Nopp140-chaperoned 2’-O-methylation of small nuclear RNAs in Cajal bodies ensures splicing fidelity

Bizarro, Jonathan; Deryusheva, Svetlana; Wacheul, Ludivine; Gupta, Varun; Ernst, Felix G.M.; Lafontaine, Denis L. J.; Gall, Joseph G.; Meier, U. Thomas

doi:10.1101/2021.04.29.441821

Cited by 2 publications

(1 citation statement)

References 89 publications

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“…Yet Nopp140 is essential for 2'-O-methylation but not for pseudouridylation of spliceosomal snRNAs. The depletion of Nopp140 from HeLa cells causes dramatic reduction of 2'-O-methylation at all positions except for two in polymerase 2transcribed snRNAs, but affects a very limited number of positions in rRNAs (Bizarro et al 2021). Another RNA-binding protein, TDP-43, also contributes to 2'-O-methylation of U1 and U2 snRNAs.…”

Section: Another Layer Of Complexity In Higher Eukaryotesmentioning

confidence: 99%

SnoRNA guide activities – real and ambiguous

Deryusheva

Talross

Gall

2021

Preprint

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In eukaryotes, rRNAs and spliceosomal snRNAs are heavily modified posttranscriptionally. Pseudouridylation and 2′-O-methylation are the most abundant types of RNA modifications. They are mediated by modification guide RNAs, also known as small nucleolar (sno)RNAs and small Cajal body-specific (sca)RNAs. We used yeast and vertebrate cells to test guide activities predicted for a number of snoRNAs, based on their regions of complementarity with rRNAs. We showed that human SNORA24 is a genuine guide RNA for 18S-ψ609, despite some non-canonical base-pairing with its target. At the same time, we found quite a few snoRNAs that have the ability to base-pair with rRNAs and can induce predicted modifications in artificial substrate RNAs, but do not modify the same target sequence within endogenous rRNA molecules. Furthermore, certain fragments of rRNAs can be modified by the endogenous yeast modification machinery when inserted into an artificial backbone RNA, even though the same sequences are not modified in endogenous yeast rRNAs. In Xenopus cells a guide RNA generated from scaRNA, but not from snoRNA, could induce an additional pseudouridylation of U2 snRNA at position 60; both guide RNAs were equally active on a U2 snRNA-specific substrate in yeast cells. Thus, posttranscriptional modification of functionally important RNAs, such as rRNAs and snRNAs, is highly regulated and more complex than simply strong base-pairing between a guide RNA and substrate RNA. We discuss possible regulatory roles for these unexpected modifications.

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Section: Another Layer Of Complexity In Higher Eukaryotesmentioning

confidence: 99%

SnoRNA guide activities – real and ambiguous

Deryusheva

Talross

Gall

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

Kang

Xiong

et al. 2023

J Cheminform

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Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

show abstract

Nopp140-chaperoned 2’-O-methylation of small nuclear RNAs in Cajal bodies ensures splicing fidelity

Cited by 2 publications

References 89 publications

SnoRNA guide activities – real and ambiguous

SnoRNA guide activities – real and ambiguous

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

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