Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines

Abstract

Spectral counting has become a widely used approach for comparing protein abundance in label-free shotgun proteomics. However, when analyzing complex samples, the ambiguity of matching between peptides and proteins greatly affects the assessment of peptide and protein differentiation. Meanwhile, the configuration of database searching algorithms that assign peptides to MS/MS spectra may produce different results. Here, I present three strategies to improve comparative proteomics through spectral counting. I show that comparing spectral counts for peptide groups rather than for protein groups forestalls problems introduced by shared peptides. I present four models to combine four popular search engines that lead to significant gains in spectral counting differentiation. Among these models, I demonstrate a powerful vote counting model that scales well for multiple search engines. I also show that semi-tryptic searching outperforms tryptic searching for comparative proteomics. Overall, these techniques considerably improve protein differentiation on the basis of spectral count tables.