Analysis of\nComplexome Profiles with the Gaussian\nInteraction Profiler (GIP) Reveals Novel Protein Complexes in Plasmodium falciparum

Complexome profiling is an experimental approach to identify\ninteractions\nby integrating native separation of protein complexes and quantitative\nmass spectrometry. In a typical complexome profile, thousands of proteins\nare detected across typically ≤100 fractions. This relatively\nlow resolution leads to similar abundance profiles between proteins\nthat are not necessarily interaction partners. To address this challenge,\nwe introduce the Gaussian Interaction Profiler (GIP), a Gaussian mixture\nmodeling-based clustering workflow that assigns protein clusters by\nmodeling the migration profile of each cluster. Uniquely, the GIP\noffers a way to prioritize actual interactors over spuriously comigrating\nproteins. Using previously analyzed human fibroblast complexome profiles,\nwe show good performance of the GIP compared to other state-of-the-art\ntools. We further demonstrate GIP utility by applying it to complexome\nprofiles from the transmissible lifecycle stage of malaria parasites.\nWe unveil promising novel associations for future experimental verification,\nincluding an interaction between the vaccine target Pfs47 and the\nhypothetical protein PF3D7_0417000. Taken together, the GIP provides\nmethodological advances that facilitate more accurate and automated\ndetection of protein complexes, setting the stage for more varied\nand nuanced analyses in the field of complexome profiling. The complexome\nprofiling data have been deposited to the ProteomeXchange Consortium\nwith the dataset identifier PXD050751.