To understand the complex relationship governing transcript abundance and the level of the encoded protein, we integrate genome-wide experimental data of ribosomal density on mRNAs with a novel stochastic model describing ribosome traffic dynamics during translation elongation. This analysis reveals that codon arrangement, rather than simply codon bias, has a key role in determining translational efficiency. It also reveals that translation output is governed both by initiation efficiency and elongation dynamics. By integrating genome-wide experimental data sets with simulation of ribosome traffic on all Saccharomyces cerevisiae ORFs, mRNA-specific translation initiation rates are for the first time estimated across the entire transcriptome. Our analysis identifies different classes of mRNAs characterised by their initiation rates, their ribosome traffic dynamics, and by their response to ribosome availability. Strikingly, this classification based on translational dynamics maps onto key gene ontological classifications, revealing evolutionary optimisation of translation responses to be strongly influenced by gene function.

Membraneless organelles are dynamical cellular condensates formed via biomolecular liquid–liquid phase separation of proteins and RNA molecules. Multiple evidence suggests that in several cases disordered proteins are structural scaffolds that drive the condensation by forming a dynamic network of inter- and intramolecular contacts. Despite the blooming research activity in this field, the structural characterization of these entities is very limited, and we still do not understand how the phase behavior is encoded in the amino acid sequences of the scaffolding proteins. Here we exploited explicit-solvent atomistic simulations to investigate the N-terminal disordered region of DEAD-box helicase 4 (NDDX4), which is a well-established model for phase separation. Notably, we determined NDDX4 conformational ensemble at the single-molecule level, and we relied on a “divide-and-conquer” strategy, based on simulations of various protein fragments at high concentration, to probe intermolecular interactions in conditions mimicking real condensates. Our results provide a high-resolution picture of the molecular mechanisms underlying phase separation in agreement with NMR and mutagenesis data and suggest that clusters of arginine and aromatic residues may stabilize the assembly of several condensates.