UTR-Diffusion: Conditional Diffusion Modeling for Multi-objective and Constrained UTR Design
Dai, C.; Sato, K.
Show abstract
Motivation: The 5-prime untranslated region (UTR) and the start-codon-proximal region of the coding sequence (CDS) jointly influence translation efficiency and local RNA secondary-structure stability, while synonymous codon choices throughout the CDS shape codon adaptation. Because the encoded protein is often predetermined, practical mRNA design must coordinate these quantitative objectives while preserving specified nucleotide sequences and amino-acid identities. Existing generative approaches typically address continuous-valued targeting, explicit sequence constraints, and codon-usage control separately rather than integrating all three within a single model. Results: We present UTR-Diffusion, a diffusion-based framework for 5-prime UTR and 5-prime UTR-CDS junction design. UTR-Diffusion conditions generation on continuous-valued MRL and MFE targets and supports nucleotide-level constraints, amino-acid-level constraints with synonymous-codon flexibility, and codon-adaptiveness control that modulates the sequence-level codon adaptation index (CAI). Systematic evaluations across dense MRL-MFE target grids showed that generated distributions shifted consistently with both targets, retained substantial diversity, and strictly preserved specified nucleotide sequences and amino-acid identities. Codon-adaptiveness control yielded distinct, monotonically ordered CAI levels that closely followed the specified adaptiveness targets. In comparative benchmarks, UTR-Diffusion outperformed representative existing methods in high-MRL optimization and precise MRL targeting for 5-prime UTR design, and achieved higher MRL, less-negative junction MFE, and higher CAI than peptide-preserving baselines in 50-nt 5-prime UTR-CDS junction design.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data 95%
- DeepLocRNA: An Interpretable Deep Learning Model for Predicting RNA Subcellular Localization with domain-specific transfer-learning 93%
- Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions 93%
Similar papers in this journal
- EvoRMD: Integrating Biological Context and Evolutionary RNA Language Models for Interpretable Prediction of RNA Modifications 95%
- HydraRNA: a hybrid architecture based full-length RNA language model 95%
- DeepSAP: Improved RNA-Seq Alignment by Integrating Transcriptome Guidance with Transformer-Based Splice Junction Scoring 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.