A protein database search and de novo sequencing model and its training method
CN121983112A · Institute of Computing Technology · priority 2025
| Patent | CN121983112A ↗ |
| Status | Pending |
| Priority date | 2025-12-01 |
| Filed | 2025-12-01 |
| Published | 2026-05-05 |
| Assignee | Institute of Computing Technology |
Abstract
The invention provides a protein database searching and de-head sequencing model and a training method thereof, wherein the model comprises a peptide fragment sequence encoder, a mass spectrogram encoder, a joint modal score device and a peptide fragment length perception decoder, the method includes training the model in multiple rounds using a training set, each sample including mass spectral data and a sequence of real peptide fragments. The method comprises the steps of searching a candidate peptide fragment set according to mass spectrum data in each round, encoding each peptide fragment sequence in the set by utilizing a peptide fragment sequence encoder to obtain characteristic representation of each peptide fragment, encoding mass spectrum data by utilizing a mass spectrum encoder to obtain characteristic representation of a mass spectrum, processing the characteristic representation of the mass spectrum and the characteristic representation of each peptide fragment by utilizing a joint modality score device for a searching task to obtain matching scores of each peptide fragment sequence and the mass spectrum data, generating the peptide fragment sequence according to the characteristic representation of the mass spectrum by utilizing a peptide fragment length perception decoder for a de novo sequencing task, and constructing a total loss function based on the two tasks to update model parameters.
Why it is here
Institute of Computing Technology: training one model for both database search and de novo sequencing, with a length-aware peptide decoder.
Inventors (15)
赵家乐, 毛鹏志, 王凯菲, 李一鸣, 彭雅萍, 陈冉飞, 卢淑祺, 吉小洪, 丁佳香, 张欣, 廖育城, 鄂维南, 张玮杰, 温翰, 迟浩
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