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Related Experiment Video

Updated: Apr 6, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Using SEQUEST with theoretically complete sequence databases.

Rovshan G Sadygov^1,2

¹Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, 77555, USA. rovshan.sadygov@utmb.edu.

Journal of the American Society for Mass Spectrometry

|August 5, 2015

Summary

This summary is machine-generated.

This study demonstrates that the SEQUEST algorithm can accurately identify peptides by searching against a comprehensive database of theoretically possible sequences, not just known protein databases. High mass accuracy enhances SEQUEST

Keywords:

All theoretically possible peptides De novo Peptide sequencing Mass distribution of peptides SEQUEST

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Area of Science:

Proteomics
Bioinformatics
Mass Spectrometry

Background:

The SEQUEST algorithm is a standard tool for peptide and protein identification using tandem mass spectrometry and protein sequence databases.
Its success relies on the presence of target peptides within these databases, limiting its scope for novel or unexpected sequences.

Purpose of the Study:

To explore a novel application of the SEQUEST algorithm for de novo-like peptide sequencing.
To assess the feasibility of searching a comprehensive database of theoretically possible peptides against experimental mass spectral data.

Main Methods:

Generated a database of all theoretically possible peptide sequences based on precursor masses and high mass accuracy (0.001 Da).
Utilized a de novo sequencing approach combined with an algorithm to determine amino acid compositions from mass intervals.
Employed SEQUEST to search experimental spectra against this complex theoretical peptide database.

Main Results:

The SEQUEST algorithm successfully ranked correct peptide matches among the top sequence hits when searching the theoretical database.
The cross-correlation score proved effective in identifying the correct peptide sequence.
This approach demonstrated the high specificity of SEQUEST, especially when coupled with high mass accuracy for intact peptides.

Conclusions:

SEQUEST can be effectively applied to identify peptides from a de novo sequencing perspective by searching against a theoretically generated peptide database.
High mass accuracy is crucial for the specificity and success of this novel application of SEQUEST.
This method expands the utility of SEQUEST beyond traditional database searching for peptide identification.