Ranff, T. | bioRxiv Science

bioRxiv2024

Codon Pair-Specific Translation Defects Trigger Ribosome-Associated Quality Control to Avoid Proteotoxic Stress

tRNA modifications tune translation rates and codon optimality, thereby optimizing co-translational protein folding, but how codon optimality defects trigger cellular phenotypes remains unclear. Here, we show that ribosomes stall at specific modification-dependent codon pairs, triggering ribosome collisions and inducing a coordinated and hierarchical response of cellular quality control pathways. Ribosome profiling reveals an unexpected functional diversity for wobble-uridine (U34) modifications during decoding. The same modification can have different effects at the A and P sites. Furthermore, modification-dependent stalling codon pairs induce ribosome collisions, triggering ribosome-associated quality control (RQC) to prevent protein aggregation by degrading aberrant nascent peptides and mRNAs. RQC inactivation stimulates the expression of molecular chaperones to remove protein aggregates. Our results show that loss of tRNA modifications primarily disrupts translation rates of suboptimal codon pairs and reveal the coordinated regulation and adaptability of cellular surveillance systems to ensure efficient and accurate protein synthesis and maintain protein homeostasis.

biochemistry↗

bioRxiv2022

PeptideForest: Semisupervised machine learning integrating multiple search engines for peptide identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights, unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semi-supervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to elevate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 {+/-} 1.6% compared to MS-GF+ data on samples containing mixed HEK and E. coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

bioinformatics↗