1
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Runge F, Franke J, Fertmann D, Backofen R, Hutter F. Partial RNA design. Bioinformatics 2024; 40:i437-i445. [PMID: 38940170 PMCID: PMC11256918 DOI: 10.1093/bioinformatics/btae222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space. RESULTS In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness. AVAILABILITY AND IMPLEMENTATION libLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.
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Affiliation(s)
- Frederic Runge
- Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
| | - Jörg Franke
- Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
| | - Daniel Fertmann
- Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
| | - Rolf Backofen
- Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
| | - Frank Hutter
- Department of Computer Science, University of Freiburg, Freiburg 79110, Germany
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2
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Huang H, Lin Z, He D, Hong L, Li Y. RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models. Bioinformatics 2024; 40:i347-i356. [PMID: 38940178 PMCID: PMC11211841 DOI: 10.1093/bioinformatics/btae259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation. RESULTS In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/ml4bio/RiboDiffusion.
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Affiliation(s)
- Han Huang
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, 999077, China
- School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
| | - Ziqian Lin
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, 999077, China
- School of Artificial Intelligence, Nanjing University, Nanjing, 210023, China
| | - Dongchen He
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, 999077, China
| | - Liang Hong
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, 999077, China
| | - Yu Li
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, 999077, China
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3
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Najeh S, Zandi K, Kharma N, Perreault J. Computational design and experimental verification of pseudoknotted ribozymes. RNA (NEW YORK, N.Y.) 2023; 29:764-776. [PMID: 36868786 PMCID: PMC10187678 DOI: 10.1261/rna.079148.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/27/2022] [Indexed: 05/18/2023]
Abstract
The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.
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Affiliation(s)
- Sabrine Najeh
- INRS - Institut Armand-Frappier, Laval, QC H7V 1B7, Canada
| | - Kasra Zandi
- Software Engineering and Computer Science Department, Concordia University, Montreal, Quebec H3G 1M8, Canada
| | - Nawwaf Kharma
- Electrical and Computer Engineering Department, Concordia University, Montreal, Quebec H3G 1M8, Canada
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4
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Zambrano RAI, Hernandez-Perez C, Takahashi MK. RNA Structure Prediction, Analysis, and Design: An Introduction to Web-Based Tools. Methods Mol Biol 2022; 2518:253-269. [PMID: 35666450 DOI: 10.1007/978-1-0716-2421-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Understanding RNA structure has become critical in the study of RNA in their roles as mediators of biological processes. To aid in these studies, computational algorithms that utilize thermodynamics have been developed to predict RNA secondary structure. Due to the importance of intermolecular interactions, the algorithms have been expanded to determine and predict RNA-RNA hybridization. This chapter discusses popular webservers with the tools for RNA secondary structure prediction, RNA-RNA hybridization, and design. We address key features that distinguish common-functioning programs and their purposes for the interests of the user. Ultimately, we hope this review elucidates web-based tools researchers may take advantage of in their investigations of RNA structure and function.
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Affiliation(s)
| | | | - Melissa K Takahashi
- Department of Biology, California State University Northridge, Northridge, CA, USA.
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5
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Cong H, Liu H, Chen Y, Cao Y. Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization. Med Biol Eng Comput 2020; 58:3017-3038. [PMID: 33078303 DOI: 10.1007/s11517-020-02275-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 10/14/2020] [Indexed: 12/12/2022]
Abstract
In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find. Second, the classifier structure is difficult to determine as it is greatly affected by the distribution of classified data. To solve these problems, a self-evoluting framework using DCNNs for multilocus protein subcellular localization is proposed. It has three characteristics that the previous algorithms do not. The first is that it combines the ant colony algorithm with the DCNN to form a self-evoluting algorithm for multilocus protein subcellular localization. The second is that it randomly groups subcellular sites using a limited random k-labelsets multi-label classification method. It also solves complex problems in a divide-and-conquer approach and proposes a flexible expansion model. The third is that it realizes the random selection feature extraction method in the positioning process and avoids the defects in individual feature extraction methods. The algorithm in the present paper is tested on the human database, and the overall correct rate is 67.17%, which is higher than that for the stacked self-encoder (SAE), support vector machine (SVM), random forest classifier (RF), or single deep convolutional neural network.Graphical abstract The algorithm mentioned in the present paper mainly includes four parts. They are protein sequence data preprocessing, integrated DCNN model construction, finding optimal DCNN combination by ant colony optimization, and protein subcellular localization for sequences. These parts are sequential relationships and the data obtained in the previous part is the basis for the latter part of the function. In the part of data preprocessing, the limited RAkEL multi-label classification method is used to randomly group subcellular sites. At the same time, the feature fusion of protein sequences is carried out by using multiple feature extraction methods. Each combination including features and sites information corresponds to a DCNN model. In the part of finding optimal DCNN combination by ant colony optimization, the main purpose is to find the best combination of DCNN models through the global optimization ability of the ant colony algorithm. The positioning of sequences is mainly to obtain multilocus subcellular localization by the optimal model combination.
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, No. 88, Wenhua East Road, Jinan City, China.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, No. 88, Wenhua East Road, Jinan City, China. .,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China.
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
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6
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Mikl M, Pilpel Y, Segal E. High-throughput interrogation of programmed ribosomal frameshifting in human cells. Nat Commun 2020; 11:3061. [PMID: 32546731 PMCID: PMC7297798 DOI: 10.1038/s41467-020-16961-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/28/2020] [Indexed: 12/30/2022] Open
Abstract
Programmed ribosomal frameshifting (PRF) is the controlled slippage of the translating ribosome to an alternative frame. This process is widely employed by human viruses such as HIV and SARS coronavirus and is critical for their replication. Here, we developed a high-throughput approach to assess the frameshifting potential of a sequence. We designed and tested >12,000 sequences based on 15 viral and human PRF events, allowing us to systematically dissect the rules governing ribosomal frameshifting and discover novel regulatory inputs based on amino acid properties and tRNA availability. We assessed the natural variation in HIV gag-pol frameshifting rates by testing >500 clinical isolates and identified subtype-specific differences and associations between viral load in patients and the optimality of PRF rates. We devised computational models that accurately predict frameshifting potential and frameshifting rates, including subtle differences between HIV isolates. This approach can contribute to the development of antiviral agents targeting PRF.
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Affiliation(s)
- Martin Mikl
- Department of Computer Science and Applied Mathematics, Rehovot, 7610001, Israel.
- Department of Molecular Cell Biology and Weizmann Institute of Science, Rehovot, 7610001, Israel.
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, Mount Carmel, Haifa, 31905, Israel.
| | - Yitzhak Pilpel
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Rehovot, 7610001, Israel.
- Department of Molecular Cell Biology and Weizmann Institute of Science, Rehovot, 7610001, Israel.
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7
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Yamagami R, Kayedkhordeh M, Mathews DH, Bevilacqua PC. Design of highly active double-pseudoknotted ribozymes: a combined computational and experimental study. Nucleic Acids Res 2019; 47:29-42. [PMID: 30462314 PMCID: PMC6326823 DOI: 10.1093/nar/gky1118] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Accepted: 10/24/2018] [Indexed: 01/02/2023] Open
Abstract
Design of RNA sequences that adopt functional folds establishes principles of RNA folding and applications in biotechnology. Inverse folding for RNAs, which allows computational design of sequences that adopt specific structures, can be utilized for unveiling RNA functions and developing genetic tools in synthetic biology. Although many algorithms for inverse RNA folding have been developed, the pseudoknot, which plays a key role in folding of ribozymes and riboswitches, is not addressed in most algorithms. For the few algorithms that attempt to predict pseudoknot-containing ribozymes, self-cleavage activity has not been tested. Herein, we design double-pseudoknot HDV ribozymes using an inverse RNA folding algorithm and test their kinetic mechanisms experimentally. More than 90% of the positively designed ribozymes possess self-cleaving activity, whereas more than 70% of negative control ribozymes, which are predicted to fold to the necessary structure but with low fidelity, do not possess it. Kinetic and mutation analyses reveal that these RNAs cleave site-specifically and with the same mechanism as the WT ribozyme. Most ribozymes react just 50- to 80-fold slower than the WT ribozyme, and this rate can be improved to near WT by modification of a junction. Thus, fast-cleaving functional ribozymes with multiple pseudoknots can be designed computationally.
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Affiliation(s)
- Ryota Yamagami
- Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA.,Center for RNA Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Mohammad Kayedkhordeh
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York, NY 14642, USA
| | - David H Mathews
- Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York, NY 14642, USA.,Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, New York, NY 14642, USA
| | - Philip C Bevilacqua
- Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA.,Center for RNA Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA.,Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
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8
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Koodli RV, Keep B, Coppess KR, Portela F, Das R. EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame. PLoS Comput Biol 2019; 15:e1007059. [PMID: 31247029 PMCID: PMC6597038 DOI: 10.1371/journal.pcbi.1007059] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 04/30/2019] [Indexed: 11/18/2022] Open
Abstract
Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants' moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players' moves are partially stereotyped. Pipelining this CNN's move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.
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Affiliation(s)
- Rohan V. Koodli
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Benjamin Keep
- Department of Education, Stanford University, Stanford, CA, United States of America
| | - Katherine R. Coppess
- Department of Physics, Stanford University, Stanford, CA, United States of America
| | - Fernando Portela
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America
| | | | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States of America
- Department of Physics, Stanford University, Stanford, CA, United States of America
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9
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Evolving methods for rational de novo design of functional RNA molecules. Methods 2019; 161:54-63. [PMID: 31059832 DOI: 10.1016/j.ymeth.2019.04.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 12/16/2022] Open
Abstract
Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.
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10
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Hammer S, Wang W, Will S, Ponty Y. Fixed-parameter tractable sampling for RNA design with multiple target structures. BMC Bioinformatics 2019; 20:209. [PMID: 31023239 PMCID: PMC6482512 DOI: 10.1186/s12859-019-2784-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 03/28/2019] [Indexed: 01/09/2023] Open
Abstract
Background The design of multi-stable RNA molecules has important applications in biology, medicine, and biotechnology. Synthetic design approaches profit strongly from effective in-silico methods, which substantially reduce the need for costly wet-lab experiments. Results We devise a novel approach to a central ingredient of most in-silico design methods: the generation of sequences that fold well into multiple target structures. Based on constraint networks, our approach supports generic Boltzmann-weighted sampling, which enables the positive design of RNA sequences with specific free energies (for each of multiple, possibly pseudoknotted, target structures) and GC-content. Moreover, we study general properties of our approach empirically and generate biologically relevant multi-target Boltzmann-weighted designs for an established design benchmark. Our results demonstrate the efficacy and feasibility of the method in practice as well as the benefits of Boltzmann sampling over the previously best multi-target sampling strategy—even for the case of negative design of multi-stable RNAs. Besides empirically studies, we finally justify the algorithmic details due to a fundamental theoretic result about multi-stable RNA design, namely the #P-hardness of the counting of designs. Conclusion introduces a novel, flexible, and effective approach to multi-target RNA design, which promises broad applicability and extensibility. Our free software is available at: https://github.com/yannponty/RNARedPrint
Supplementary data are available online. Electronic supplementary material The online version of this article (10.1186/s12859-019-2784-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Stefan Hammer
- Dept. Computer Science, and Interdisciplinary Center for Bioinformatics, Univ. Leipzig, Härtelstr. 16-18, Leipzig, D-04107, Germany.,Dept. Theoretical Chemistry, Univ. Vienna, Währingerstr. 17, Wien, A-1090, Austria.,Bioinformatics and Computational Biology Research Group, Univ. Vienna, Währingerstr. 17, Wien, A-1090, Austria
| | - Wei Wang
- CNRS UMR 7161 LIX, Ecole Polytechnique, Bat. Alan Turing, Palaiseau, 91120, France
| | - Sebastian Will
- Dept. Theoretical Chemistry, Univ. Vienna, Währingerstr. 17, Wien, A-1090, Austria. .,Bioinformatics and Computational Biology Research Group, Univ. Vienna, Währingerstr. 17, Wien, A-1090, Austria.
| | - Yann Ponty
- CNRS UMR 7161 LIX, Ecole Polytechnique, Bat. Alan Turing, Palaiseau, 91120, France.
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11
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Raden M, Ali SM, Alkhnbashi OS, Busch A, Costa F, Davis JA, Eggenhofer F, Gelhausen R, Georg J, Heyne S, Hiller M, Kundu K, Kleinkauf R, Lott SC, Mohamed MM, Mattheis A, Miladi M, Richter AS, Will S, Wolff J, Wright PR, Backofen R. Freiburg RNA tools: a central online resource for RNA-focused research and teaching. Nucleic Acids Res 2018; 46:W25-W29. [PMID: 29788132 PMCID: PMC6030932 DOI: 10.1093/nar/gky329] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/03/2018] [Accepted: 05/18/2018] [Indexed: 12/20/2022] Open
Abstract
The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.
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Affiliation(s)
- Martin Raden
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Syed M Ali
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Omer S Alkhnbashi
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Anke Busch
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany
| | - Fabrizio Costa
- Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK
| | - Jason A Davis
- Coreva Scientific, Kaiser-Joseph-Str 198-200, 79098 Freiburg, Germany
| | - Florian Eggenhofer
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Rick Gelhausen
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Jens Georg
- Genetics and Experimental Bioinformatics, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Steffen Heyne
- Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany
| | - Michael Hiller
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Kousik Kundu
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
- Department of Human Genetics, The Wellcome Trust Sanger Institute, Hinxton Cambridge CB10 1HH, UK
| | - Robert Kleinkauf
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Steffen C Lott
- Genetics and Experimental Bioinformatics, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany
| | - Mostafa M Mohamed
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Alexander Mattheis
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Milad Miladi
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | | | - Sebastian Will
- Theoretical Biochemistry Group, University of Vienna, Währingerstraße 17, 1090 Vienna, Austria
| | - Joachim Wolff
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Patrick R Wright
- Department of Clinical Research, Clinical Trial Unit, University of Basel Hospital, Schanzenstrasse 55, 4031 Basel, Switzerland
| | - Rolf Backofen
- Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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12
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Churkin A, Retwitzer MD, Reinharz V, Ponty Y, Waldispühl J, Barash D. Design of RNAs: comparing programs for inverse RNA folding. Brief Bioinform 2018; 19:350-358. [PMID: 28049135 PMCID: PMC6018860 DOI: 10.1093/bib/bbw120] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.
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Affiliation(s)
- Alexander Churkin
- Shamoon College of Engineering and Physics Department at Ben-Gurion University, Beer-Sheva, Israel
| | | | - Vladimir Reinharz
- Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel
- School of Computer Science, McGill University, Montréal QC, Canada
| | - Yann Ponty
- Laboratoire d’informatique, École Polytechnique, Palaiseau, France
| | | | - Danny Barash
- Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel
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13
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Yang X, Yoshizoe K, Taneda A, Tsuda K. RNA inverse folding using Monte Carlo tree search. BMC Bioinformatics 2017; 18:468. [PMID: 29110632 PMCID: PMC5674771 DOI: 10.1186/s12859-017-1882-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 10/26/2017] [Indexed: 11/10/2022] Open
Abstract
Background Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases. Result We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding. Conclusion MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1882-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiufeng Yang
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan
| | - Kazuki Yoshizoe
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi Chuo-ku, Tokyo, 103-0027, Japan
| | - Akito Taneda
- Graduate School of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki, 036-8561, Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8561, Japan. .,Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan. .,RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi Chuo-ku, Tokyo, 103-0027, Japan.
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14
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Backofen R, Engelhardt J, Erxleben A, Fallmann J, Grüning B, Ohler U, Rajewsky N, Stadler PF. RNA-bioinformatics: Tools, services and databases for the analysis of RNA-based regulation. J Biotechnol 2017; 261:76-84. [PMID: 28554830 DOI: 10.1016/j.jbiotec.2017.05.019] [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] [Received: 02/21/2017] [Revised: 05/20/2017] [Accepted: 05/23/2017] [Indexed: 12/26/2022]
Abstract
The importance of RNA-based regulation is becoming more and more evident. Genome-wide sequencing efforts have shown that the majority of the DNA in eukaryotic genomes is transcribed. Advanced high-throughput techniques like CLIP for the genome-wide detection of RNA-protein interactions have shown that post-transcriptional regulation by RNA-binding proteins matches the complexity of transcriptional regulation. The need for a specialized and integrated analysis of RNA-based data has led to the foundation of the RNA Bioinformatics Center (RBC) within the German Network of Bioinformatics Infrastructure (de.NBI). This paper describes the tools, services and databases provided by the RBC, and shows example applications. Furthermore, we have setup an RNA workbench within the Galaxy framework. For an easy dissemination, we offer a virtualized version of Galaxy (via Galaxy Docker) enabling other groups to use our RNA workbench in a very simple way.
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Affiliation(s)
- Rolf Backofen
- Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany; BIOSS Centre for Biological Signaling Studies, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany.
| | - Jan Engelhardt
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany
| | - Anika Erxleben
- Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Jörg Fallmann
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany
| | - Björn Grüning
- Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Uwe Ohler
- Max-Delbrück-Centrum (MDC), Robert-Rössle-Str. 10, D-13092 Berlin, Germany
| | - Nikolaus Rajewsky
- Max-Delbrück-Centrum (MDC), Robert-Rössle-Str. 10, D-13092 Berlin, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany; Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria; RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany; Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
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15
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Zandi K, Butler G, Kharma N. An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures. Front Genet 2016; 7:129. [PMID: 27499762 PMCID: PMC4956659 DOI: 10.3389/fgene.2016.00129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Accepted: 07/06/2016] [Indexed: 01/18/2023] Open
Abstract
Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer.
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Affiliation(s)
- Kasra Zandi
- Computer Science Department, Concordia UniversityMontreal, QC, Canada
| | - Gregory Butler
- Computer Science Department, Concordia UniversityMontreal, QC, Canada
- Centre for Structural and Functional Genomics, Concordia UniversityMontreal, QC, Canada
| | - Nawwaf Kharma
- Centre for Structural and Functional Genomics, Concordia UniversityMontreal, QC, Canada
- Electrical and Computer Engineering Department, Concordia UniversityMontreal, QC, Canada
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