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Related Experiment Video

Updated: Sep 28, 2025

Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

TIDD: tool-independent and data-dependent machine learning for peptide identification.

Honglan Li¹, Seungjin Na², Kyu-Baek Hwang³

¹Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea.

BMC Bioinformatics

|March 31, 2022

Summary

TIDD is a new tool for shotgun proteomics that improves peptide identification accuracy. It works with any search engine, unlike existing methods, by using universal features for better results.

Keywords:

Data-dependent Machine learning Mass spectrometry PSM rescoring Peptide identification Tool-independent

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Area of Science:

Proteomics
Computational Biology
Mass Spectrometry

Background:

Shotgun proteomics relies on database search engines and post-processing tools to identify peptides from MS/MS spectra.
Popular tools like Percolator and PeptideProphet enhance peptide identification but require specific feature optimization for each search engine.
This limitation hinders their application with novel or diverse search engine outputs.

Purpose of the Study:

To develop a universal post-processing tool for peptide identification in shotgun proteomics.
To overcome the limitations of existing methods that require engine-specific feature engineering.

Main Methods:

Developed TIDD, a post-processing tool utilizing universal features to assess peptide-spectrum match quality.
TIDD allows integration of additional search engine-specific features.
Implemented a user-friendly graphical interface.

Main Results:

TIDD demonstrated comparable or superior performance to Percolator in peptide identification.
Achieved 10.23-38.95% increase in PSMs compared to target-decoy estimation for MSFragger.
Successfully processed results from various search engines without prior feature optimization.

Conclusions:

TIDD eliminates the need for specialized feature engineering for different database search tools.
The tool is directly applicable to any database search results, including those from newly developed engines.
Enhances confident peptide identifications across diverse proteomics search platforms.