sORF-Trans2MS: a two-module deep learning framework for sORF translation and MS-supported microprotein prediction

Ribosome profiling is widely used to identify translated small open reading frames (sORFs), but existing prediction models often generalize poorly to newly collected sORF datasets. In addition, many microproteins with strong mass spectrometry (MS) evidence lack support in public Ribo-seq resources, suggesting that Ribo-supported sORF and MS-supported microprotein detection may represent complementary prediction tasks. Here, we developed sORF-Trans2MS, a two-module deep learning framework for predicting Ribo-supported sORFs and MS-supported microproteins. Module I uses RNA-FM representations for upstream, ORF, and downstream regions with a Transformer encoder for Ribo-supported sORF translation prediction. Module II integrates pretrained RNA-FM representations of RNA context with ESM-2 protein representations using multi-scale CNN encoders for predicting translated microproteins supported by MS but not Ribo-seq data. Module I was trained on 9,579 Ribo-supported sORFs and matched background sORFs, while Module II used 14,697 MS-supported (MS++Ribo-) microproteins and matched backgrounds. Module I achieved an AUROC of 0.870 internally and 0.930 on an independent GENCODE dataset. Ablation analysis highlighted upstream RNA context, including a distinct signal around 50 nt upstream of AUG. Module II achieved an AUROC/AUPR of 0.804/0.797 internally and 0.785/0.773 on an independent mass spectrometry dataset. Model interpretation highlighted upstream RNA context and the protein N terminus, while literature-supported microproteins further illustrated the complementary roles of the two modules. Overall, sORF-Trans2MS improves prediction and generalization of Ribo-supported sORFs and extends computational discovery to MS-supported candidates that are not represented in current public Ribo-seq resources.