Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction
peer-reviewed · 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2025
| Date | 2025-12-15 |
| Type | peer-reviewed |
| Venue | 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
| Publisher | IEEE |
| Contribution | adjacent |
| DOI | 10.1109/bibm66473.2025.11356942 |
| Citations (OpenAlex) | 1 |
Abstract
Mirror-image peptides composed of D-amino acids offer exceptional storage density, stability, and longevity, making them a promising medium for biological data storage. However, accurate de-novo sequencing remains challenging due to limited tandem mass spectrometry data and algorithmic constraints. To address this, we propose DBond, a deep neural network that predicts peptide bond cleavage by integrating sequence, precursor ion, and environmental features, enabling the design of mirror-image peptides that are easier to sequence. We further introduce MiPD513, a dataset of 513 mirror-image peptides, and a peptide bond cleavage labeling algorithm (PBCLA) that generates approximately 12.5 million labeled samples. Two prediction strategies, multi-label and single-label classification, are proposed, with the single-label approach found to perform better and providing a basis for sequence optimization. Code is available at https://github.com/LoserLus/DBond.
Methods and tools
- DBond: Deep network predicting peptide bond cleavage from sequence, precursor and environment features, used to design mirror-image (D-amino acid) storage peptides that de novo sequencing reads more reliably.
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