Protein de novo peptide sequencing method and system based on mass spectrometry
CN113921086A · Shanghai Applied Protein Technology · priority 2021
| Patent | CN113921086A ↗ |
| Status | Granted |
| Priority date | 2021-09-14 |
| Filed | 2021-09-14 |
| Published | 2022-01-11 |
| Assignee | Shanghai Applied Protein Technology (printed Shanghai Applied Protein Technology Co Ltd) |
| Also published as | CN113921086B |
Abstract
The invention provides a protein de novo peptide sequencing method and a system based on mass spectrometry, which comprises a convolutional neural network model, a migration neural network model and a multi-modal data model; extracting mass spectrum MS1 data and mass spectrum MS2 data from the original mass spectrogram by the convolutional neural network model, and extracting and processing features; the migration neural network model carries out deep learning on the convolution neural network model; the multi-mode data model trains and predicts a convolution neural network model and a migration neural network model by utilizing an amino acid sequence to obtain a peptide segment sequence. The mass spectrum MS2 data are filtered and screened through the ion retention time of the mass spectrum MS1 data, and then the migration neural network model is used for deep learning of the convolution model, so that the peptide fragment sequence of the original map can be accurately predicted finally, and the problem of how to improve the accuracy of the non-reference sequencing in the de novo protein peptide sequencing is solved.
Why it is here
Shanghai Applied Protein Technology: de novo sequencing with convolutional networks over MS1 and MS2 data.
Inventors (3)
阮宏强, 张鹏, 薛冰冰
An inventor links to an author page only where the same person is evident, never on a name alone.
Catalogued papers it cites (1)
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) added by the examiner
Matched from the patent’s 5 literature citations by the rules the paper citation graph uses.
Catalogued patents it cites (1)
- US11694769B2 Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning
Cited by catalogued patents (1)
- CN116825198B Peptide sequence tag identification method based on graph annotation mechanism
Cited by (4 patent families, 4 organisations)
- Hunan University of Technology and Business academic: CN116825198A
- Xiamen University academic: CN115828152A
- 五邑大学 academic: CN118298928A
- 南开大学 academic: CN115798581A
Every family, of any applicant, that cites this one, as Google Patents lists them; an assignee is given its English name.