Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Leonardis, Eleonora De; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rémi; Schug, Alexander; Weigt, Martin

doi:10.1093/nar/gkv932

Cited by 111 publications

(184 citation statements)

References 56 publications

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“…RNAsnap-prof is used to illustrate if sequence conservation information contained in multiple sequence alignment is useful for more accurate determination of tertiary structural properties. A significant increase in the PCC values between predicted and actual ASAs from 0.54 to 0.63 by using the sequence profile confirmed that an RNA base can be conserved for its tertiary structural role (Capriotti and Marti-Renom 2010;De Leonardis et al 2015). Sequence conservation in RNA secondary structure (Gruber et al 2008;Gu et al 2014) has also been studied and used for secondary structure prediction (Rivas 2013;Sharma et al 2014;De Leonardis et al 2015).…”

Section: Discussionmentioning

confidence: 75%

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Yang

Zhao

et al. 2016

RNA

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As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5 ′ cap of 5 ′ and 3 ′ cap of 3 ′ untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org.

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Section: Discussionmentioning

confidence: 75%

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Yang

Zhao

et al. 2016

RNA

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“…The Direct-Coupling Analysis (DCA) has been proposed to disentangle such indirect effects from direct (i.e. epistatic) couplings [10] , allowing prediction of native contacts of protein structures, protein-protein interactions and RNA structures, [11] and the likelihood model underlying them has also been shown to be correlated with protein stability [12] . Indeed, this type of analysis has been used to help design targeted single-point sequence variants [13] .…”

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confidence: 99%

Co‐Evolutionary Fitness Landscapes for Sequence Design

et al. 2018

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Efficient and accurate models to predict the fitness of a sequence would be extremely valuable in protein design. We have explored the use of statistical potentials for the coevolutionary fitness landscape, extracted from known protein sequences, in conjunction with Monte Carlo simulations, as a tool for design. As proof of principle, we created a series of predicted high-fitness sequences for three different protein folds, representative of different structural classes: the GA (all-α) and GB (α/β) binding domains of streptococcal protein G, and an SH3 (all-β) domain. We found that most of the designed proteins can fold stably to the target structure, and a structure for a representative of each for GA, GB and SH3 was determined. Several of our designed proteins were also able to bind to native ligands, in some cases with higher affinity than wild-type. Thus, a search using a statistical fitness landscape is a remarkably effective tool for finding novel stable protein sequences.

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“…In contrast, our novel rnaDCA is able to extract tertiary contacts from genomic data. We further demonstrate that these tertiary contacts are sufficient to systematically improve tertiary RNA prediction quality [4]. Considering the large gap of known ncRNA sequences to experimentally resolved tertiary structures, we are convinced that this will significantly impact all structural RNA related research.…”

Section: -Platmentioning

confidence: 63%

“…

methylammonium, acetate and water [4]. With the removal of the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor fragment [2], improved screening results are seen using targets in the DUD database in the extended SILCS-Pharm method.

…”

mentioning

confidence: 99%

Protein and RNA Structure Prediction by Integration of Co-Evolutionary Information into Molecular Simulation

Leonardis¹,

Lutz²,

Monasson³

et al. 2015

Biophysical Journal

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Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Cited by 111 publications

References 56 publications

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction

Co‐Evolutionary Fitness Landscapes for Sequence Design

Protein and RNA Structure Prediction by Integration of Co-Evolutionary Information into Molecular Simulation

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