Peptide turnover prediction using transformer architectures on large-scale time-series proteomic data

Protein turnover is essential for maintaining cellular homeostasis and is closely linked to regulatory and disease-related mechanisms. Recent advances in mass spectrometry have enabled high-precision peptide-level measurements; however, identifying sequence-intrinsic signals that determine protein lifespan remains challenging due to the diversity of degradation pathways and cellular variability. Meanwhile, transformer-based protein language models have demonstrated strong capabilities for learning biologically relevant features from amino acid sequences, motivating the development of predictive frameworks for turnover dynamics directly from sequence information. We developed two architectures that integrate embeddings from a pre-trained protein language model with time-series prediction models: TimeSeq and AASeq. TimeSeq uses averaged sequence embeddings to predict all time points (1–48 h) in a single pass, while AASeq retains residue-level embeddings to enable sequential prediction and attribution analysis. TimeSeq achieved high predictive performance on a large public dataset (R² = 0.809 ± 0.015 on the full dataset; R² = 0.760 ± 0.023 on the sampled dataset). Attribution analysis from AASeq suggested that known degradation-associated features, including PEST-like motifs, may contribute to the model’s predictions, suggesting that the learned representations may capture certain sequence-related determinants of turnover. This study demonstrates that integrating transformer-derived sequence embeddings with time-series models enables accurate prediction of peptide-level turnover dynamics and may contribute to understanding how sequence-encoded features relate to degradation behavior. The proposed framework provides a foundation for exploring determinants of protein lifespan directly from amino acid sequences.