Blind prediction of noncanonical RNA structure at atomic accuracy

Mapping Intimacies ◽

10.1101/223305 ◽

2017 ◽

Cited By ~ 1

Author(s):

Andrew Watkins ◽

Caleb Geniesse ◽

Wipapat Kladwang ◽

Paul Zakrevsky ◽

Luc Jaeger ◽

...

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

Grand Challenge ◽

Chemical Mapping ◽

Base Pairs ◽

General Ability ◽

Rna Structural Motifs ◽

Nucleotide Resolution ◽

Noncanonical Base Pairs

AbstractPrediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report herein a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method’s general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

SHAPE directed RNA folding

10.1101/015537 ◽

2015 ◽

Author(s):

Dominik Luntzer ◽

Ronny Lorenz ◽

Ivo L Hofacker ◽

Peter F Stadler ◽

Michael T. Wolfinger

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

Rna Folding ◽

Secondary Structure Prediction ◽

Reference Structure ◽

Chemical Mapping ◽

Rna Secondary Structure Prediction ◽

New Feature ◽

Nucleotide Resolution ◽

Standard Energy

Summary: Chemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics- based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary structures. As a showcase application for this new feature of the ViennaRNA Package we compare three distinct, previously published strategies to utilize SHAPE reactivities for structure prediction. The new tool is benchmarked on a set of RNAs with known reference structure. Availability and implementation: The capability for SHAPE directed RNA folding is part of the upcoming release of the ViennaRNA Package 2.2, for which a preliminary release is already freely available at http://www.tbi.univie.ac.at/RNA.

A method for RNA structure prediction shows evidence for structure in lncRNAs

10.1101/284869 ◽

2018 ◽

Author(s):

Riccardo Delli ponti ◽

Alexandros Armaos ◽

Stefanie Marti ◽

Gian Gaetano Tartaglia

Keyword(s):

Secondary Structure ◽

Rna Structure ◽

Structure Prediction ◽

Sequence Information ◽

Time Warping ◽

Single Nucleotide ◽

Rna Molecules ◽

Rna Structure Prediction ◽

Dynamic Time ◽

Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

Traditional Chemical Mapping of RNA Structure In Vitro and In Vivo

RNA Structure Determination - Methods in Molecular Biology ◽

10.1007/978-1-4939-6433-8_7 ◽

2016 ◽

pp. 83-103 ◽

Cited By ~ 1

Author(s):

Pierre Fechter ◽

Delphine Parmentier ◽

ZongFu Wu ◽

Olivier Fuchsbauer ◽

Pascale Romby ◽

...

Keyword(s):

Rna Structure ◽

Chemical Mapping

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

Nature Communications ◽

10.1038/s41467-019-13395-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 26

Author(s):

Jaswinder Singh ◽

Jack Hanson ◽

Kuldip Paliwal ◽

Yaoqi Zhou

Keyword(s):

Secondary Structure ◽

Transfer Learning ◽

Rna Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Noncoding Rnas ◽

Rna Structures ◽

Base Pairs ◽

Functional Annotations ◽

Model Training

AbstractThe majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only $$<$$<250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of $$> $$>10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

10.1101/2020.08.31.274761 ◽

2020 ◽

Author(s):

Tycho Marinus ◽

Adam B. Fessler ◽

Craig A. Ogle ◽

Danny Incarnato

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

Critical Role ◽

Living Cells ◽

Pathological Process ◽

Rna Structures ◽

Rna Molecules ◽

Derived Data

ABSTRACTDue to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art SHAPE reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

Nucleic Acids Research ◽

10.1093/nar/gkaa1255 ◽

2021 ◽

Author(s):

Tycho Marinus ◽

Adam B Fessler ◽

Craig A Ogle ◽

Danny Incarnato

Keyword(s):

Rna Structure ◽

Structure Prediction ◽

Critical Role ◽

Living Cells ◽

Pathological Process ◽

Rna Structures ◽

Rna Molecules ◽

Derived Data

Abstract Due to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

RNA structure inference through chemical mapping after accidental or intentional mutations

10.1101/169953 ◽

2017 ◽

Author(s):

Clarence Y. Cheng ◽

Wipapat Kladwang ◽

Joseph Yesselman ◽

Rhiju Das

Keyword(s):

False Positive ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Dimethyl Sulfate ◽

Chemical Mapping ◽

Rna Structures ◽

Group I ◽

Egg Extract ◽

Special Cases

ABSTRACTDespite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical mapping method called M2-seq (mutate-and-map read out through next-generation sequencing) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a new neural-net-inspired algorithm called M2-net. Measurements on the P4-P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.SIGNIFICANCE STATEMENTThe intricate structures of RNA molecules are crucial to their biological functions but have been difficult to accurately characterize. Multidimensional chemical mapping methods improve accuracy but have so far involved painstaking experiments and reliance on secondary structure prediction software. A methodology called M2-seq now lifts these limitations. Mechanistic studies clarify the origin of serendipitous M2-seq-like signals that were recently discovered but not correctly explained and also provide mutational strategies that enable robust M2-seq for new RNA transcripts. The method detects dozens of Watson-Crick helices across diverse RNA folds in vitro and within frog egg extract, with low false positive rate (< 5%). M2-seq opens a route to unbiased discovery of RNA structures in vitro and beyond.

RNA structure through multidimensional chemical mapping

10.1101/038679 ◽

2016 ◽

Author(s):

Siqi Tian ◽

Rhiju Das

Keyword(s):

Structure Determination ◽

Rna Structure ◽

De Novo ◽

Compensatory Mutation ◽

Chemical Mapping ◽

Rna Molecules ◽

Viral Genomes ◽

Rna Interaction

The discoveries of myriad non-coding RNA molecules, each transiting through multiple flexible states in cells or virions, present major challenges for structure determination. Advances in high-throughput chemical mapping give new routes for characterizing entire transcriptomes in vivo, but the resulting one-dimensional data generally remain too information-poor to allow accurate de novo structure determination. Multidimensional chemical mapping (MCM) methods seek to address this challenge. Mutate-and-map (M2), RNA interaction groups by mutational profiling (RING-MaP and MaP-2D analysis) and multiplexed .OH cleavage analysis (MOHCA) measure how the chemical reactivities of every nucleotide in an RNA molecule change in response to modifications at every other nucleotide. A growing body of in vitro blind tests and compensatory mutation/rescue experiments indicate that MCM methods give consistently accurate secondary structures and global tertiary structures for ribozymes, ribosomal domains and ligand-bound riboswitch aptamers up to two hundred nucleotides in length. Importantly, MCM analyses provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches, that are functionally important but difficult to resolve with other approaches. The sequencing requirements of currently available MCM protocols scale at least quadratically with RNA length, precluding general application to transcriptomes or viral genomes at present. We propose a modify-crosslink-map expansion to overcome this and other current limitations to resolving the in vivo "RNA structurome".

RNA structure inference through chemical mapping after accidental or intentional mutations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1619897114 ◽

2017 ◽

Vol 114 (37) ◽

pp. 9876-9881 ◽

Cited By ~ 27

Author(s):

Clarence Y. Cheng ◽

Wipapat Kladwang ◽

Joseph D. Yesselman ◽

Rhiju Das

Keyword(s):

Rna Structure ◽

Dimethyl Sulfate ◽

Mapping Method ◽

Chemical Mapping ◽

Group I ◽

Egg Extract ◽

Special Cases ◽

Minimization Algorithms ◽

Rna Domains

Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called “mutate-and-map read out through next-generation sequencing” (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network–inspired algorithm called M2-net. Measurements on the P4–P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.

RNA structure through multidimensional chemical mapping

Quarterly Reviews of Biophysics ◽

10.1017/s0033583516000020 ◽

2016 ◽

Vol 49 ◽

Cited By ~ 29

Author(s):

Siqi Tian ◽

Rhiju Das

Keyword(s):

Structure Determination ◽

Rna Structure ◽

De Novo ◽

Compensatory Mutation ◽

Chemical Mapping ◽

Rna Molecules ◽

Viral Genomes ◽

Rna Interaction

AbstractThe discoveries of myriad non-coding RNA molecules, each transiting through multiple flexible states in cells or virions, present major challenges for structure determination. Advances in high-throughput chemical mapping give new routes for characterizing entire transcriptomesin vivo, but the resulting one-dimensional data generally remain too information-poor to allow accuratede novostructure determination. Multidimensional chemical mapping (MCM) methods seek to address this challenge. Mutate-and-map (M2), RNA interaction groups by mutational profiling (RING-MaP and MaP-2D analysis) and multiplexed •OH cleavage analysis (MOHCA) measure how the chemical reactivities of every nucleotide in an RNA molecule change in response to modifications at every other nucleotide. A growing body ofin vitroblind tests and compensatory mutation/rescue experiments indicate that MCM methods give consistently accurate secondary structures and global tertiary structures for ribozymes, ribosomal domains and ligand-bound riboswitch aptamers up to 200 nucleotides in length. Importantly, MCM analyses provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches that are functionally important but difficult to resolve with other approaches. The sequencing requirements of currently available MCM protocols scale at least quadratically with RNA length, precluding general application to transcriptomes or viral genomes at present. We propose a modify-cross-link-map (MXM) expansion to overcome this and other current limitations to resolving thein vivo ‘RNA structurome’.