Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning

US11694769B2 · kind B2 · utility

Assignee

• BIOINFORMATICS SOLUTIONS INC. · CA

Inventors

• Baozhen Shan · Waterloo, CA
• Ngoc Hieu Tran · Waterloo, CA
• Ming-Shuan Li · Singapore, SG
• Lei Xin · Waterloo, CA
• Rui Qiao · Cupertino, US
• Xin Chen · Lexington, US
• Chuyi Liu · Waterloo, CA

Key dates

• Filing date: Dec 19, 2018
• Grant date: Jul 4, 2023
• Expiry date: May 4, 2042

Classification

• Technology area (CPC H): Electricity
• CPC primary: H01J49/0036
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data, and in particular mass spectrometry data obtained by data-independent acquisition. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. To sequence peptides from mass spectrometry data obtained by data-independent acquisition, precursor profiles representing intensities of one or more precursor ion signals associated with a precursor retention time and fragment ion spectra representing signals from fragment ions and fragment retention times are fed into a neural network.