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PreprintCited by 8RNA and protein synthesis mechanismsRNA Research and SplicingProtein Structure and Dynamics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Deciphering the rules of mRNA structure differentiation in vivo and in vitro with deep neural networks in Saccharomyces cerevisiae

Open access
Yu, Haopeng, Meng, Wenjing, Mao, Yuanhui, Zhang, Yi, Sun, Qing, Tao, Shiheng·2018·RNA Biology
DOI 10.1101/337733PMC PMC6602416

Preprint — published as 10.1080/15476286.2019.1612692

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Abstract

The structure of mRNA in vivo is influenced by various factors involved in the translation process, resulting in significant differentiation of mRNA structure from that in vitro . Because multiple factors cause the differentiation of in vivo and in vitro mRNA structures, it was difficult to perform a more accurate analysis of mRNA structures in previous studies. In this study, we have proposed a novel application of a deep neural network (DNN) model to predict the structural stability of mRNA in vivo by fitting six quantifiable features that may affect mRNA folding: ribosome density, minimum folding free energy, GC content, mRNA abundance, ribosomal initial density and position of mRNA structure. Simulated mutations of the mRNA structure were designed and then fed into the trained DNN model to compute their structural stability. We found unique effects of these six features on mRNA structural stability in vivo . Strikingly, the ribosome density of the structural region is the most important factor affecting the structural stability of mRNA in vivo , and the strength of the mRNA structure in vitro should have a relatively small effect on its structural stability in vivo . The recruitment of DNNs provides a new paradigm to decipher the differentiation of mRNA structure in vivo and in vitro . This improved knowledge on the mechanisms of factors influencing mRNA structural stability will facilitate the design and functional analysis of mRNA structure in vivo .

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Metadata

Authors
Yu, H., Meng, W., Mao, Y., Zhang, Y., Sun, Q., & Tao, S.
Affiliations
Northwest A&F University (CN)
Corresponding author
Tao, Shiheng
ORCID
Venue
RNA Biology
Publisher
openRxiv
Published
2018-06-03
Volume
16
Issue
8
Pages
1044-1054
ISSN
1547-6286 (ISSN-L)
DOI
10.1101/337733
PMC
PMC6602416
OpenAlex
W2946341725
Cited by
8
References
1
Citations by year
2024: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 3
FWCI
0.58
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • RNA Biology (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Francis Crick Institute
  • National Natural Science Foundation of China (3177080732)
  • Key Programme (3177080732)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
ribosome density, GC content, deep neural network inference, Saccharomyces cerevisiae
MeSH terms
Nucleic Acid Conformation, RNA, Messenger, Saccharomyces cerevisiae, Protein Biosynthesis, Neural Networks, Computer

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