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Sheng Wang
- Shanghai Zelixir Biotech Co. Ltd, Shanghai, China
| | - Peng Xiong
- University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
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12
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Schneider B, Sweeney BA, Bateman A, Cerny J, Zok T, Szachniuk M. When will RNA get its AlphaFold moment? Nucleic Acids Res 2023; 51:9522-9532. [PMID: 37702120 PMCID: PMC10570031 DOI: 10.1093/nar/gkad726] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023] Open
Abstract
The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.
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Affiliation(s)
- Bohdan Schneider
- Institute of Biotechnology of the Czech Academy of Sciences, Prumyslova 595, CZ-252 50 Vestec, Czech Republic
| | - Blake Alexander Sweeney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Jiri Cerny
- Institute of Biotechnology of the Czech Academy of Sciences, Prumyslova 595, CZ-252 50 Vestec, Czech Republic
| | - Tomasz Zok
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
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13
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Perry ZR, Pyle AM, Zhang C. Arena: Rapid and Accurate Reconstruction of Full Atomic RNA Structures From Coarse-grained Models. J Mol Biol 2023; 435:168210. [PMID: 37479079 DOI: 10.1016/j.jmb.2023.168210] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
RNA tertiary structures from experiments or computational predictions often contain missing atoms, which prevent analyses requiring full atomic structures. Current programs for RNA reconstruction can be slow, inaccurate, and/or require specific atoms to be present in the input. We present Arena (Atomic Reconstruction of RNA), which reconstructs a full atomic RNA structure from residues that can have as few as one atom. Arena first fills in missing atoms and then iteratively refines their placement to reduce nonideal geometries. We benchmarked Arena on a dataset of 361 RNA structures, where Arena achieves high accuracy and speed compared to other structure reconstruction programs. For example, Arena was used to reconstruct full atomic structures from a single phosphorus atom per nucleotide to, on average, within 3.63 Å RMSD of the experimental structure, while virtually removing all clashes and running in <3 s, which is 353× and 46× faster than state-of-the-art programs PDBFixer and C2A, respectively. The Arena source code is available at https://github.com/pylelab/Arena and the webserver at https://zhanggroup.org/Arena/.
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Affiliation(s)
- Zion R Perry
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA. https://twitter.com/@zionrperry
| | - Anna Marie Pyle
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Department of Chemistry, Yale University, New Haven, CT 06511, USA.
| | - Chengxin Zhang
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
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14
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Tang M, Hwang K, Kang SH. StemP: A Fast and Deterministic Stem-Graph Approach for RNA Secondary Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3278-3291. [PMID: 37028040 DOI: 10.1109/tcbb.2023.3253049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We propose a new deterministic methodology to predict the secondary structure of RNA sequences. What information of stem is important for structure prediction, and is it enough ? The proposed simple deterministic algorithm uses minimum stem length, Stem-Loop score, and co-existence of stems, to give good structure predictions for short RNA and tRNA sequences. The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for structure prediction. Stem-Loop score adds structure information and speeds up the computation. The proposed method can predict secondary structure even with pseudo knots. One of the strengths of this approach is the simplicity and flexibility of the algorithm, and it gives a deterministic answer. Numerical experiments are done on various sequences from Protein Data Bank and the Gutell Lab using a laptop and results take only a few seconds.
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15
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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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16
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Vallat B, Tauriello G, Bienert S, Haas J, Webb BM, Žídek A, Zheng W, Peisach E, Piehl DW, Anischanka I, Sillitoe I, Tolchard J, Varadi M, Baker D, Orengo C, Zhang Y, Hoch JC, Kurisu G, Patwardhan A, Velankar S, Burley SK, Sali A, Schwede T, Berman HM, Westbrook JD. ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models. J Mol Biol 2023; 435:168021. [PMID: 36828268 PMCID: PMC10293049 DOI: 10.1016/j.jmb.2023.168021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023]
Abstract
ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.
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Affiliation(s)
- Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Juergen Haas
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Benjamin M Webb
- Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94157, USA
| | | | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ivan Anischanka
- Department of Biochemistry, and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Ian Sillitoe
- Department of Structural and Molecular Biology, UCL, London, UK
| | - James Tolchard
- AlphaFold Protein Structure Database, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK; Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Mihaly Varadi
- AlphaFold Protein Structure Database, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK; Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - David Baker
- Department of Biochemistry, and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | | | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, University of Connecticut, Farmington, CT 06030, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Ardan Patwardhan
- Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sameer Velankar
- AlphaFold Protein Structure Database, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK; Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94157, USA. https://twitter.com/salilab_ucsf
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
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17
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Henderson AN, McDonnell RT, Elcock AH. Modeling the 3D structure and conformational dynamics of very large RNAs using coarse-grained molecular simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543892. [PMID: 37333149 PMCID: PMC10274748 DOI: 10.1101/2023.06.06.543892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
We describe a computational approach to building and simulating realistic 3D models of very large RNA molecules (>1000 nucleotides) at a resolution of one "bead" per nucleotide. The method starts with a predicted secondary structure and uses several stages of energy minimization and Brownian dynamics (BD) simulation to build 3D models. A key step in the protocol is the temporary addition of a 4 th spatial dimension that allows all predicted helical elements to become disentangled from each other in an effectively automated way. We then use the resulting 3D models as input to Brownian dynamics simulations that include hydrodynamic interactions (HIs) that allow the diffusive properties of the RNA to be modelled as well as enabling its conformational dynamics to be simulated. To validate the dynamics part of the method, we first show that when applied to small RNAs with known 3D structures the BD-HI simulation models accurately reproduce their experimental hydrodynamic radii (Rh). We then apply the modelling and simulation protocol to a variety of RNAs for which experimental Rh values have been reported ranging in size from 85 to 3569 nucleotides. We show that the 3D models, when used in BD-HI simulations, produce hydrodynamic radii that are usually in good agreement with experimental estimates for RNAs that do not contain tertiary contacts that persist even under very low salt conditions. Finally, we show that sampling of the conformational dynamics of large RNAs on timescales of 100 µs is computationally feasible with BD-HI simulations.
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18
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Kamga Youmbi FI, Kengne Tchendji V, Tayou Djamegni C. P-FARFAR2: A multithreaded greedy approach to sampling low-energy RNA structures in Rosetta FARFAR2. Comput Biol Chem 2023; 104:107878. [PMID: 37167861 DOI: 10.1016/j.compbiolchem.2023.107878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 04/23/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
RNA (ribonucleic acid) structure prediction finds many applications in health science and drug discovery due to its importance in several life regulatory processes. But despite significant advances in the close field of protein prediction, RNA 3D structure still poses a tremendous challenge to predict, especially for large sequences. In this regard, the approach unfolded by Rosetta FARFAR2 (Fragment Assembly of RNA with Full-Atom Refinement, version 2) has shown promising results, but the algorithm is non-deterministic by nature. In this paper, we develop P-FARFAR2: a parallel enhancement of FARFAR2 that increases its ability to assemble low-energy structures via multithreaded exploration of random configurations in a greedy manner. This strategy, appearing in the literature under the term "parallel mechanism", is made viable through two measures: first, the synchronization window is coarsened to several Monte Carlo cycles; second, all but one of the threads are differentiated as auxiliary and set to perform a weakened version of the problem. Following empirical analysis on a diverse range of RNA structures, we report achieving statistical significance in lowering the energy levels of ensuing samples. And consequently, despite the moderate-to-weak correlation between energy levels and prediction accuracy, this achievement happens to propagate to accuracy measurements.
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Affiliation(s)
| | - Vianney Kengne Tchendji
- Department of Mathematics and Computer Science, University of Dschang, PO Box 67, Dschang, Cameroon.
| | - Clémentin Tayou Djamegni
- Department of Mathematics and Computer Science, University of Dschang, PO Box 67, Dschang, Cameroon; Department of Computer Engineering, University of Dschang, PO Box 134, Bandjoun, Cameroon.
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19
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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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20
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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: 10] [Impact Index Per Article: 10.0] [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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21
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Tan YL, Wang X, Yu S, Zhang B, Tan ZJ. cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation. NAR Genom Bioinform 2023; 5:lqad016. [PMID: 36879898 PMCID: PMC9985339 DOI: 10.1093/nargab/lqad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/21/2023] [Accepted: 02/03/2023] [Indexed: 03/07/2023] Open
Abstract
Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.
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Affiliation(s)
- Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China.,Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xunxun Wang
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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22
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Magnus M, Miao Z. RNA 3D Structure Comparison Using RNA-Puzzles Toolkit. Methods Mol Biol 2023; 2586:263-285. [PMID: 36705910 DOI: 10.1007/978-1-0716-2768-6_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Computational modeling of RNA three-dimensional (3D) structure may help in unrevealing the molecular mechanisms of RNA molecules and in designing molecules with novel functions. An unbiased blind assessment to benchmark the computational modeling is required to understand the achievements and bottlenecks of the prediction, while a standard structure comparison protocol is necessary. RNA-Puzzles is a community-wide effort on the assessment of blind prediction of RNA tertiary structures. And RNA-Puzzles toolkit is a computational resource derived from RNA-Puzzles, which includes (i) decoy sets generated by different RNA 3D structure prediction methods; (ii) 3D structure normalization, analysis, manipulation, and visualization tools; and (iii) 3D structure comparison metric tools. In this chapter, we illustrate a standard RNA 3D structure prediction assessment protocol using the selected tools from RNA-Puzzles toolkit: rna-tools and RNA_assessment.
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Affiliation(s)
- Marcin Magnus
- ReMedy-International Research Agenda Unit, Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. .,Department of Anesthesiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China.
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23
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Ma H, Pham P, Luo B, Rangan R, Kappel K, Su Z, Das R. Auto-DRRAFTER: Automated RNA Modeling Based on Cryo-EM Density. Methods Mol Biol 2023; 2568:193-211. [PMID: 36227570 DOI: 10.1007/978-1-0716-2687-0_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .
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Affiliation(s)
- Haiyun Ma
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Phillip Pham
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA, USA.
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24
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Wang J, Sha CM, Dokholyan NV. Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology. Methods Mol Biol 2023; 2709:51-64. [PMID: 37572272 PMCID: PMC10680996 DOI: 10.1007/978-1-0716-3417-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/14/2023]
Abstract
Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.
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Affiliation(s)
- Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Congzhou M Sha
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA.
- Department of Engineering Science and Mechanics, Penn State University, State College, PA, USA.
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA.
- Department of Chemistry, Penn State University, State College, PA, USA.
- Department of Biomedical Engineering, Penn State University, State College, PA, USA.
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25
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Abstract
RNA molecules carry out various cellular functions, and understanding the mechanisms behind their functions requires the knowledge of their 3D structures. Different types of computational methods have been developed to model RNA 3D structures over the past decade. These methods were widely used by researchers although their performance needs to be further improved. Recently, along with these traditional methods, machine-learning techniques have been increasingly applied to RNA 3D structure prediction and show significant improvement in performance. Here we shall give a brief review of the traditional methods and recent related advances in machine-learning approaches for RNA 3D structure prediction.
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Affiliation(s)
- Xiujuan Ou
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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26
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Paloncýová M, Pykal M, Kührová P, Banáš P, Šponer J, Otyepka M. Computer Aided Development of Nucleic Acid Applications in Nanotechnologies. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204408. [PMID: 36216589 DOI: 10.1002/smll.202204408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.
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Affiliation(s)
- Markéta Paloncýová
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Martin Pykal
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Petra Kührová
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Pavel Banáš
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
| | - Jiří Šponer
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
- Institute of Biophysics of the Czech Academy of Sciences, v. v. i., Královopolská 135, Brno, 612 65, Czech Republic
| | - Michal Otyepka
- Regional Center of Advanced Technologies and Materials, The Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 779 00, Czech Republic
- IT4Innovations, VŠB - Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba, 708 00, Czech Republic
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27
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Zhang D, Li Y, Zhong Q, Wang A, Weng J, Gong L, Li G. Ribonucleic Acid Folding Prediction Based on Iterative Multiscale Simulation. J Phys Chem Lett 2022; 13:9957-9966. [PMID: 36260782 DOI: 10.1021/acs.jpclett.2c01342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
RNA folding prediction is a challenge. Currently, many RNA folding models are coarse-grained (CG) with the potential derived from the known RNA structures. However, this potential is not suitable for modified and entirely new RNA. It is also not suitable for the folding simulation of RNA in the real cellular environment, including many kinds of molecular interactions. In contrast, our proposed model has the potential to address these issues, which is a multiscale simulation scheme based on all-atom (AA) force fields. We fit the CG force field using the trajectories generated by the AA force field and then iteratively perform molecular dynamics (MD) simulations of the two scales. The all-atom molecular dynamics (AAMD) simulation is mainly responsible for the correction of RNA structure, and the CGMD simulation is mainly responsible for efficient conformational sampling. On the basis of this scheme, we can successfully fold three RNAs belonging to a hairpin, a pseudoknot, and a four-way junction.
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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, Dalian116023, P. R. China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing100049, P. R. 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, Dalian116023, P. R. China
| | - Qinglu Zhong
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. 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, Dalian116023, P. R. 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, Dalian116023, P. R. China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing100049, P. R. China
| | - Lidong Gong
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian116029, 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, Dalian116023, P. R. China
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28
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Bheemireddy S, Sandhya S, Srinivasan N, Sowdhamini R. Computational tools to study RNA-protein complexes. Front Mol Biosci 2022; 9:954926. [PMID: 36275618 PMCID: PMC9585174 DOI: 10.3389/fmolb.2022.954926] [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: 05/27/2022] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.
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Affiliation(s)
- Sneha Bheemireddy
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sankaran Sandhya
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
| | | | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
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29
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Zhou L, Wang X, Yu S, Tan YL, Tan ZJ. FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures. Biophys J 2022; 121:3381-3392. [PMID: 35978551 PMCID: PMC9515226 DOI: 10.1016/j.bpj.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 11/23/2022] Open
Abstract
Knowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.
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Affiliation(s)
- Li Zhou
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xunxun Wang
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China.
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China.
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30
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Sinha S, Tam B, Wang SM. Applications of Molecular Dynamics Simulation in Protein Study. MEMBRANES 2022; 12:844. [PMID: 36135863 PMCID: PMC9505860 DOI: 10.3390/membranes12090844] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 05/29/2023]
Abstract
Molecular Dynamics (MD) Simulations is increasingly used as a powerful tool to study protein structure-related questions. Starting from the early simulation study on the photoisomerization in rhodopsin in 1976, MD Simulations has been used to study protein function, protein stability, protein-protein interaction, enzymatic reactions and drug-protein interactions, and membrane proteins. In this review, we provide a brief review for the history of MD Simulations application and the current status of MD Simulations applications in protein studies.
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Affiliation(s)
| | | | - San Ming Wang
- MoE Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, China
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31
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Xu B, Zhu Y, Cao C, Chen H, Jin Q, Li G, Ma J, Yang SL, Zhao J, Zhu J, Ding Y, Fang X, Jin Y, Kwok CK, Ren A, Wan Y, Wang Z, Xue Y, Zhang H, Zhang QC, Zhou Y. Recent advances in RNA structurome. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1285-1324. [PMID: 35717434 PMCID: PMC9206424 DOI: 10.1007/s11427-021-2116-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/01/2022] [Indexed: 12/27/2022]
Abstract
RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.
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Affiliation(s)
- Bingbing Xu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yanda Zhu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Changchang Cao
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Hao Chen
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China
| | - Qiongli Jin
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Guangnan Li
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Junfeng Ma
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Siwy Ling Yang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Jieyu Zhao
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jianghui Zhu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China
| | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, United Kingdom.
| | - Xianyang Fang
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Yongfeng Jin
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Chun Kit Kwok
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, China.
| | - Aiming Ren
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China.
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
| | - Zhiye Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Yuanchao Xue
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100101, China.
| | - Huakun Zhang
- Key Laboratory of Molecular Epigenetics of the Ministry of Education, Northeast Normal University, Changchun, 130024, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China.
| | - Yu Zhou
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
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32
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Li J, Zhang S, Zhang D, Chen SJ. Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences. Bioinformatics 2022; 38:4042-4043. [PMID: 35758624 PMCID: PMC9364377 DOI: 10.1093/bioinformatics/btac426] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/05/2022] [Accepted: 06/24/2022] [Indexed: 01/19/2023] Open
Abstract
SUMMARY RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. AVAILABILITY AND IMPLEMENTATION http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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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
| | - Sicheng Zhang
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Dong Zhang
- College of Life Sciences and Institute of Quantitative Biology, Zhejiang University, Hangzhou 310058, China
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33
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Zhang C, Pyle AM. CSSR: assignment of secondary structure to coarse-grained RNA tertiary structures. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:466-471. [PMID: 35362469 PMCID: PMC8972804 DOI: 10.1107/s2059798322001292] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/02/2022] [Indexed: 11/16/2022]
Abstract
CSSR, an algorithm for assigning secondary structures to RNA 3D structures with missing atoms, has been developed. The base-pair assignment accuracy is close to 90% for 3D structures in which only one atom per nucleotide can be empirically identified. RNA secondary-structure (rSS) assignment is one of the most routine forms of analysis of RNA 3D structures. However, traditional rSS assignment programs require full-atomic structures of the individual RNA nucleotides. This prevents their application to the modeling of RNA structures in which base atoms are missing. To address this issue, Coarse-grained Secondary Structure of RNA (CSSR), an algorithm for the assignment of rSS for structures in which nucleobase atomic positions are incomplete, has been developed. Using CSSR, an rSS assignment accuracy of ∼90% is achieved even for RNA structures in which only one backbone atom per nucleotide is known. Thus, CSSR will be useful for the analysis of experimentally determined and computationally predicted RNA 3D structures alike. The source code of CSSR is available at https://github.com/pylelab/CSSR.
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34
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Carrascoza F, Antczak M, Miao Z, Westhof E, Szachniuk M. Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions. RNA (NEW YORK, N.Y.) 2022; 28:250-262. [PMID: 34819324 PMCID: PMC8906551 DOI: 10.1261/rna.078685.121] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.
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Affiliation(s)
- Francisco Carrascoza
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China
| | - Eric Westhof
- Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire CNRS, Architecture et Réactivité de l'ARN, 67084 Strasbourg, France
| | - Marta Szachniuk
- Institute of Computing Science and European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
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35
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Guo ZH, Yuan L, Tan YL, Zhang BG, Shi YZ. RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures. FRONTIERS IN BIOINFORMATICS 2022; 1:809082. [PMID: 36303785 PMCID: PMC9580920 DOI: 10.3389/fbinf.2021.809082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/17/2021] [Indexed: 11/13/2022] Open
Abstract
The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).
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Affiliation(s)
- Zhi-Hao Guo
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
| | - Li Yuan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
| | - Ben-Gong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- *Correspondence: Ya-Zhou Shi,
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36
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rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J 2022; 121:142-156. [PMID: 34798137 PMCID: PMC8758408 DOI: 10.1016/j.bpj.2021.11.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.
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37
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3dRNA: 3D structure prediction from linear to circular RNAs. J Mol Biol 2022; 434:167452. [DOI: 10.1016/j.jmb.2022.167452] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/07/2022] [Accepted: 01/07/2022] [Indexed: 12/30/2022]
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Zerihun MB, Pucci F, Schug A. CoCoNet-boosting RNA contact prediction by convolutional neural networks. Nucleic Acids Res 2021; 49:12661-12672. [PMID: 34871451 PMCID: PMC8682773 DOI: 10.1093/nar/gkab1144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/27/2021] [Accepted: 11/05/2021] [Indexed: 11/24/2022] Open
Abstract
Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins. Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps. We introduce an algorithm called CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the positive predictive value (PPV) of predicted contacts by about 70% with respect to DCA as tested by cross-validation of about eighty RNA structures. However, the direct inclusion of the CoCoNet contacts in 3D modeling tools does not result in a proportional increase of the 3D RNA structure prediction accuracy. Therefore, we suggest that the field develops, in addition to contact PPV, metrics which estimate the expected impact for 3D structure modeling tools better. CoCoNet is freely available and can be found at https://github.com/KIT-MBS/coconet.
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Affiliation(s)
- Mehari B Zerihun
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
| | - Fabrizio Pucci
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Computational Biology and Bioinformatics, Université Libre de Bruxelles 1050, Brussels, Belgium
| | - Alexander Schug
- John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Biology, University of Duisburg-Essen, 45117 Essen, Germany
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Manigrasso J, Marcia M, De Vivo M. Computer-aided design of RNA-targeted small molecules: A growing need in drug discovery. Chem 2021. [DOI: 10.1016/j.chempr.2021.05.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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40
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Binzel DW, Li X, Burns N, Khan E, Lee WJ, Chen LC, Ellipilli S, Miles W, Ho YS, Guo P. Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity. Chem Rev 2021; 121:7398-7467. [PMID: 34038115 PMCID: PMC8312718 DOI: 10.1021/acs.chemrev.1c00009] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
RNA nanotechnology is the bottom-up self-assembly of nanometer-scale architectures, resembling LEGOs, composed mainly of RNA. The ideal building material should be (1) versatile and controllable in shape and stoichiometry, (2) spontaneously self-assemble, and (3) thermodynamically, chemically, and enzymatically stable with a long shelf life. RNA building blocks exhibit each of the above. RNA is a polynucleic acid, making it a polymer, and its negative-charge prevents nonspecific binding to negatively charged cell membranes. The thermostability makes it suitable for logic gates, resistive memory, sensor set-ups, and NEM devices. RNA can be designed and manipulated with a level of simplicity of DNA while displaying versatile structure and enzyme activity of proteins. RNA can fold into single-stranded loops or bulges to serve as mounting dovetails for intermolecular or domain interactions without external linking dowels. RNA nanoparticles display rubber- and amoeba-like properties and are stretchable and shrinkable through multiple repeats, leading to enhanced tumor targeting and fast renal excretion to reduce toxicities. It was predicted in 2014 that RNA would be the third milestone in pharmaceutical drug development. The recent approval of several RNA drugs and COVID-19 mRNA vaccines by FDA suggests that this milestone is being realized. Here, we review the unique properties of RNA nanotechnology, summarize its recent advancements, describe its distinct attributes inside or outside the body and discuss potential applications in nanotechnology, medicine, and material science.
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Affiliation(s)
- Daniel W Binzel
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Xin Li
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Nicolas Burns
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Eshan Khan
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, College of Medicine, Center for RNA Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Wen-Jui Lee
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Li-Ching Chen
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Satheesh Ellipilli
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Wayne Miles
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, College of Medicine, Center for RNA Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Yuan Soon Ho
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Peixuan Guo
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
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41
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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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42
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Sun S, Wang W, Peng Z, Yang J. RNA inter-nucleotide 3D closeness prediction by deep residual neural networks. Bioinformatics 2021; 37:1093-1098. [PMID: 33135062 PMCID: PMC8150135 DOI: 10.1093/bioinformatics/btaa932] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/01/2020] [Accepted: 10/22/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. RESULTS We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. AVAILABILITY AND IMPLEMENTATION The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Saisai Sun
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Wenkai Wang
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
| | - Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin 300072, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin 300071, China
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43
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Tripto E, Orenstein Y. A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data. Brief Bioinform 2021; 22:6278600. [PMID: 34017982 DOI: 10.1093/bib/bbab149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/16/2021] [Accepted: 03/27/2021] [Indexed: 11/14/2022] Open
Abstract
Understanding post-transcriptional gene regulation is a key challenge in today's biology. The new technologies of RNAcompete and RNA Bind-n-Seq enable the measurement of the binding intensities of one RNA-binding protein (RBP) to numerous synthetic RNA sequences in a single experiment. Recently, Van Nostrand et al. reported the results of RNA Bind-n-Seq experiments measuring binding of 78 human RBPs. Because 31 of these RBPs were also covered by RNAcompete technology, a large-scale comparison between implementations of these two in vitro technologies is now possible. Here, we assessed the similarities and differences between binding models, represented as a list of $k$-mer scores, inferred from RNAcompete and RNA Bind-n-Seq, and also measured how well these models predict in vivo binding. Our results show that RNA Bind-n-Seq- and RNAcompete-derived models agree (Pearson correlation $> 0.5$) for most RBPs (23 out of 31). RNA Bind-n-Seq-derived $k$-mer scores predict RNAcompete binding measurements quite well (average Pearson correlation 0.26), and both technologies produce $k$-mer scores that achieve comparable results in predicting in vivo binding (average AUC 0.7). When inspecting RNA structural preferences inferred from the data of RNA Bind-n-Seq and RNAcompete, we observed high concordance in binding preferences. Through our study, we developed a new $k$-mer score for RNA Bind-n-Seq and extended it to include RNA structural preferences.
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Affiliation(s)
- Eitamar Tripto
- Department of Biomedical Engineering at Ben-Gurion University of the Negev, Ben-Gurion, 8410501 Beer-Sheva, Israel
| | - Yaron Orenstein
- School of Electrical and Computer Engineering at Ben-Gurion University of the Negev, Ben-Gurion, 8410501 Beer-Sheva, Israel
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44
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Zhang D, Li J, Chen SJ. IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures. J Chem Theory Comput 2021; 17:1842-1857. [PMID: 33560836 DOI: 10.1021/acs.jctc.0c01148] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.
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Affiliation(s)
- Dong Zhang
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Jun Li
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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45
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Reinharz V, Sarrazin-Gendron R, Waldispühl J. Modeling and Predicting RNA Three-Dimensional Structures. Methods Mol Biol 2021; 2284:17-42. [PMID: 33835435 DOI: 10.1007/978-1-0716-1307-8_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis-Westhof extended base pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. We show how to take advantage of this knowledge to quickly predict three-dimensional structures of large RNA molecules and present the RNA-MoIP web server (http://rnamoip.cs.mcgill.ca) that streamlines the computational and visualization processes. Finally, we show recent advances in the prediction of local 3D motifs from sequence data with the BayesPairing software and discuss its impact toward complete 3D structure prediction.
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Affiliation(s)
- Vladimir Reinharz
- Department of Computer Science, Université du Québec à Montréal, Montréal, QC, Canada
| | | | - Jérôme Waldispühl
- School of Computer Science, McGill University, Montréal, QC, Canada.
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46
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Hurst T, Zhang D, Zhou Y, Chen SJ. A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field. COMMUNICATIONS IN INFORMATION AND SYSTEMS 2021; 21:65-83. [PMID: 34354546 PMCID: PMC8336718 DOI: 10.4310/cis.2021.v21.n1.a4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Because of their potential utility in predicting conformational changes and assessing folding dynamics, coarse-grained (CG) RNA folding models are appealing for rapid characterization of RNA molecules. Previously, we reported the iterative simulated RNA reference state (IsRNA) method for parameterizing a CG force field for RNA folding, which consecutively updates the simulation force field to reflect marginal distributions of folding coordinates in the structure database and extract various energy terms. While the IsRNA model was validated by showing close agreement between the IsRNA-simulated and experimentally observed distributions, here, we expand our theoretical understanding of the model and, in doing so, improve the parameterization process to optimize the subset of included folding coordinates, which leads to accelerated simulations. Using statistical mechanical theory, we analyze the underlying, Bayesian concept that drives parameterization of the energy function, providing a general method for developing predictive, knowledge-based, polymer force fields on the basis of limited data. Furthermore, we propose an optimal parameterization procedure, based on the principal of maximum entropy.
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Affiliation(s)
- Travis Hurst
- Department of Physics, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Dong Zhang
- Department of Physics, University of Missouri-Columbia
| | - Yuanzhe Zhou
- Department of Physics, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO 65211, USA
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47
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Nicholson DA, Sengupta A, Nesbitt DJ. Chirality-Dependent Amino Acid Modulation of RNA Folding. J Phys Chem B 2020; 124:11561-11572. [PMID: 33296203 DOI: 10.1021/acs.jpcb.0c07420] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The preponderance of a specific d- or l-chirality in fats, sugars, amino acids, nucleic acids, and so on is ubiquitous in nature, yet the biological origin of such chiral dominance (i.e., with one enantiomer overwhelmingly present) remains an open question. One plausible proposal for the predominance of l-chirality in amino acids could be through evolutionary templating of chiral RNA-folding via chaperone activity. To help evaluate this possibility, single molecule fluorescence experiments have been performed that measure the chiral dependence of chaperone folding dynamics for the simple tetraloop-tetraloop receptor (TL-TLR) tertiary binding motif in the presence of a series of chiral amino acids. Specifically, d- vs l-arginine is found to accelerate the unfolding of this RNA motif in a chirally selective fashion, with temperature-dependent studies of the kinetics performed to extract free energy, enthalpy, and entropy landscapes for the underlying thermodynamics. Furthermore, all-atom molecular dynamics (MD) simulations are pursued to provide additional physical insight into this chiral sensitivity, which reveal enantiomer-specific sampling of nucleic acid surfaces by d- vs l-arginine and support a putative mechanism for chirally specific denaturation of RNA tertiary structure by arginine but not other amino acids.
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Affiliation(s)
- David A Nicholson
- JILA, National Institute of Standards and Technology and University of Colorado Boulder, Boulder, Colorado 80309 United States.,Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Abhigyan Sengupta
- Department of Physics, Technical University of Munich, Garching, Munich, Germany 85748
| | - David J Nesbitt
- JILA, National Institute of Standards and Technology and University of Colorado Boulder, Boulder, Colorado 80309 United States.,Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, United States.,Department of Physics, University of Colorado Boulder, Boulder, Colorado 80309, United States
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48
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Li B, Cao Y, Westhof E, Miao Z. Advances in RNA 3D Structure Modeling Using Experimental Data. Front Genet 2020; 11:574485. [PMID: 33193680 PMCID: PMC7649352 DOI: 10.3389/fgene.2020.574485] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/02/2020] [Indexed: 12/26/2022] Open
Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
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Affiliation(s)
- Bing Li
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France
| | - Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
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49
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Kumar K, Chakraborty A, Chakrabarti S. PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region. RNA Biol 2020; 18:1152-1159. [PMID: 33103602 DOI: 10.1080/15476286.2020.1836455] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.
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Affiliation(s)
- Krishna Kumar
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India
| | - Abhijit Chakraborty
- Division of Vaccine-Discovery, La Jolla Institute for Immunology, San Diego, California, USA
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India
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50
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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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