The tRNA adaptation index (tAI) is a widely used measure of the efficiency by which a coding sequence is recognized by the intra-cellular tRNA pool. This index includes among others weights that represent wobble interactions between codons and tRNA molecules. Currently, these weights are based only on the gene expression in Saccharomyces cerevisiae. However, the efficiencies of the different codon–tRNA interactions are expected to vary among different organisms. In this study, we suggest a new approach for adjusting the tAI weights to any target model organism without the need for gene expression measurements. Our method is based on optimizing the correlation between the tAI and a measure of codon usage bias. Here, we show that in non-fungal the new tAI weights predict protein abundance significantly better than the traditional tAI weights. The unique tRNA–codon adaptation weights computed for 100 different organisms exhibit a significant correlation with evolutionary distance. The reported results demonstrate the usefulness of the new measure in future genomic studies.

BackgroundDuring protein synthesis, the nascent peptide chain emerges from the ribosome through the ribosomal exit tunnel. Biochemical interactions between the nascent peptide and the tunnel may stall the ribosome movement and thus affect the expression level of the protein being synthesized. Earlier studies focused on one model organism (S. cerevisiae), have suggested that certain amino acid sequences may be responsible for ribosome stalling; however, the stalling effect at the individual amino acid level across many organisms has not yet been quantified.ResultsBy analyzing multiple ribosome profiling datasets from different organisms (including prokaryotes and eukaryotes), we report for the first time the organism-specific amino acids that significantly lead to ribosome stalling. We show that the identity of the stalling amino acids vary across the tree of life. In agreement with previous studies, we observed a remarkable stalling signal of proline and arginine in S. cerevisiae. In addition, our analysis supports the conjecture that the stalling effect of positively charged amino acids is not universal and that in certain conditions, negative charge may also induce ribosome stalling. Finally, we show that the beginning part of the tunnel tends to undergo more interactions with the translated amino acids than other positions along the tunnel.ConclusionsThe reported results support the conjecture that the ribosomal exit tunnel interacts with various amino acids and that the nature of these interactions varies among different organisms. Our findings should contribute towards better understanding of transcript and proteomic evolution and translation elongation regulation.

tamirtul@post.tau.ac.il.

Interactions between the ribosomal exit tunnel and the nascent peptide can affect translation elongation rates. While previous studies have already demonstrated the feasibility of such interactions, little is known about the nature of the stalling peptide sequences and their distribution in the proteome. Here we ask which peptide sequences tend to occupy the tunnel of stalled ribosomes and how they are distributed in the proteome. Using computational analysis of ribosome profiling data from we identified for the first time dozens of short stalling peptide sequences and studied their statistical properties. We found that short peptide sequences associated with ribosome stalling tend significantly to be either over- or underrepresented in the proteome. We then showed that the stalling interactions may occur at different positions along the length of the tunnel, prominently close to the P-site. Our findings throw light on the determinants of nascent peptide-mediated ribosome stalling during translation elongation and support the novel conjecture that mRNA translation affects the proteomic distribution of short peptide sequences.

Dissecting the competition between genes for shared expressional resources is of fundamental importance for understanding the interplay between cellular components. Owing to the relationship between gene expression and cellular fitness, genomes are shaped by evolution to improve resource allocation. Whereas experimental approaches to investigate intracellular competition require technical resources and human expertise, computational models and in silico simulations allow vast numbers of experiments to be carried out and controlled easily, and with significantly reduced costs. Thus, modelling competition has a pivotal role in understanding the effects of competition on the biophysics of the cell. In this article, we review various computational models proposed to describe the different types of competition during gene expression. We also present relevant synthetic biology experiments and their biotechnological implications, and discuss the open questions in the field.