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Updated: May 21, 2026

Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling
Published on: October 7, 2021
A computational investigation on the connection between dynamics properties of ribosomal proteins and ribosome
Brittany Burton1, Michael T Zimmermann, Robert L Jernigan
1Department of Chemistry, The University of Memphis, Memphis, Tennessee, United States of America.
Abstract:
Assembly of the ribosome from its protein and RNA constituents has been studied extensively over the past 50 years, and experimental evidence suggests that prokaryotic ribosomal proteins undergo conformational changes during assembly. However, to date, no studies have attempted to elucidate these conformational changes. The present work utilizes computational methods to analyze protein dynamics and to investigate the linkage between dynamics and binding of these proteins during the assembly of the ribosome. Ribosomal proteins are known to be positively charged and we find the percentage of positive residues in r-proteins to be about twice that of the average protein: Lys+Arg is 18.7% for E. coli and 21.2% for T. thermophilus. Also, positive residues constitute a large proportion of RNA contacting residues: 39% for E. coli and 46% for T. thermophilus. This affirms the known importance of charge-charge interactions in the assembly of the ribosome. We studied the dynamics of three primary proteins from E. coli and T. thermophilus 30S subunits that bind early in the assembly (S15, S17, and S20) with atomic molecular dynamic simulations, followed by a study of all r-proteins using elastic network models. Molecular dynamics simulations show that solvent-exposed proteins (S15 and S17) tend to adopt more stable solution conformations than an RNA-embedded protein (S20). We also find protein residues that contact the 16S rRNA are generally more mobile in comparison with the other residues. This is because there is a larger proportion of contacting residues located in flexible loop regions. By the use of elastic network models, which are computationally more efficient, we show that this trend holds for most of the 30S r-proteins.
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