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 -