Inferring rules of Escherichia coli translational efficiency using an artificial neural network

Koya Mori, Rintaro Saito, Shinichi Kikuchi, Masaru Tomita

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Although the machinery for translation initiation in Escherichia coli is very complicated, the translational efficiency has been reported to be predictable from upstream oligonucleotide sequences. Conventional models have difficulties in their generalization ability and prediction nonlinearity and in their ability to deal with a variety of input attributions. To address these issues, we employed structural learning by artificial neural networks to infer general rules for translational efficiency. The correlation between translational activities measured by biological experiments and those predicted by our method in the test data was significant (r = 0.78), and our method uncovered underlying rules of translational activities and sequence patterns from the obtained skeleton structure. The significant rules for predicting translational efficiency were (1) G- and A-rich oligonucleotide sequences, resembling the Shine-Dalgarno sequence, at positions -10 to -7; (2) first base A in the initiation codon; (3) transport/binding or amino acid metabolism gene function; (4) high binding energy between mRNA and 16S rRNA at positions -15 to -5. An additional inferred novel rule was that C at position -1 increases translational efficiency. When our model was applied to the entire genomic sequence of E. coli, translational activities of genes for metabolism and translational were significantly high.

Original languageEnglish
Pages (from-to)414-420
Number of pages7
JournalBioSystems
Volume90
Issue number2
DOIs
Publication statusPublished - 2007 Sep

Fingerprint

oligonucleotides
artificial neural network
neural networks
Escherichia coli
Escherichia Coli
Artificial Neural Network
metabolism
Neural networks
Efficiency
start codon
Aptitude
gene
Oligonucleotides
amino acid metabolism
Metabolism
machinery
nonlinearity
skeleton
translation (genetics)
genomics

Keywords

  • Neural network
  • Prediction
  • Shine-Dalgarno sequence
  • Structural learning method
  • Translation efficiency

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Biotechnology
  • Drug Discovery

Cite this

Inferring rules of Escherichia coli translational efficiency using an artificial neural network. / Mori, Koya; Saito, Rintaro; Kikuchi, Shinichi; Tomita, Masaru.

In: BioSystems, Vol. 90, No. 2, 09.2007, p. 414-420.

Research output: Contribution to journalArticle

Mori, Koya ; Saito, Rintaro ; Kikuchi, Shinichi ; Tomita, Masaru. / Inferring rules of Escherichia coli translational efficiency using an artificial neural network. In: BioSystems. 2007 ; Vol. 90, No. 2. pp. 414-420.
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