Analysis of Complexome Profiles with the Gaussian Interaction Profiler (GIP) Reveals Novel Protein Complexes in Plasmodium falciparum

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