Research output: Contribution to journal › Article › peer-review
Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes. / Korenskaia, Aleksandra E.; Matushkin, Yury G.; Lashin, Sergey A. et al.
In: International Journal of Molecular Sciences, Vol. 23, No. 19, 11996, 10.2022.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes
AU - Korenskaia, Aleksandra E.
AU - Matushkin, Yury G.
AU - Lashin, Sergey A.
AU - Klimenko, Alexandra I.
N1 - Funding Information: This research was funded by the Kurchatov Genomic Centre of the Institute of Cytology and Genetics, SB RAS (№ 075-15-2019-1662). The data analysis was performed using computational resources of the “Bioinformatics” Joint Computational Center supported by the Ministry of Science and Higher Education budget project № FWNR-2022-0020. Publisher Copyright: © 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type—the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity—bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.
AB - Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type—the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity—bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.
KW - protein abundance prediction
KW - translation elongation efficiency
KW - translation in prokaryotes
KW - Protein Biosynthesis
KW - Codon/genetics
KW - Computational Biology
KW - Ribosomal Proteins/metabolism
KW - RNA, Messenger/metabolism
UR - http://www.scopus.com/inward/record.url?scp=85139960994&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/ddc82ea4-2146-3648-a01f-2eec0e6051d5/
U2 - 10.3390/ijms231911996
DO - 10.3390/ijms231911996
M3 - Article
C2 - 36233299
AN - SCOPUS:85139960994
VL - 23
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
SN - 1661-6596
IS - 19
M1 - 11996
ER -
ID: 38182844