Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry

peer-reviewed · 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) · 2023

peer-reviewed · 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) · 2023. Shiva Ebrahimi et al. Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively…
Date 2023-12-04
Type peer-reviewed
Venue 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
Publisher IEEE
Contribution algorithm
DOI 10.1109/BIBE60311.2023.00013
Citations (OpenAlex) 15

Abstract

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.

Authors

  1. Shiva Ebrahimi · University of North Texas
  2. Xuan Guo · University of North Texas

Methods and tools

Cites (13)

Cited by (10)

Seen in the charts

Back to the full map

Back to top