Optimal peptide quantification via machine learning enhanced fragment ion ranking in DIA-MS proteomics

Abstract: In a standard mass spectrometry workflow, acquired mass spectra are searched against a library of peptides to extract peptide spectra matches (PSMs). Peptides are normally quantified by aggregating the intensities of fragment ions extracted from MS/MS spectra. However, quantifying peptides by summing up the intensities of all fragment ions in PSMs can resulting in inaccurate results due to the fact that multiple fragment ions can interfere with each other in complex samples. This project aims to use machine learning to enhance fragment ion ranking for every precursor to ensure optimal peptide quantification. Here, we describe a workflow that leverages machine learning to pick only the most confident fragments extracted for each potential peptide for quantification. We demonstrate the usability of the workflow on yeast standard benchmark data, showing that the average accuracy of quantification and differential expression across all optimized methods is 22.46% higher than the standard workflow. In addition, we investigate the performance of our workflow on existing complex and low-fold-change proteomic data containing four species and demonstrate the generalizability of our models on unrelated diverse data sets.