Deep learning-based investigation of chloroplast translation regulatory sequences.

ABSTRACT: Understanding the architecture of translational regulatory sequences in diverse chloroplasts is critical for advancing synthetic biology and genetic engineering. In this study, a hybrid deep learning model combining convolutional neural network (CNN), long short-term memory (LSTM), Attention, and Residual architectures was developed to classify and analyse two datasets: 5' untranslated region sequences from plants and algae, and the sequences with and without Shine-Dalgarno (SD) motifs from both groups. Using 300-nucleotide leader sequences upstream of the start codon as input, the model achieved strong prediction performance for both taxonomic origin and the presence or absence of SD motifs. However, a small subset of plant and algal sequences exhibited algal-like and plant-like patterns,

SUBMITTER: Abbasi-Vineh MA 

PROVIDER: S-EPMC12738836 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature