ArticleNucleic acids research2024
Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- The Japan-UK Synthetic Biology Conference, Spring 2025: Strengthening Global Links to Engineer Biology.ACS synthetic biology · 2025Article
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Emerging trends in genome integration tools for precision engineering of diverse bacterial species.Synthetic biology (Oxford, England) · 2025Review
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
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2 authors.
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Abstract
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.
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