ArticleJournal of microbiology and biotechnology2026
Deep Learning-Based Protein Half-Life Prediction for Identifying Rate-Limiting Enzymes in Metabolic Pathways to Alleviate Bottleneck Reactions.
Article in Journal of microbiology and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
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9 authors.
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Abstract
In synthetic metabolic pathways, the intracellular level of enzymes is a critical determinant of pathway efficiency and, thus, short-lived enzymes create bottlenecks and limit overall metabolic productivity due to their low abundance. However, since studies on protein half-life remain limited in bacteria, its accurate prediction is a significant challenge. To address this, we developed a machine learning model, ProHL, for the classification of short-lived and long-lived proteins. ProHL employs a multimodal strategy, integrating ProteinBERT encodings (at both residue and sequence levels) with physicochemical encodings of the protein sequences. This integration enables the effective capture of both local and global sequence features, thereby ensuring accurate half-life classification. When evaluated on an independent test dataset of
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