De novo glycopeptide sequencing
US20250111901A1 · Venn Biosciences · priority 2022
| Patent | US20250111901A1 ↗ |
| Status | Abandoned |
| Priority date | 2022-02-14 |
| Filed | 2023-02-14 |
| Published | 2025-04-03 |
| Assignee | Venn Biosciences (printed Venn Biosciences Corp) |
| Also published as | AU2023217752A1, CA3243725A1, EP4479753A1, WO2023154943A1 |
Abstract
A system and method of training a machine learning model to predict glycopeptide fragmentation patterns and retention times. Spectral data for a plurality of fragments of a glycopeptide structure is received. Glycan fragment composition data is generated using the spectral data. The glycan fragment composition data identifies a plurality of composition codes and a plurality of total intensities for a plurality of glycan fragments identified from the plurality of fragments using the spectral data. A linear glycan sequence is created using the glycan fragment composition data. A training input is formed for a machine learning model using the linear glycan sequence. The machine learning model is trained using the training input to predict a fragmentation pattern and a retention time for the glycopeptide structure.
Why it is here
Venn Biosciences: de novo glycopeptide sequencing, predicting glycan structures from MS/MS with a learned glycopeptide spectral library (family of WO2023154943A1).
Inventors (2)
Zhewei LIANG, Mingqi LIU
An inventor links to an author page only where the same person is evident, never on a name alone.