1
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Zhang Y, Yang C, Xiong Y, Xiao Y. 3dDNAscoreA: A scoring function for evaluation of DNA 3D structures. Biophys J 2024; 123:2696-2704. [PMID: 38409781 PMCID: PMC11393702 DOI: 10.1016/j.bpj.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/31/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024] Open
Abstract
DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.
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Affiliation(s)
- Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chenxi Yang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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2
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Zhang Y, Xiong Y, Yang C, Xiao Y. 3dRNA/DNA: 3D Structure Prediction from RNA to DNA. J Mol Biol 2024; 436:168742. [PMID: 39237199 DOI: 10.1016/j.jmb.2024.168742] [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: 01/03/2024] [Revised: 07/03/2024] [Accepted: 08/05/2024] [Indexed: 09/07/2024]
Abstract
There is an increasing need for determining 3D structures of DNAs, e.g., for increasing the efficiency of DNA aptamer selection. Recently, we have proposed a computational method of 3D structure prediction of DNAs, called 3dDNA, which has been integrated into our original web server 3dRNA, now renamed 3dRNA/DNA (http://biophy.hust.edu.cn/new/3dRNA). Currently, 3dDNA can only output the predicted DNA 3D structures for users but cannot rank them as an energy function for assessing DNA 3D structures is still lacking. Here, we first provide a brief introduction to 3dDNA and then introduce a new energy function, 3dDNAscore, for the assessment of DNA 3D structures. 3dDNAscore is an all-atom knowledge-based potential by integrating 86 atomic types from nucleic acids. Benchmarks demonstrate that 3dDNAscore can effectively identify near-native structures from the decoys generated by 3dDNA, thus enhancing the completeness of 3dDNA.
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Affiliation(s)
- 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
| | - Chenxi Yang
- 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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3
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Tarafder S, Bhattacharya D. lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.04.565599. [PMID: 37961488 PMCID: PMC10635153 DOI: 10.1101/2023.11.04.565599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.
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Affiliation(s)
- Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, 24061, USA
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4
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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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5
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Zhang S, Liu Y, Xie L. A universal framework for accurate and efficient geometric deep learning of molecular systems. Sci Rep 2023; 13:19171. [PMID: 37932352 PMCID: PMC10628308 DOI: 10.1038/s41598-023-46382-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023] Open
Abstract
Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications.
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Affiliation(s)
- Shuo Zhang
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10065, USA
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA.
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10065, USA.
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA.
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6
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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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7
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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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8
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Li J, Chen SJ. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling. Nucleic Acids Res 2023; 51:3341-3356. [PMID: 36864729 PMCID: PMC10123122 DOI: 10.1093/nar/gkad122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/14/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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9
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Zeng C, Jian Y, Vosoughi S, Zeng C, Zhao Y. Evaluating native-like structures of RNA-protein complexes through the deep learning method. Nat Commun 2023; 14:1060. [PMID: 36828844 PMCID: PMC9958188 DOI: 10.1038/s41467-023-36720-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.
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Affiliation(s)
- Chengwei Zeng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Yiren Jian
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC, 20052, USA
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
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10
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Childs-Disney JL, Yang X, Gibaut QMR, Tong Y, Batey RT, Disney MD. Targeting RNA structures with small molecules. Nat Rev Drug Discov 2022; 21:736-762. [PMID: 35941229 PMCID: PMC9360655 DOI: 10.1038/s41573-022-00521-4] [Citation(s) in RCA: 173] [Impact Index Per Article: 86.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2022] [Indexed: 01/07/2023]
Abstract
RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.
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Affiliation(s)
| | - Xueyi Yang
- Department of Chemistry, Scripps Research, Jupiter, FL, USA
| | | | - Yuquan Tong
- Department of Chemistry, Scripps Research, Jupiter, FL, USA
| | - Robert T Batey
- Department of Biochemistry, University of Colorado, Boulder, CO, USA.
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11
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Magnus M. rna-tools.online: a Swiss army knife for RNA 3D structure modeling workflow. Nucleic Acids Res 2022; 50:W657-W662. [PMID: 35580057 PMCID: PMC9252763 DOI: 10.1093/nar/gkac372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/20/2022] [Accepted: 05/02/2022] [Indexed: 11/15/2022] Open
Abstract
Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods in recent years; however, many tools developed in the field stay exclusive to only a few bioinformatic groups. To perform a complete RNA 3D structure modeling analysis as proposed by the RNA-Puzzles community, researchers must familiarize themselves with a quite complex set of tools. In order to facilitate the processing of RNA sequences and structures, we previously developed the rna-tools package. However, using rna-tools requires the installation of a mixture of libraries and tools, basic knowledge of the command line and the Python programming language. To provide an opportunity for the broader community of biologists to take advantage of the new developments in RNA structural biology, we developed rna-tools.online. The web server provides a user-friendly platform to perform many standard analyses required for the typical modeling workflow: 3D structure manipulation and editing, structure minimization, structure analysis, quality assessment, and comparison. rna-tools.online supports biologists to start benefiting from the maturing field of RNA 3D structural bioinformatics and can be used for educational purposes. The web server is available at https://rna-tools.online.
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Affiliation(s)
- Marcin Magnus
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
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12
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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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13
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Wienecke A, Laederach A. A novel algorithm for ranking RNA structure candidates. Biophys J 2022; 121:7-10. [PMID: 34896370 PMCID: PMC8758412 DOI: 10.1016/j.bpj.2021.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/02/2021] [Accepted: 12/06/2021] [Indexed: 01/07/2023] Open
Abstract
RNA research is advancing at an ever increasing pace. The newest and most state-of-the-art instruments and techniques have made possible the discoveries of new RNAs, and they have carried the field to new frontiers of disease research, vaccine development, therapeutics, and architectonics. Like proteins, RNAs show a marked relationship between structure and function. A deeper grasp of RNAs requires a finer understanding of their elaborate structures. In pursuit of this, cutting-edge experimental and computational structure-probing techniques output several candidate geometries for a given RNA, each of which is perfectly aligned with experimentally determined parameters. Identifying which structure is the most accurate, however, remains a major obstacle. In recent years, several algorithms have been developed for ranking candidate RNA structures in order from most to least probable, though their levels of accuracy and transparency leave room for improvement. Most recently, advances in both areas are demonstrated by rsRNASP, a novel algorithm proposed by Tan et al. rsRNASP is a residue-separation-based statistical potential for three-dimensional structure evaluation, and it outperforms the leading algorithms in the field.
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Affiliation(s)
- Anastacia Wienecke
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina,Corresponding author
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14
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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: 7] [Impact Index Per Article: 3.5] [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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15
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Kilgour M, Liu T, Walker BD, Ren P, Simine L. E2EDNA: Simulation Protocol for DNA Aptamers with Ligands. J Chem Inf Model 2021; 61:4139-4144. [PMID: 34435773 PMCID: PMC9536994 DOI: 10.1021/acs.jcim.1c00696] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments. As a rule, they do not offer flexibility with respect to the choice of the theoretical engine or direct access to the simulation platform. Practical aptamer optimization often requires higher accuracy predictions for only a small subset of sequences suggested, e.g., by SELEX experiments, but in the absence of a streamlined procedure, this task is extremely time and expertise intensive. We address this gap by introducing E2EDNA, a computational framework that accepts a DNA sequence in the FASTA format and the structures of the desired ligands and performs approximate folding followed by a refining step, analyte complexation, and molecular dynamics sampling at the desired level of accuracy. As a case study, we simulate a DNA-UTP (uridine triphosphate) complex in water using the state-of-the-art AMOEBA polarizable force field. The code is available at https://github.com/InfluenceFunctional/E2EDNA.
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Affiliation(s)
- Michael Kilgour
- Department of Chemistry, McGill University, 801 Sherbrooke St. W, Montreal, Quebec, H3A 0B8, Canada
| | - Tao Liu
- Department of Chemistry, McGill University, 801 Sherbrooke St. W, Montreal, Quebec, H3A 0B8, Canada
| | | | - Pengyu Ren
- Cockrell School of Engineering, University of Texas at Austin
| | - Lena Simine
- Department of Chemistry, McGill University, 801 Sherbrooke St. W, Montreal, Quebec, H3A 0B8, Canada
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16
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Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Geometric deep learning of RNA structure. Science 2021; 373:1047-1051. [PMID: 34446608 PMCID: PMC9829186 DOI: 10.1126/science.abe5650] [Citation(s) in RCA: 155] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 07/14/2021] [Indexed: 01/28/2023]
Abstract
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
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Affiliation(s)
| | - Stephan Eismann
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Andrew M Watkins
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Ramya Rangan
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Masha Karelina
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, CA, USA.
- Department of Physics, Stanford University, Stanford, CA, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, CA, USA.
- Department of Structural Biology, Stanford University, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
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17
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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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18
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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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19
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Singh J, Paliwal K, Singh J, Zhou Y. RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks. J Chem Inf Model 2021; 61:2610-2622. [PMID: 34037398 DOI: 10.1021/acs.jcim.1c00153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to paired bases, and the conformational space of the ribose-phosphate backbone is still too large to be sampled efficiently. Here, we employed the dilated convolutional neural network to predict backbone torsion and pseudotorsion angles using a single RNA sequence as input. The method called SPOT-RNA-1D was trained on a high-resolution training data set and tested on three independent, nonredundant, and high-resolution test sets. The proposed method yields substantially smaller mean absolute errors than the baseline predictors based on random predictions and based on helix conformations according to actual angle distributions. The mean absolute errors for three test sets range from 14°-44° for different angles, compared to 17°-62° by random prediction and 14°-58° by helix prediction. The method also accurately recovers the overall patterns of single or pairwise angle distributions. In general, torsion angles further away from the bases and associated with unpaired bases and paired bases involved in tertiary interactions are more difficult to predict. Compared to the best models in RNA-puzzles experiments, SPOT-RNA-1D yielded more accurate dihedral angles and, thus, are potentially useful as model quality indicators and restraints for RNA structure prediction as in protein structure prediction.
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Affiliation(s)
- Jaswinder Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Peking University Shenzhen Graduate School, Shenzhen 518055, P.R. China
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20
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Magnus M, Antczak M, Zok T, Wiedemann J, Lukasiak P, Cao Y, Bujnicki JM, Westhof E, Szachniuk M, Miao Z. RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools. Nucleic Acids Res 2020; 48:576-588. [PMID: 31799609 PMCID: PMC7145511 DOI: 10.1093/nar/gkz1108] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/06/2019] [Accepted: 11/15/2019] [Indexed: 12/12/2022] Open
Abstract
Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.
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Affiliation(s)
- Marcin Magnus
- International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
- ReMedy-International Research Agenda Unit, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Maciej Antczak
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
| | - Jakub Wiedemann
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Piotr Lukasiak
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, PR China
| | - Janusz M Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
- Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 12 allée Konrad Roentgen, 67084 Strasbourg, France
| | - Marta Szachniuk
- Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200081, China
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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21
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Serafimova K, Mihaylov I, Vassilev D, Avdjieva I, Zielenkiewicz P, Kaczanowski S. Using Machine Learning in Accuracy Assessment of Knowledge-Based Energy and Frequency Base Likelihood in Protein Structures. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304015 DOI: 10.1007/978-3-030-50420-5_43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Many aspects of the study of protein folding and dynamics have been affected by the accumulation of data about native protein structures and recent advances in machine learning. Computational methods for predicting protein structures from their sequences are now heavily based on machine learning tools and on approaches that extract knowledge and rules from data using probabilistic models. Many of these methods use scoring functions to determine which structure best fits a native protein sequence. Using computational approaches, we obtained two scoring functions: knowledge-based energy and likelihood of base frequency, and we compared their accuracy in measuring the sequence structure fit. We compared the machine learning models’ accuracy of predictions for knowledge-based energy and likelihood values to validate our results, showing that likelihood is a more accurate scoring function than knowledge-based energy.
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22
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Zhang T, Hu G, Yang Y, Wang J, Zhou Y. All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State. J Comput Biol 2019; 27:856-867. [PMID: 31638408 DOI: 10.1089/cmb.2019.0251] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.
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Affiliation(s)
- Tongchuan Zhang
- Institute for Glycomics, School of Informatics and Communication Technology, Griffith University, Southport, Australia
| | - Guodong Hu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Jihua Wang
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Yaoqi Zhou
- Institute for Glycomics, School of Informatics and Communication Technology, Griffith University, Southport, Australia.,Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
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23
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Jian Y, Wang X, Qiu J, Wang H, Liu Z, Zhao Y, Zeng C. DIRECT: RNA contact predictions by integrating structural patterns. BMC Bioinformatics 2019; 20:497. [PMID: 31615418 PMCID: PMC6794908 DOI: 10.1186/s12859-019-3099-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 09/13/2019] [Indexed: 01/25/2023] Open
Abstract
Background It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference. Results Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features. Conclusions We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.
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Affiliation(s)
- Yiren Jian
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.,Department of Physics, The George Washington University, Washington DC, 20052, USA
| | - Xiaonan Wang
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Jaidi Qiu
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Huiwen Wang
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Zhichao Liu
- Department of Physics, The George Washington University, Washington DC, 20052, USA
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington DC, 20052, USA.
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24
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Yan Y, Wen Z, Zhang D, Huang SY. Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method. Nucleic Acids Res 2019; 46:e56. [PMID: 29506237 PMCID: PMC5961370 DOI: 10.1093/nar/gky113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 02/08/2018] [Indexed: 11/15/2022] Open
Abstract
RNA–RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA–RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA–RNA interactions based on a training set of 97 diverse RNA–RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA–RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA–RNA complexes.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Zeyu Wen
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
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25
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Tan YL, Feng CJ, Jin L, Shi YZ, Zhang W, Tan ZJ. What is the best reference state for building statistical potentials in RNA 3D structure evaluation? RNA (NEW YORK, N.Y.) 2019; 25:793-812. [PMID: 30996105 PMCID: PMC6573789 DOI: 10.1261/rna.069872.118] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 04/06/2019] [Indexed: 05/14/2023]
Abstract
Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.
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Affiliation(s)
- Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Chen-Jie Feng
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China
| | - Wenbing Zhang
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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26
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Wang YZ, Li J, Zhang S, Huang B, Yao G, Zhang J. An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks. Mol Biol 2019. [DOI: 10.1134/s0026893319010175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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27
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Stasiewicz J, Mukherjee S, Nithin C, Bujnicki JM. QRNAS: software tool for refinement of nucleic acid structures. BMC STRUCTURAL BIOLOGY 2019; 19:5. [PMID: 30898165 PMCID: PMC6429776 DOI: 10.1186/s12900-019-0103-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Accepted: 03/05/2019] [Indexed: 01/03/2023]
Abstract
BACKGROUND Computational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure. RESULTS We present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues. CONCLUSIONS We demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.
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Affiliation(s)
- Juliusz Stasiewicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, 02-109 Warsaw, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, 02-109 Warsaw, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, 02-109 Warsaw, Poland
| | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, 02-109 Warsaw, Poland
- Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, 61-614 Poznań, Poland
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Ponce-Salvatierra A, Astha, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM. Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 2019; 39:BSR20180430. [PMID: 30670629 PMCID: PMC6367127 DOI: 10.1042/bsr20180430] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 01/02/2023] Open
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
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Affiliation(s)
- Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Astha
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Katarzyna Merdas
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, Poznan PL-61-614, Poland
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Dans PD, Gallego D, Balaceanu A, Darré L, Gómez H, Orozco M. Modeling, Simulations, and Bioinformatics at the Service of RNA Structure. Chem 2019. [DOI: 10.1016/j.chempr.2018.09.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Li J, Zhu W, Wang J, Li W, Gong S, Zhang J, Wang W. RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks. PLoS Comput Biol 2018; 14:e1006514. [PMID: 30481171 PMCID: PMC6258470 DOI: 10.1371/journal.pcbi.1006514] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 09/14/2018] [Indexed: 11/18/2022] Open
Abstract
Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA structures. All these potentials are based on the inverse Boltzmann formula, while differing in the choice of the geometrical descriptor, reference state, and training dataset. Via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3D convolutional neural network (CNN)-based approach as a quality evaluator for RNA 3D structures, which used a 3D grid representation of the structure as input without extracting features manually. The RNA structures were evaluated by examining each nucleotide, so our method can also provide local quality assessment. Two sets of training samples were built. The first one included 1 million samples generated by high-temperature molecular dynamics (MD) simulations and the second one included 1 million samples generated by Monte Carlo (MC) structure prediction. Both MD and MC procedures were performed for a non-redundant set of 414 RNAs. For two training datasets (one including only MD training samples and the other including both MD and MC training samples), we trained two neural networks, named RNA3DCNN_MD and RNA3DCNN_MDMC, respectively. The former is suitable for assessing near-native structures, while the latter is suitable for assessing structures covering large structural space. We tested the performance of our method and made comparisons with four other traditional scoring functions. On two of three test datasets, our method performed similarly to the state-of-the-art traditional scoring function, and on the third test dataset, our method was far superior to other scoring functions. Our method can be downloaded from https://github.com/lijunRNA/RNA3DCNN.
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Affiliation(s)
- Jun Li
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Wei Zhu
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Jun Wang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Wenfei Li
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Sheng Gong
- Department of Pharmaceutics, Nanjing General Hospital, Nanjing University Medical School, Nanjing, China
| | - Jian Zhang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Wei Wang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
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31
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Zhao Y, Wang J, Zeng C, Xiao Y. Evaluation of RNA secondary structure prediction for both base-pairing and topology. BIOPHYSICS REPORTS 2018. [DOI: 10.1007/s41048-018-0058-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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32
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Shi YZ, Jin L, Feng CJ, Tan YL, Tan ZJ. Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions. PLoS Comput Biol 2018; 14:e1006222. [PMID: 29879103 PMCID: PMC6007934 DOI: 10.1371/journal.pcbi.1006222] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 06/19/2018] [Accepted: 05/22/2018] [Indexed: 01/30/2023] Open
Abstract
RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.
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Affiliation(s)
- Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Lei Jin
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Chen-Jie Feng
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Ya-Lan Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
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33
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Masso M. All-atom four-body knowledge-based statistical potential to distinguish native tertiary RNA structures from nonnative folds. J Theor Biol 2018; 453:58-67. [PMID: 29782930 DOI: 10.1016/j.jtbi.2018.05.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 04/05/2018] [Accepted: 05/17/2018] [Indexed: 11/16/2022]
Abstract
Scientific breakthroughs in recent decades have uncovered the capability of RNA molecules to fulfill a wide array of structural, functional, and regulatory roles in living cells, leading to a concomitantly significant increase in both the number and diversity of experimentally determined RNA three-dimensional (3D) structures. Atomic coordinates from a representative training set of solved RNA structures, displaying low sequence and structure similarity, facilitate derivation of knowledge-based energy functions. Here we develop an all-atom four-body statistical potential and evaluate its capacity to distinguish native RNA 3D structures from nonnative folds based on calculated free energy scores. Atomic four-body nearest-neighbors are objectively identified by their occurrence as tetrahedral vertices in the Delaunay tessellations of RNA structures, and rates of atomic quadruplet interactions expected by chance are obtained from a multinomial reference distribution. Our four-body energy function, referred to as RAMP (ribonucleic acids multibody potential), is subsequently derived by applying the inverted Boltzmann principle to the frequency data, yielding an energy score for each type of atomic quadruplet interaction. Several well-known benchmark datasets reveal that RAMP is comparable with, and often outperforms, existing knowledge- and physics-based energy functions. To the best of our knowledge, this is the first study detailing an RNA tertiary structure-based multibody statistical potential and its comparative evaluation.
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Affiliation(s)
- Majid Masso
- School of Systems Biology, 10900 University Blvd. MS 5B3, George Mason University, Manassas, VA 20110 USA.
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34
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Šponer J, Bussi G, Krepl M, Banáš P, Bottaro S, Cunha RA, Gil-Ley A, Pinamonti G, Poblete S, Jurečka P, Walter NG, Otyepka M. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. Chem Rev 2018; 118:4177-4338. [PMID: 29297679 PMCID: PMC5920944 DOI: 10.1021/acs.chemrev.7b00427] [Citation(s) in RCA: 336] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Indexed: 12/14/2022]
Abstract
With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.
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Affiliation(s)
- Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory, Department of Biology , University of Copenhagen , Copenhagen 2200 , Denmark
| | - Richard A Cunha
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Alejandro Gil-Ley
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Giovanni Pinamonti
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Simón Poblete
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Petr Jurečka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Nils G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
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Exploring DNA dynamics within oligonucleosomes with coarse-grained simulations: SIRAH force field extension for protein-DNA complexes. Biochem Biophys Res Commun 2018; 498:319-326. [DOI: 10.1016/j.bbrc.2017.09.086] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/06/2017] [Accepted: 09/15/2017] [Indexed: 12/22/2022]
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36
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Abstract
In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.
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Affiliation(s)
- Li-Zhen Sun
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Dong Zhang
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
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37
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Wang J, Xiao Y. Using 3dRNA for RNA 3-D Structure Prediction and Evaluation. ACTA ACUST UNITED AC 2017; 57:5.9.1-5.9.12. [PMID: 28463400 DOI: 10.1002/cpbi.21] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This unit describes how to use 3dRNA to predict RNA 3-D structures from their sequences and secondary (2-D) structures, and how to use 3dRNAscore to evaluate the predicted structures. The predicted RNA 3-D structures can be used to predict or understand their functions and can also be used to find the interactions between the RNA and other molecules. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Jian Wang
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Xiao
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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38
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Li J, Zhang J, Wang J, Li W, Wang W. Structure Prediction of RNA Loops with a Probabilistic Approach. PLoS Comput Biol 2016; 12:e1005032. [PMID: 27494763 PMCID: PMC4975501 DOI: 10.1371/journal.pcbi.1005032] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 06/26/2016] [Indexed: 12/13/2022] Open
Abstract
The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.
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Affiliation(s)
- Jun Li
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Jian Zhang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Jun Wang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Wenfei Li
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
| | - Wei Wang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China
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39
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Coarse-grained modeling of RNA 3D structure. Methods 2016; 103:138-56. [PMID: 27125734 DOI: 10.1016/j.ymeth.2016.04.026] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 04/21/2016] [Accepted: 04/22/2016] [Indexed: 12/21/2022] Open
Abstract
Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.
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40
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Lukasiak P, Antczak M, Ratajczak T, Szachniuk M, Popenda M, Adamiak RW, Blazewicz J. RNAssess--a web server for quality assessment of RNA 3D structures. Nucleic Acids Res 2015; 43:W502-6. [PMID: 26068469 PMCID: PMC4489242 DOI: 10.1093/nar/gkv557] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 05/16/2015] [Indexed: 01/15/2023] Open
Abstract
Nowadays, various methodologies can be applied to model RNA 3D structure. Thus, the plausible quality assessment of 3D models has a fundamental impact on the progress of structural bioinformatics. Here, we present RNAssess server, a novel tool dedicated to visual evaluation of RNA 3D models in the context of the known reference structure for a wide range of accuracy levels (from atomic to the whole molecule perspective). The proposed server is based on the concept of local neighborhood, defined as a set of atoms observed within a sphere localized around a central atom of a particular residue. A distinctive feature of our server is the ability to perform simultaneous visual analysis of the model-reference structure coherence. RNAssess supports the quality assessment through delivering both static and interactive visualizations that allows an easy identification of native-like models and/or chosen structural regions of the analyzed molecule. A combination of results provided by RNAssess allows us to rank analyzed models. RNAssess offers new route to a fast and efficient 3D model evaluation suitable for the RNA-Puzzles challenge. The proposed automated tool is implemented as a free and open to all users web server with an user-friendly interface and can be accessed at: http://rnassess.cs.put.poznan.pl/
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Affiliation(s)
- Piotr Lukasiak
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Tomasz Ratajczak
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science, 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
| | - Mariusz Popenda
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Ryszard W Adamiak
- Institute of Computing Science, 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
| | - Jacek Blazewicz
- Institute of Computing Science, 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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Miao Z, Adamiak RW, Blanchet MF, Boniecki M, Bujnicki JM, Chen SJ, Cheng C, Chojnowski G, Chou FC, Cordero P, Cruz JA, Ferré-D'Amaré AR, Das R, Ding F, Dokholyan NV, Dunin-Horkawicz S, Kladwang W, Krokhotin A, Lach G, Magnus M, Major F, Mann TH, Masquida B, Matelska D, Meyer M, Peselis A, Popenda M, Purzycka KJ, Serganov A, Stasiewicz J, Szachniuk M, Tandon A, Tian S, Wang J, Xiao Y, Xu X, Zhang J, Zhao P, Zok T, Westhof E. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA (NEW YORK, N.Y.) 2015; 21:1066-84. [PMID: 25883046 PMCID: PMC4436661 DOI: 10.1261/rna.049502.114] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2015] [Accepted: 02/12/2015] [Indexed: 05/04/2023]
Abstract
This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.
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Affiliation(s)
- Zhichao Miao
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 67000 Strasbourg, France
| | - Ryszard W Adamiak
- Department of Structural Chemistry and Biology of Nucleic Acids, Structural Chemistry of Nucleic Acids Laboratory, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Marc-Frédérick Blanchet
- Institute for Research in Immunology and Cancer (IRIC), Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada H3C 3J7
| | - Michal Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland Laboratory of Bioinformatics, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, 61-614 Poznan, Poland
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, and Informatics Institute, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Clarence Cheng
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Grzegorz Chojnowski
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Fang-Chieh Chou
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Pablo Cordero
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - José Almeida Cruz
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 67000 Strasbourg, France
| | | | - Rhiju Das
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Feng Ding
- Department of Physics and Astronomy, College of Engineering and Science, Clemson University, Clemson, South Carolina 29634, USA
| | - Nikolay V Dokholyan
- Department of Biochemistry and Biophysics, University of North Carolina, School of Medicine, Chapel Hill, North Carolina 27599, USA
| | - Stanislaw Dunin-Horkawicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Wipapat Kladwang
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Andrey Krokhotin
- Department of Biochemistry and Biophysics, University of North Carolina, School of Medicine, Chapel Hill, North Carolina 27599, USA
| | - Grzegorz Lach
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Marcin Magnus
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - François Major
- Institute for Research in Immunology and Cancer (IRIC), Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada H3C 3J7
| | - Thomas H Mann
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Benoît Masquida
- Génétique Moléculaire Génomique Microbiologie, Institut de physiologie et de la chimie biologique, 67084 Strasbourg, France
| | - Dorota Matelska
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Mélanie Meyer
- Institut de génétique et de biologie moléculaire et cellulaire, 67400 Strasbourg, France
| | - Alla Peselis
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York 10016, USA
| | - Mariusz Popenda
- Department of Structural Chemistry and Biology of Nucleic Acids, Structural Chemistry of Nucleic Acids Laboratory, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Katarzyna J Purzycka
- Department of Structural Chemistry and Biology of Nucleic Acids, Structural Chemistry of Nucleic Acids Laboratory, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Alexander Serganov
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York 10016, USA
| | - Juliusz Stasiewicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 02-109 Warsaw, Poland
| | - Marta Szachniuk
- Poznan University of Technology, Institute of Computing Science, 60-965 Poznan, Poland
| | - Arpit Tandon
- Department of Biochemistry and Biophysics, University of North Carolina, School of Medicine, Chapel Hill, North Carolina 27599, USA
| | - Siqi Tian
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Jian Wang
- Department of Physics, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Yi Xiao
- Department of Physics, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Xiaojun Xu
- Department of Physics and Astronomy, Department of Biochemistry, and Informatics Institute, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Jinwei Zhang
- National Heart, Lung and Blood Institute, Bethesda, Maryland 20892-8012, USA
| | - Peinan Zhao
- Department of Physics and Astronomy, Department of Biochemistry, and Informatics Institute, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Tomasz Zok
- Poznan University of Technology, Institute of Computing Science, 60-965 Poznan, Poland
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 67000 Strasbourg, France
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42
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Wang J, Zhao Y, Zhu C, Xiao Y. 3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures. Nucleic Acids Res 2015; 43:e63. [PMID: 25712091 PMCID: PMC4446410 DOI: 10.1093/nar/gkv141] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 02/06/2015] [Indexed: 01/02/2023] Open
Abstract
Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.
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Affiliation(s)
- Jian Wang
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yunjie Zhao
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Chunyan Zhu
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Biomolecular Physics and Modeling Group, Department of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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43
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Bottaro S, Di Palma F, Bussi G. The role of nucleobase interactions in RNA structure and dynamics. Nucleic Acids Res 2014; 42:13306-14. [PMID: 25355509 PMCID: PMC4245972 DOI: 10.1093/nar/gku972] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The intricate network of interactions observed in RNA three-dimensional structures is often described in terms of a multitude of geometrical properties, including helical parameters, base pairing/stacking, hydrogen bonding and backbone conformation. We show that a simple molecular representation consisting in one oriented bead per nucleotide can account for the fundamental structural properties of RNA. In this framework, canonical Watson-Crick, non-Watson-Crick base-pairing and base-stacking interactions can be unambiguously identified within a well-defined interaction shell. We validate this representation by performing two independent, complementary tests. First, we use it to construct a sequence-independent, knowledge-based scoring function for RNA structural prediction, which compares favorably to fully atomistic, state-of-the-art techniques. Second, we define a metric to measure deviation between RNA structures that directly reports on the differences in the base–base interaction network. The effectiveness of this metric is tested with respect to the ability to discriminate between structurally and kinetically distant RNA conformations, performing better compared to standard techniques. Taken together, our results suggest that this minimalist, nucleobase-centric representation captures the main interactions that are relevant for describing RNA structure and dynamics.
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Affiliation(s)
- Sandro Bottaro
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| | - Francesco Di Palma
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
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44
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Olechnovič K, Venclovas C. The use of interatomic contact areas to quantify discrepancies between RNA 3D models and reference structures. Nucleic Acids Res 2014; 42:5407-15. [PMID: 24623815 PMCID: PMC4027170 DOI: 10.1093/nar/gku191] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Growing interest in computational prediction of ribonucleic acid (RNA) three-dimensional structure has highlighted the need for reliable and meaningful methods to compare models and experimental structures. We present a structure superposition-free method to quantify both the local and global accuracy of RNA structural models with respect to the reference structure. The method, initially developed for proteins and here extended to RNA, closely reflects physical interactions, has a simple definition, a fixed range of values and no arbitrary parameters. It is based on the correspondence of respective contact areas between nucleotides or their components (base or backbone). The better is the agreement between respective contact areas in a model and the reference structure, the more accurate the model is considered to be. Since RNA bases account for the largest contact areas, we further distinguish stacking and non-stacking contacts. We have extensively tested the contact area-based evaluation method and found it effective in both revealing local discrepancies and ranking models by their overall quality. Compared to other reference-based RNA model evaluation methods, the new method shows a stronger emphasis on stereochemical quality of models. In addition, it takes into account model completeness, enabling a meaningful evaluation of full models and those missing some residues.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Vilnius University, Graičiūno 8, Vilnius LT-02241, Lithuania Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius LT-03225, Lithuania
| | - Ceslovas Venclovas
- Institute of Biotechnology, Vilnius University, Graičiūno 8, Vilnius LT-02241, Lithuania
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45
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Olechnovič K, Venclovas C. Voronota: A fast and reliable tool for computing the vertices of the Voronoi diagram of atomic balls. J Comput Chem 2014; 35:672-81. [PMID: 24523197 DOI: 10.1002/jcc.23538] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2013] [Revised: 12/11/2013] [Accepted: 01/06/2014] [Indexed: 12/22/2022]
Abstract
The Voronoi diagram of balls, corresponding to atoms of van der Waals radii, is particularly well-suited for the analysis of three-dimensional structures of biological macromolecules. However, due to the shortage of practical algorithms and the corresponding software, simpler approaches are often used instead. Here, we present a simple and robust algorithm for computing the vertices of the Voronoi diagram of balls. The vertices of Voronoi cells correspond to the centers of the empty tangent spheres defined by quadruples of balls. The algorithm is implemented as an open-source software tool, Voronota. Large-scale tests show that Voronota is a fast and reliable tool for processing both experimentally determined and computationally modeled macromolecular structures. Voronota can be easily deployed and may be used for the development of various other structure analysis tools that utilize the Voronoi diagram of balls. Voronota is available at: http://www.ibt.lt/bioinformatics/voronota.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Vilnius University, Graičiūno 8, Vilnius, LT-02241, Lithuania; Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius, LT-03225, Lithuania
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46
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Abstract
This chapter gives an overview over the current methods for automated modeling of RNA structures, with emphasis on template-based methods. The currently used approaches to RNA modeling are presented with a side view on the protein world, where many similar ideas have been used. Two main programs for automated template-based modeling are presented: ModeRNA assembling structures from fragments and MacroMoleculeBuilder performing a simulation to satisfy spatial restraints. Both approaches have in common that they require an alignment of the target sequence to a known RNA structure that is used as a modeling template. As a way to find promising template structures and to align the target and template sequences, we propose a pipeline combining the ParAlign and Infernal programs on RNA family data from Rfam. We also briefly summarize template-free methods for RNA 3D structure prediction. Typically, RNA structures generated by automated modeling methods require local or global optimization. Thus, we also discuss methods that can be used for local or global refinement of RNA structures.
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Affiliation(s)
- Kristian Rother
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland,
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47
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Matelska D, Purta E, Panek S, Boniecki MJ, Bujnicki JM, Dunin-Horkawicz S. S6:S18 ribosomal protein complex interacts with a structural motif present in its own mRNA. RNA (NEW YORK, N.Y.) 2013; 19:1341-8. [PMID: 23980204 PMCID: PMC3854524 DOI: 10.1261/rna.038794.113] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 07/05/2013] [Indexed: 05/24/2023]
Abstract
Prokaryotic ribosomal protein genes are typically grouped within highly conserved operons. In many cases, one or more of the encoded proteins not only bind to a specific site in the ribosomal RNA, but also to a motif localized within their own mRNA, and thereby regulate expression of the operon. In this study, we computationally predicted an RNA motif present in many bacterial phyla within the 5' untranslated region of operons encoding ribosomal proteins S6 and S18. We demonstrated that the S6:S18 complex binds to this motif, which we hereafter refer to as the S6:S18 complex-binding motif (S6S18CBM). This motif is a conserved CCG sequence presented in a bulge flanked by a stem and a hairpin structure. A similar structure containing a CCG trinucleotide forms the S6:S18 complex binding site in 16S ribosomal RNA. We have constructed a 3D structural model of a S6:S18 complex with S6S18CBM, which suggests that the CCG trinucleotide in a specific structural context may be specifically recognized by the S18 protein. This prediction was supported by site-directed mutagenesis of both RNA and protein components. These results provide a molecular basis for understanding protein-RNA recognition and suggest that the S6S18CBM is involved in an auto-regulatory mechanism.
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MESH Headings
- 5' Untranslated Regions/genetics
- Bacterial Proteins/chemistry
- Bacterial Proteins/genetics
- Bacterial Proteins/metabolism
- Base Pairing
- Base Sequence
- Binding Sites
- Electrophoretic Mobility Shift Assay
- Escherichia coli/genetics
- Escherichia coli/metabolism
- Models, Molecular
- Molecular Sequence Data
- Nucleic Acid Conformation
- Operon/genetics
- Protein Binding
- Protein Structure, Tertiary
- RNA, Bacterial/chemistry
- RNA, Bacterial/genetics
- RNA, Bacterial/metabolism
- RNA, Messenger/chemistry
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
- RNA, Ribosomal/chemistry
- RNA, Ribosomal/genetics
- RNA, Ribosomal/metabolism
- Ribosomal Protein S6/chemistry
- Ribosomal Protein S6/genetics
- Ribosomal Protein S6/metabolism
- Ribosomal Proteins/chemistry
- Ribosomal Proteins/genetics
- Ribosomal Proteins/metabolism
- Ribosomes/chemistry
- Ribosomes/genetics
- Ribosomes/metabolism
- Sequence Homology, Nucleic Acid
- Thermus thermophilus/genetics
- Thermus thermophilus/metabolism
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Affiliation(s)
- Dorota Matelska
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
| | - Elzbieta Purta
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
| | - Sylwia Panek
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
| | - Michal J. Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
| | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, 61-614, Poland
| | - Stanislaw Dunin-Horkawicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, 02-109, Poland
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48
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Computational modeling of protein-RNA complex structures. Methods 2013; 65:310-9. [PMID: 24083976 DOI: 10.1016/j.ymeth.2013.09.014] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 09/17/2013] [Accepted: 09/19/2013] [Indexed: 12/26/2022] Open
Abstract
Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.
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49
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Norambuena T, Cares JF, Capriotti E, Melo F. WebRASP: a server for computing energy scores to assess the accuracy and stability of RNA 3D structures. Bioinformatics 2013; 29:2649-50. [PMID: 23929030 PMCID: PMC3789544 DOI: 10.1093/bioinformatics/btt441] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Summary: The understanding of the biological role of RNA molecules has changed. Although it is widely accepted that RNAs play important regulatory roles without necessarily coding for proteins, the functions of many of these non-coding RNAs are unknown. Thus, determining or modeling the 3D structure of RNA molecules as well as assessing their accuracy and stability has become of great importance for characterizing their functional activity. Here, we introduce a new web application, WebRASP, that uses knowledge-based potentials for scoring RNA structures based on distance-dependent pairwise atomic interactions. This web server allows the users to upload a structure in PDB format, select several options to visualize the structure and calculate the energy profile. The server contains online help, tutorials and links to other related resources. We believe this server will be a useful tool for predicting and assessing the quality of RNA 3D structures. Availability and implementation: The web server is available at http://melolab.org/webrasp. It has been tested on the most popular web browsers and requires Java plugin for Jmol visualization. Contact:fmelo@bio.puc.cl
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Affiliation(s)
- Tomas Norambuena
- Facultad de Ciencias Biologicas, Departamento de Genetica Molecular y Microbiologia, Pontificia Universidad Catolica de Chile, Alameda 340, Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chile and Division of Informatics, Department of Pathology, University of Alabama at Birmingham, 619 19 st. south, Birmingham, AL 35249, USA
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50
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Sim AYL, Schwander O, Levitt M, Bernauer J. Evaluating mixture models for building RNA knowledge-based potentials. J Bioinform Comput Biol 2012; 10:1241010. [PMID: 22809345 DOI: 10.1142/s0219720012410107] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting - a commonly taken approach - to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.
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Affiliation(s)
- Adelene Y L Sim
- Department of Applied Physics, Stanford University, Stanford, CA 94305-4090, USA.
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