Differential analysis of combinatorial protein complexes with CompleXChange

Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale.

We observed for simulated data that results obtained by our complex abundance estimation algorithm were in better agreement with the ground truth and physicochemically more reasonable compared to previous efforts that used linear programming while running in a fraction of the time. The practical usability of the method was assessed in the context of transcription factor complexes in human monocyte and lymphoblastoid samples. We demonstrated that our new method is robust against false-positive detection and reports deregulated complexomes that can only be partially explained by differential analysis of individual protein-coding genes. Furthermore we showed that deregulated complexes identified by the tool potentially harbor significant yet unused information content.

CompleXChange allows to analyze deregulation of the protein complexome on a whole-genome scale by integrating a plethora of input data that is already available. A platform-independent Java binary, a user guide with example data and the source code are freely available at https://sourceforge.net/projects/complexchange/ .