sequencing data encoded peptides according to tandem mass spectrometry
By employing a two-stage sequencing method and a sequencing method based on the highest strength tag, the problem of low sequencing efficiency of data-encoded peptides in existing technologies has been solved, enabling efficient and accurate sequencing of non-natural amino acids and random peptide sequences.
CN116895335BActive Publication Date: 2026-04-14THE HONG KONG POLYTECHNIC UNIV
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Patent Information
- Application Number
- CN202310375584.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Priority Date
- 2022-04-11
- Filing Date
- 2023-04-10
- Publication Date
- 2026-04-14
- Estimated Expiration
- 2043-04-10
AI Technical Summary
Technical Problem
Existing technologies struggle to efficiently and economically sequence data-encoded peptides, especially when using non-natural amino acids and random peptide sequences, as existing algorithms lack training data and adaptability.
Method used
A two-stage sequencing method and a sequencing method based on the highest intensity tag were used. Candidate sequences were identified and peptide sequences were estimated by preprocessing mass spectrometry data. Invalid sequences were removed by checksum and cleanup, and the peptide sequences were finally determined.
Benefits of technology
It improves the efficiency and accuracy of data-encoded peptide sequencing, reduces sequencing time and cost, and adapts to the complexity of non-natural amino acid and random peptide sequences.
✦ Generated by Eureka AI based on patent content.
Abstract
Peptide sequencing is important in decoding data stored in data-encoding peptides. Tandem mass spectrometry (MS / MS) is particularly useful for peptide sequencing. In a computer-implemented method for sequencing a data-encoding peptide from an experimental spectrum, raw data of the experimental spectrum is first pre-processed to remove uninterpretable peaks, resulting in pre-processed data. A first set of one or more candidate sequences of a peptide sequence that competes with the peptide is identified from the spectrum. The spectrum is formed from the pre-processed data rather than the raw data to result in a smaller number of candidate sequences, thereby reducing the time cost of sequencing. The first set of candidate sequences is then processed to estimate the peptide sequence, resulting in a set of peptide sequence estimate sequences. Each estimate sequence is verified for invalidity. The set of peptide sequence estimate sequences is cleaned to remove any invalid estimate sequences.
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