Transforming de novo peptide sequencing by explainable AI
preprint · Research Square · 2024
| Date | 2024-08-05 |
| Type | preprint |
| Venue | Research Square |
| Publisher | Research Square |
| Contribution | algorithm |
| DOI | 10.21203/rs.3.rs-4716013/v1 |
| Citations (OpenAlex) | 4 |
Abstract
De novo peptide sequencing is crucial for identifying novel proteins, yet its broader application is constrained by the lack of a robust quality control system. In response, we developed a transformer-based model, π-xNovo, that accurately predicts peptides. By analyzing the model’s attention matrix, we elucidated the contribution of spectral peaks to amino acid predictions, thus making de novo sequencing results explainable. Leveraging these insights, we designed a quality control system, π-xNovo-QC, which distinguishes peptide predictions with an accuracy exceeding 80% and a sensitivity above 90%. Applying this system to a large-scale deep human proteome dataset resulted in the identification of 1,931,761 additional peptides, marking a 137% increase over traditional database search results. These newly identified peptides with high confidence facilitated a 17.9% increase in protein identification, a 23.59% increase in the detection of single amino acid polymorphism events, and a 20.02% increase in exon-skipping splicing events. The deployment of this explainable AI system holds significant potential for expanding the application of de novo peptide sequencing, particularly in exploring the darker matter of the entire proteome universe.
Methods and tools
- π-xNovo: Explainable AI sequencing
Cites (13)
- NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024) crossref
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024) crossref
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024) crossref
- Accurate de novo peptide sequencing using fully convolutional neural networks (2023) crossref
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) crossref
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) crossref
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) crossref
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010) crossref
- DirecTag: Accurate Sequence Tags from Peptide MS/MS through Statistical Scoring (2008) crossref
- De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) crossref
- Algorithms for de novo peptide sequencing using tandem mass spectrometry (2004) crossref
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref
- De novo peptide sequencing via tandem mass spectrometry (1999) crossref