Alexander B Saltzman, Mei Leng, Bhoomi Bhatt, Purba Singh, Doug W Chan, Lacey Dobrolecki, Hamssika Chandrasekaran, Jong M Choi, Antrix Jain, Sung Y Jung, Michael T Lewis, Matthew J Ellis, Anna Malovannaya

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy in comparison to conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient- derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.