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The efficiency of translation termination is determined by the nature of the stop codon as well as its context. In eukaryotes, recognition of the A-site stop codon and release of the polypeptide are mediated by release factors eRF1 and eRF3, respectively. Translation termination is modulated by other factors which either directly interact with release factors or bind to the E-site and modulate the activity of the peptidyl transferase center. Previous studies suggested that the Saccharomyces cerevisiae ABCF ATPase New1 is involved in translation termination and/or ribosome recycling, however, the exact function remained unclear. Here, we have applied 5PSeq, single-particle cryo-EM and readthrough reporter assays to provide insight into the biological function of New1. We show that the lack of New1 results in ribosomal stalling at stop codons preceded by a lysine or arginine codon and that the stalling is not defined by the nature of the C-terminal amino acid but rather by the identity of the tRNA isoacceptor in the P-site. Collectively, our results suggest that translation termination is inefficient when ribosomes have specific tRNA isoacceptors in the P-site and that the recruitment of New1 rescues ribosomes at these problematic termination contexts., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The efficiency of translation termination is determined by the nature of the stop codon as well as its context. In eukaryotes, recognition of the A-site stop codon and release of the polypeptide are mediated by release factors eRF1 and eRF3, respectively. Translation termination is modulated by other factors which either directly interact with release factors or bind to the E-site and modulate the activity of the peptidyl transferase center. Previous studies suggested that the Saccharomyces cerevisiae ABCF ATPase New1 is involved in translation termination and/or ribosome recycling, however, the exact function remained unclear. Here, we have applied 5PSeq, single-particle cryo-EM and readthrough reporter assays to provide insight into the biological function of New1. We show that the lack of New1 results in ribosomal stalling at stop codons preceded by a lysine or arginine codon and that the stalling is not defined by the nature of the C-terminal amino acid but rather by the identity of the tRNA isoacceptor in the P-site. Collectively, our results suggest that translation termination is inefficient when ribosomes have specific tRNA isoacceptors in the P-site and that the recruitment of New1 rescues ribosomes at these problematic termination contexts., Competing Interests: Conflict of interest statement. V.P. and L.N. are co-founders and shareholders of 3N Bio AB. All other authors declare no competing interests.

During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically. Here, we offer a computational protocol for analyzing positional distribution profiles using ribosome profiling (Ribo-Seq) data. The protocol uses papolarity, a new Python toolkit for the analysis of transcript-level short read coverage profiles. For a single sample, for each transcript papolarity allows for computing the classic polarity metric which, in the case of Ribo-Seq, reflects ribosome positional preferences. For comparison versus a control sample, papolarity estimates an improved metric, the relative linear regression slope of coverage along transcript length. This involves de-noising by profile segmentation with a Poisson model and aggregation of Ribo-Seq coverage within segments, thus achieving reliable estimates of the regression slope. The papolarity software and the associated protocol can be conveniently used for Ribo-Seq data analysis in the command-line Linux environment. Papolarity package is available through Python pip package manager. The source code is available at https://github.com/autosome-ru/papolarity .