Conditional deep learning model reveals translation elongation determinants during amino acid deprivation.

ABSTRACT: Translation elongation plays a key role in cellular homeostasis, and dysregulation of this process has been implicated in various diseases and metabolic disorders. Uncovering the causes of intragenic heterogeneity of translation, especially in contexts of different amino acid deprivations, could help increase our understanding of these disorders and pave the way for novel therapeutics. Ribosome profiling provides accurate measurements for the genome-wide ribosome footprints, which could be utilized to investigate these mechanisms. Here we present Riboclette, a conditional deep learning model featuring a dual output head that uses the mRNA sequence input to accurately predict the ribosome footprint profiles across six amino acid deprivation conditions. Exploiting standard interpretability m

SUBMITTER: Nallapareddy MV 

PROVIDER: S-EPMC12657957 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature