1
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Khoroshkin M, Asarnow D, Zhou S, Navickas A, Winters A, Goudreau J, Zhou SK, Yu J, Palka C, Fish L, Borah A, Yousefi K, Carpenter C, Ansel KM, Cheng Y, Gilbert LA, Goodarzi H. A systematic search for RNA structural switches across the human transcriptome. Nat Methods 2024; 21:1634-1645. [PMID: 39014073 PMCID: PMC11399106 DOI: 10.1038/s41592-024-02335-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/29/2024] [Indexed: 07/18/2024]
Abstract
RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3' untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.
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Affiliation(s)
- Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Asarnow
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Shaopu Zhou
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Albertas Navickas
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institut Curie, UMR3348 CNRS, U1278 Inserm, Orsay, France
| | - Aidan Winters
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - Jackson Goudreau
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Simon K Zhou
- Sandler Asthma Basic Research Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Johnny Yu
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Christina Palka
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
| | - Lisa Fish
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ashir Borah
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Kian Yousefi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Christopher Carpenter
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - K Mark Ansel
- Sandler Asthma Basic Research Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Yifan Cheng
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA
| | - Luke A Gilbert
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Arc Institute, Palo Alto, CA, USA.
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2
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Xu J, Hou J, Ding M, Wang Z, Chen T. Riboswitches, from cognition to transformation. Synth Syst Biotechnol 2023; 8:357-370. [PMID: 37325181 PMCID: PMC10265488 DOI: 10.1016/j.synbio.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/20/2023] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
Riboswitches are functional RNA elements that regulate gene expression by directly detecting metabolites. Twenty years have passed since it was first discovered, researches on riboswitches are becoming increasingly standardized and refined, which could significantly promote people's cognition of RNA function as well. Here, we focus on some representative orphan riboswitches, enumerate the structural and functional transformation and artificial design of riboswitches including the coupling with ribozymes, hoping to attain a comprehensive understanding of riboswitch research.
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Affiliation(s)
- Jingdong Xu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Junyuan Hou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Mengnan Ding
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Zhiwen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
| | - Tao Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300350, China
- Frontier Science Center for Synthetic Biology, Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin, 300350, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300350, China
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3
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Noell SE, Hellweger FL, Temperton B, Giovannoni SJ. A Reduction of Transcriptional Regulation in Aquatic Oligotrophic Microorganisms Enhances Fitness in Nutrient-Poor Environments. Microbiol Mol Biol Rev 2023; 87:e0012422. [PMID: 36995249 PMCID: PMC10304753 DOI: 10.1128/mmbr.00124-22] [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: 03/31/2023] Open
Abstract
In this review, we consider the regulatory strategies of aquatic oligotrophs, microbial cells that are adapted to thrive under low-nutrient concentrations in oceans, lakes, and other aquatic ecosystems. Many reports have concluded that oligotrophs use less transcriptional regulation than copiotrophic cells, which are adapted to high nutrient concentrations and are far more common subjects for laboratory investigations of regulation. It is theorized that oligotrophs have retained alternate mechanisms of regulation, such as riboswitches, that provide shorter response times and smaller amplitude responses and require fewer cellular resources. We examine the accumulated evidence for distinctive regulatory strategies in oligotrophs. We explore differences in the selective pressures copiotrophs and oligotrophs encounter and ask why, although evolutionary history gives copiotrophs and oligotrophs access to the same regulatory mechanisms, they might exhibit distinctly different patterns in how these mechanisms are used. We discuss the implications of these findings for understanding broad patterns in the evolution of microbial regulatory networks and their relationships to environmental niche and life history strategy. We ask whether these observations, which have emerged from a decade of increased investigation of the cell biology of oligotrophs, might be relevant to recent discoveries of many microbial cell lineages in nature that share with oligotrophs the property of reduced genome size.
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Affiliation(s)
- Stephen E. Noell
- Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
| | | | - Ben Temperton
- School of Biosciences, University of Exeter, Exeter, United Kingdom
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4
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Wang Q, Wang Z, He Y, Xiong B, Li Y, Wang F. Chemical and structural modification of RNA-cleaving DNAzymes for efficient biosensing and biomedical applications. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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5
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Wu Y, Zhu L, Li S, Chu H, Wang X, Xu W. High content design of riboswitch biosensors: All-around rational module-by-module design. Biosens Bioelectron 2022; 220:114887. [DOI: 10.1016/j.bios.2022.114887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/27/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022]
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6
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Vargas-Junior V, Antunes D, Guimarães AC, Caffarena E. In silico investigation of riboswitches in fungi: structural and dynamical insights into TPP riboswitches in Aspergillus oryzae. RNA Biol 2022; 19:90-103. [PMID: 34989318 PMCID: PMC8786325 DOI: 10.1080/15476286.2021.2015174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Riboswitches are RNA sensors affecting post-transcriptional processes through their ability to bind to small molecules. Thiamine pyrophosphate (TPP) riboswitch plays a crucial role in regulating genes involved in synthesizing or transporting thiamine and phosphorylated derivatives in bacteria, archaea, plants, and fungi. Although TPP riboswitch is reasonably well known in bacteria, there is a gap in the knowledge of the fungal TPP riboswitches structure and dynamics, involving mainly sequence variation and TPP interaction with the aptamers. On the other hand, the increase of fungal infections and antifungal resistance raises the need for new antifungal therapies. In this work, we used computational approaches to build three-dimensional models for the three TPP riboswitches identified in Aspergillus oryzae, in which we studied their structure, dynamics, and binding free energy change (ΔGbind) with TPP. Interaction patterns between the TPP and the surrounding nucleotides were conserved among the three models, evidencing high structural conservation. Furthermore, we show that the TPP riboswitch from the A. oryzae NMT1 gene behaves similarly to the E. coli thiA gene concerning the ΔGbind. In contrast, mutations in the fungal TPP riboswitches from THI4 and the nucleoside transporter genes led to structural differences, affecting the binding-site volume, hydrogen bond occupancy, and ΔGbind. Besides, the number of water molecules surrounding TPP influenced the ΔGbind considerably. Notably, our ΔGbind estimation agreed with previous experimental data, reinforcing the relationship between sequence conservation and TPP interaction.
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Affiliation(s)
- Valdemir Vargas-Junior
- Computational Biophysics and Molecular Modeling Group, Scientific Computing Programme (Procc - Fiocruz), Rio de Janeiro, Brazil
| | - Deborah Antunes
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute (Ioc - Fiocruz), Rio de Janeiro, Brazil
| | - Ana Carolina Guimarães
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute (Ioc - Fiocruz), Rio de Janeiro, Brazil
| | - Ernesto Caffarena
- Computational Biophysics and Molecular Modeling Group, Scientific Computing Programme (Procc - Fiocruz), Rio de Janeiro, Brazil
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7
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Kameda T, Awazu A, Togashi Y. Molecular dynamics analysis of biomolecular systems including nucleic acids. Biophys Physicobiol 2022; 19:e190027. [DOI: 10.2142/biophysico.bppb-v19.0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/18/2022] [Indexed: 12/01/2022] Open
Affiliation(s)
| | - Akinori Awazu
- Graduate School of Integrated Sciences for Life, Hiroshima University
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8
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Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement. Nat Commun 2021; 12:2777. [PMID: 33986288 PMCID: PMC8119458 DOI: 10.1038/s41467-021-23100-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/13/2021] [Indexed: 12/04/2022] Open
Abstract
Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.
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9
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Greenwood T, Heitsch CE. On the Problem of Reconstructing a Mixture of RNA Structures. Bull Math Biol 2020; 82:133. [PMID: 33029669 DOI: 10.1007/s11538-020-00804-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 09/08/2020] [Indexed: 01/02/2023]
Abstract
A growing number of RNA sequences are now known to exist in some distribution with two or more different stable structures. Recent algorithms attempt to reconstruct such mixtures using the list of nucleotides in a sequence in conjunction with auxiliary experimental footprinting data. In this paper, we demonstrate some challenges which remain in addressing this problem; in particular we consider the difficulty of reconstructing a mixture of two RNA structures across a spectrum of different relative abundances. Although progress has been made in identifying the stable structures present, it remains nontrivial to predict the relative abundance of each within the experimentally sampled mixture. Because the ratio of structures present can change depending on experimental conditions, it is the footprinting data-and not the sequence-which must encode information on changes in the relative abundance. Here, we use simulated experimental data to demonstrate that there exist RNA sequences and relative abundance combinations which cannot be recovered by current methods. We then prove that this is not a single exception, but rather part of the rule. In particular, we show, using a Nussinov-Jacobson model, that recovering the relative abundances is difficult for a large proportion of RNA structure pairs. Lastly, we use information theory to establish a framework for quantifying how useful auxiliary data is in predicting the relative abundance of a structure. Together, these results demonstrate that aspects of the problem of reconstructing a mixture of RNA structures from experimental data remain open.
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10
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The assessment of three dimensional modelling design for single strand DNA aptamers for computational chemistry application. Biophys Chem 2020; 267:106492. [PMID: 33035750 DOI: 10.1016/j.bpc.2020.106492] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/17/2020] [Accepted: 09/29/2020] [Indexed: 11/23/2022]
Abstract
Aptamers are oligonucleotides and peptides around 15-100 bases in length and are suitable as detection probes or as therapeutics molecules. There are growing interests in the aptamer screening approach through computational simulation methods. DNA and RNA modelling lacks of validation on their predicted 3D structures due to less number of validation tools, unlike protein structures. We suggest an approach to design the stem-loop/hairpin for the three dimensional structure of DNA aptamers through serial applications of computational prediction methods by comparing the simulated structures with the experimental data deposited in PDB Data bank, followed by MD simulations. The result shows minimal structural differences were observed between the designed and the original NMR aptamers, and the stem-loop conformational structures were also retained during the MD thus suggesting the proposed aptamers designing methods are able to synthesize a high quality molecular structure of hairpin aptamers, comparable to the NMR structures.
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11
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Premkumar KAR, Bharanikumar R, Palaniappan A. Riboflow: Using Deep Learning to Classify Riboswitches With ∼99% Accuracy. Front Bioeng Biotechnol 2020; 8:808. [PMID: 32760712 PMCID: PMC7371854 DOI: 10.3389/fbioe.2020.00808] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 06/23/2020] [Indexed: 01/05/2023] Open
Abstract
Riboswitches are cis-regulatory genetic elements that use an aptamer to control gene expression. Specificity to cognate ligand and diversity of such ligands have expanded the functional repertoire of riboswitches to mediate mounting apt responses to sudden metabolic demands and signal changes in environmental conditions. Given their critical role in microbial life, riboswitch characterisation remains a challenging computational problem. Here we have addressed the issue with advanced deep learning frameworks, namely convolutional neural networks (CNN), and bidirectional recurrent neural networks (RNN) with Long Short-Term Memory (LSTM). Using a comprehensive dataset of 32 ligand classes and a stratified train-validate-test approach, we demonstrated the accurate performance of both the deep learning models (CNN and RNN) relative to conventional hyperparameter-optimized machine learning classifiers on all key performance metrics, including the ROC curve analysis. In particular, the bidirectional LSTM RNN emerged as the best-performing learning method for identifying the ligand-specificity of riboswitches with an accuracy >0.99 and macro-averaged F-score of 0.96. An additional attraction is that the deep learning models do not require prior feature engineering. A dynamic update functionality is built into the models to factor for the constant discovery of new riboswitches, and extend the predictive modeling to new classes. Our work would enable the design of genetic circuits with custom-tuned riboswitch aptamers that would effect precise translational control in synthetic biology. The associated software is available as an open-source Python package and standalone resource for use in genome annotation, synthetic biology, and biotechnology workflows.
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Affiliation(s)
- Keshav Aditya R. Premkumar
- MS Program in Computer Science, Department of Computer Science, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, United States
| | - Ramit Bharanikumar
- MS in Bioinformatics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ashok Palaniappan
- Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, India
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12
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Emami N, Pakchin PS, Ferdousi R. Computational predictive approaches for interaction and structure of aptamers. J Theor Biol 2020; 497:110268. [PMID: 32311376 DOI: 10.1016/j.jtbi.2020.110268] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/27/2020] [Accepted: 04/02/2020] [Indexed: 02/07/2023]
Abstract
Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parvin Samadi Pakchin
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
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13
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Ren H, Shi C, Zhao H. Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways. iScience 2020; 23:100795. [PMID: 31926431 PMCID: PMC6957853 DOI: 10.1016/j.isci.2019.100795] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/24/2019] [Accepted: 12/19/2019] [Indexed: 01/09/2023] Open
Abstract
Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications. Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.
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Affiliation(s)
- Hengqian Ren
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Chengyou Shi
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Departments of Chemistry, Biochemistry, and Bioengineering, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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14
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Sagar A, Xue B. Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions. Protein Pept Lett 2019; 26:601-619. [PMID: 31215361 DOI: 10.2174/0929866526666190619103853] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 04/04/2019] [Accepted: 06/01/2019] [Indexed: 12/18/2022]
Abstract
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.
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Affiliation(s)
- Amit Sagar
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
| | - Bin Xue
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
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15
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Manzourolajdad A, Spouge JL. Structural prediction of RNA switches using conditional base-pair probabilities. PLoS One 2019; 14:e0217625. [PMID: 31188853 PMCID: PMC6561571 DOI: 10.1371/journal.pone.0217625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/15/2019] [Indexed: 11/23/2022] Open
Abstract
An RNA switch triggers biological functions by toggling between two conformations. RNA switches include bacterial riboswitches, where ligand binding can stabilize a bound structure. For RNAs with only one stable structure, structural prediction usually just requires a straightforward free energy minimization, but for an RNA switch, the prediction of a less stable alternative structure is often computationally costly and even problematic. The current sampling-clustering method predicts stable and alternative structures by partitioning structures sampled from the energy landscape into two clusters, but it is very time-consuming. Instead, we predict the alternative structure of an RNA switch from conditional probability calculations within the energy landscape. First, our method excludes base pairs related to the most stable structure in the energy landscape. Then, it detects stable stems (“seeds”) in the remaining landscape. Finally, it folds an alternative structure prediction around a seed. While having comparable riboswitch classification performance, the conditional-probability computations had fewer adjustable parameters, offered greater predictive flexibility, and were more than one thousand times faster than the sampling step alone in sampling-clustering predictions, the competing standard. Overall, the described approach helps traverse thermodynamically improbable energy landscapes to find biologically significant substructures and structures rapidly and effectively.
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Affiliation(s)
- Amirhossein Manzourolajdad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - John L. Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
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Groher AC, Jager S, Schneider C, Groher F, Hamacher K, Suess B. Tuning the Performance of Synthetic Riboswitches using Machine Learning. ACS Synth Biol 2019; 8:34-44. [PMID: 30513199 DOI: 10.1021/acssynbio.8b00207] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Riboswitch development for clinical, technological, and synthetic biology applications constantly seeks to optimize regulatory behavior. Here, we present a machine learning approach to improve the regulation of a tetracycline (tc)-dependent riboswitch device composed of two individual tc aptamers. We developed a bioinformatics model that combines random forest analysis with a convolutional neural network to predict the switching behavior of such tandem riboswitches. We found that both biophysical parameters and the hydrogen bond pattern influence regulation. Our new design pipeline led to significant improvement of the tc riboswitch device with a dynamic range extension from 8.5 to 40-fold. We are confident that our novel method not only results in an excellent tc-dependent riboswitch device but further holds great promise and potential for the optimization of other riboswitches.
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17
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Kumar S, Jain S, Dilbaghi N, Ahluwalia AS, Hassan AA, Kim KH. Advanced Selection Methodologies for DNAzymes in Sensing and Healthcare Applications. Trends Biochem Sci 2018; 44:190-213. [PMID: 30559045 DOI: 10.1016/j.tibs.2018.11.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 11/01/2018] [Accepted: 11/01/2018] [Indexed: 02/07/2023]
Abstract
DNAzymes have been widely explored owing to their excellent catalytic activity in a broad range of applications, notably in sensing and biomedical devices. These newly discovered applications have built high hopes for designing novel catalytic DNAzymes. However, the selection of efficient DNAzymes is a challenging process but one that is of crucial importance. Initially, systemic evolution of ligands by exponential enrichment (SELEX) was a labor-intensive and time-consuming process, but recent advances have accelerated the automated generation of DNAzyme molecules. This review summarizes recent advances in SELEX that improve the affinity and specificity of DNAzymes. The thriving generation of new DNAzymes is expected to open the door to several healthcare applications. Therefore, a significant portion of this review is dedicated to various biological applications of DNAzymes, such as sensing, therapeutics, and nanodevices. In addition, discussion is further extended to the barriers encountered for the real-life application of these DNAzymes to provide a foundation for future research.
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Affiliation(s)
- Sandeep Kumar
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar-Haryana, 125001, India; Department of Civil Engineering, College of Engineering, University of Nebraska at Lincoln, PO Box 886105, Lincoln, NE 68588-6105, USA.
| | - Shikha Jain
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar-Haryana, 125001, India
| | - Neeraj Dilbaghi
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar-Haryana, 125001, India
| | | | - Ashraf Aly Hassan
- Department of Civil Engineering, College of Engineering, University of Nebraska at Lincoln, PO Box 886105, Lincoln, NE 68588-6105, USA
| | - Ki-Hyun Kim
- Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul 04763, Republic of Korea.
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Schroeder SJ. Challenges and approaches to predicting RNA with multiple functional structures. RNA (NEW YORK, N.Y.) 2018; 24:1615-1624. [PMID: 30143552 PMCID: PMC6239171 DOI: 10.1261/rna.067827.118] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The revolution in sequencing technology demands new tools to interpret the genetic code. As in vivo transcriptome-wide chemical probing techniques advance, new challenges emerge in the RNA folding problem. The emphasis on one sequence folding into a single minimum free energy structure is fading as a new focus develops on generating RNA structural ensembles and identifying functional structural features in ensembles. This review describes an efficient combinatorially complete method and three free energy minimization approaches to predicting RNA structures with more than one functional fold, as well as two methods for analysis of a thermodynamics-based Boltzmann ensemble of structures. The review then highlights two examples of viral RNA 3'-UTR regions that fold into more than one conformation and have been characterized by single molecule fluorescence energy resonance transfer or NMR spectroscopy. These examples highlight the different approaches and challenges in predicting structure and function from sequence for RNA with multiple biological roles and folds. More well-defined examples and new metrics for measuring differences in RNA structures will guide future improvements in prediction of RNA structure and function from sequence.
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Affiliation(s)
- Susan J Schroeder
- Department of Chemistry and Biochemistry, Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, USA
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Fukunaga T, Hamada M. Computational approaches for alternative and transient secondary structures of ribonucleic acids. Brief Funct Genomics 2018; 18:182-191. [PMID: 30689706 DOI: 10.1093/bfgp/ely042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Transient and alternative structures of ribonucleic acids (RNAs) play essential roles in various regulatory processes, such as translation regulation in living cells. Because experimental analyses for RNA structures are difficult and time-consuming, computational approaches based on RNA secondary structures are promising. In this article, we review computational methods for detecting and analyzing transient/alternative secondary structures of RNAs, including static approaches based on probabilistic distributions of RNA secondary structures and dynamic approaches such as kinetic folding and folding pathway predictions.
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