Paused in translation: A model for the transcript length-dependent impact of ribosome-targeting antibiotics

TL;DR

This paper develops a length-dependent model of ribosome traffic affected by antibiotics, revealing how transcript length and initiation rates influence translation inhibition, with implications for biological transport processes.

Contribution

It introduces a biologically constrained TASEP model with stochastic pausing, providing analytical insights into antibiotic effects on translation based on transcript length.

Findings

Longer transcripts are more affected by antibiotics.

Reducing initiation rates lessens antibiotic impact.

Translation inhibition is driven by collective ribosome dynamics.

Abstract

Ribosome-targeting antibiotics, such as chloramphenicol, stall elongating ribosomes during protein synthesis, disrupting mRNA translation. These antibiotic-induced pauses occur stochastically, alter collective ribosome dynamics and transiently block protein production on the affected transcript. Existing models of ribosome traffic often rely on idealized assumptions, such as infinitely long mRNAs and simplified pausing dynamics, overlooking key biological constraints. Here, we develop a Totally Asymmetric Simple Exclusion Process (TASEP) that incorporates stochastic particle pausing, using experimentally determined pausing and unpausing rates to model the effects of ribosome-targeting antibiotics. We introduce a Single-Cluster approximation, which is analytically treatable, tailored to capture the biologically relevant regime of rare and long antibiotic-induced pauses. This biologically constrained model reveals three key insights: (i) the inhibition of antibiotic-induced translation strongly depends on transcript length, with longer transcripts being disproportionately affected; (ii) reducing ribosome initiation rates significantly mitigates antibiotic vulnerability; and (iii) inhibition of translation is governed more by collective ribosome dynamics than by single-ribosome properties. Our analytical predictions match Gillespie simulations, align quantitatively with experimental observations, and yield testable hypotheses for future experiments. These findings may have broader implications for the mechanistic modeling of other biological transport processes (e.g., RNAP dynamics), and more generally for the community studying traffic models.