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Updated: May 12, 2026
09:37
An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrrolysine
Christian Theil Have1, Sine Zambach, Henning Christiansen
1Research group PLIS: Programming, Logic and Intelligent Systems, Department of Communication, Business and Information Technologies, Roskilde University, P.O. Box 260, Roskilde, DK-4000, Denmark. cth@ruc.dk
BMC Bioinformatics
|April 6, 2013
Summary
We developed a computational method to predict genes encoding pyrrolysine (the 22nd amino acid) in bacteria and archaea. This strategy identifies new gene candidates and provides insights into pyrrolysine translation regulation.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Pyrrolysine, the 22nd amino acid, is encoded by the amber stop codon (UAG) in specific organisms.
- The precise conditions that trigger pyrrolysine translation remain incompletely understood.
- While a downstream mRNA structure was hypothesized to be involved, its presence is not universal across pyrrolysine-incorporating genes.
Purpose of the Study:
- To develop a predictive strategy for identifying pyrrolysine-encoding genes within archaeal and bacterial genomes.
- To elucidate the factors influencing pyrrolysine translation.
- To discover novel pyrrolysine gene candidates for experimental validation.
Main Methods:
- Clustering of open reading frames interrupted by the amber codon based on sequence similarity.
- Ranking of gene clusters using features relevant to pyrrolysine translation.
- Assessment of feature effects, including structural conservation, and development of a weighted combination for prediction.
Main Results:
- A weighted combination of features was determined to best explain known pyrrolysine-incorporating genes.
- Structural conservation was found to be influential but not a mandatory factor for pyrrolysine incorporation.
- Several potentially pyrrolysine-encoding genes were identified through the weighted ranking.
Conclusions:
- A computational method for predicting pyrrolysine-incorporating genes in bacteria and archaea has been proposed.
- The method offers insights into the regulatory mechanisms of pyrrolysine translation.
- The pipeline successfully predicted known genes with high recall and identified promising new candidates.
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