Physiological mapping and prediction reveal stress-responsive translation initiation landscapes

Abstract

Translation initiation defines the protein output of the transcriptome, but its physiological dynamics in cells and tissues remain incompletely characterized. Here we develop TISCA2, an inhibitor-free method that captures eIF5B-associated initiating ribosomes to map translation initiation sites genome-wide. By directly identifying start sites rather than inferring translation from elongating-ribosome periodicity, TISCA2 improves the detection of short and overlapping translons. Translation of a subset of TISCA2-identified translons is supported by N-terminal proteomics and HLA-I immunopeptidomics. The arsenite-induced integrated stress response broadly redistributes start-site usage, reducing upstream initiation in favor of downstream initiation. TISCA2 further reveals tissue-specific initiation landscapes in vivo, with increased detection of downstream candidate start sites in the testis. Finally, we develop IncepTIS, a sequence-based deep learning model that predicts position-resolved and condition-dependent initiation landscapes. IncepTIS-guided optimization generates de novo mRNA 5' leaders with stress-responsive initiation patterns. Together, TISCA2 and IncepTIS provide an experimental and computational framework for mapping, predicting and engineering translation initiation in mammalian cells and tissues.