An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics.

Area of Science:

• Proteomics and Bioinformatics
• Biochemical Analysis
• Mass Spectrometry Applications

Background:

• Peptidomics, the analysis of protein degradation products, offers insights into cellular processes and disease.
• Existing proteomics data analysis tools require evaluation for peptidomics applications.
• A comprehensive informatics pipeline is needed for efficient peptidomics data analysis.

Purpose of the Study:

• To evaluate and compare different informatics pipelines for peptidomics data analysis.
• To identify the most effective strategies for peptide identification and label-free quantification.
• To propose an optimized pipeline for high-throughput peptidomics.

Main Methods:

• Comparative analysis of MS/MS database search engines: MS-GF+, SEQUEST, and MS-Align+.
• Identification and label-free quantification using Accurate Mass and Time (AMT) tag and Informed Quantification (IQ) approaches.
• Direct Liquid Chromatography-Mass Spectrometry (LC-MS) analysis.

Main Results:

• MS-GF+ demonstrated superior performance in identifying peptidome peptides compared to SEQUEST and MS-Align+.
• The AMT tag and IQ approaches, using an MS-GF+-derived database, achieved deeper peptidome coverage and reduced missing data.
• Both AMT tag and IQ methods provided robust label-free quantification, with IQ showing slightly higher coverage and correlating well with AMT tag results.

Conclusions:

• An optimized informatics pipeline combining MS-GF+ for database searching and IQ (or AMT tag) for quantification is proposed.
• This pipeline enables high-throughput, comprehensive, and quantitative peptidomics analysis.
• The findings advance the utility of peptidomics in disease diagnosis and prognosis.