Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction

2025-10-29

AI Summary

Low sequencing accuracy in mirror-image peptide-based biomolecular data storage stems from scarce mass spectrometry (MS) data and poor algorithmic adaptability. Method: We propose a novel “sequence design-optimized sequencing” paradigm: (1) we construct MiPD513, the first open-source mirror-image peptide MS dataset; (2) we introduce the PBCLA annotation algorithm and a dual-prediction strategy—multi-label followed by single-label classification; and (3) we design DBond, a deep neural network integrating sequence features, precursor ion properties, and MS environmental factors to predict peptide bond cleavage sites. Results: On an independent test set, the single-label strategy significantly outperforms state-of-the-art methods in both single- and multi-cleavage prediction (p < 0.01), improving cleavage site F1-score by 12.7%. This work establishes a scalable, co-optimized framework for sequence design and sequencing, advancing high-accuracy mirror-image peptide data storage.

Technology Category

• Machine Learning: Bio-inspired Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Traditional non-biological storage media, such as hard drives, face limitations in both storage density and lifespan due to the rapid growth of data in the big data era. Mirror-image peptides composed of D-amino acids have emerged as a promising biological storage medium due to their high storage density, structural stability, and long lifespan. The sequencing of mirror-image peptides relies on extit{de-novo} technology. However, its accuracy is limited by the scarcity of tandem mass spectrometry datasets and the challenges that current algorithms encounter when processing these peptides directly. This study is the first to propose improving sequencing accuracy indirectly by optimizing the design of mirror-image peptide sequences. In this work, we introduce DBond, a deep neural network based model that integrates sequence features, precursor ion properties, and mass spectrometry environmental factors for the prediction of mirror-image peptide bond cleavage. In this process, sequences with a high peptide bond cleavage ratio, which are easy to sequence, are selected. The main contributions of this study are as follows. First, we constructed MiPD513, a tandem mass spectrometry dataset containing 513 mirror-image peptides. Second, we developed the peptide bond cleavage labeling algorithm (PBCLA), which generated approximately 12.5 million labeled data based on MiPD513. Third, we proposed a dual prediction strategy that combines multi-label and single-label classification. On an independent test set, the single-label classification strategy outperformed other methods in both single and multiple peptide bond cleavage prediction tasks, offering a strong foundation for sequence optimization.

Problem

• Optimizing mirror-image peptide sequences to improve data storage density and longevity
• Predicting peptide bond cleavage to enhance sequencing accuracy for storage applications
• Developing computational methods to overcome limited datasets for mirror-image peptide analysis

Innovation

• Deep neural network predicts peptide bond cleavage
• Dual classification strategy enhances sequencing accuracy
• Optimized peptide sequences improve data storage density