Higher‐order modular regulation of the human proteome

Abstract

Operons are transcriptional modules that allow bacteria to adapt to environmental changes by coordinately expressing the relevant set of genes. In humans, biological pathways and their regulation are more complex. If and how human cells coordinate the expression of entire biological processes is unclear. Here, we capture 31 higher‐order co‐regulation modules, which we term progulons, by help of supervised machine‐learning on proteomics data. Progulons consist of dozens to hundreds of proteins that together mediate core cellular functions. They are not restricted to physical interactions or co‐localisation. Progulon abundance changes are primarily controlled at the level of protein synthesis and degradation. Implemented as a web app at www.proteomehd.net/progulonFinder , our approach enables the targeted search for progulons of specific cellular processes. We use it to identify a DNA replication progulon and reveal multiple new replication factors, validated by extensive phenotyping of siRNA‐induced knockdowns. Progulons provide a new entry point into the molecular understanding of biological processes. image Clustering is combined with machine‐learning to identify large modules of co‐regulated proteins in the human proteome. A simple web‐based implementation of the workflow allows users to search for additional co‐regulation modules. Progulons, large modules of proteins that are co‐regulated in response to perturbations, are identified as an organisational principle of the human proteome. Progulons are evolutionarily conserved and most, but not all, are controlled predominantly at the protein level. The webtool https://www.proteomehd.net/progulonFinder allows noncomputational biologists to execute the developed machine‐learning workflow to identify additional progulons. The progulonFinder webtool is used to predict novel DNA replication factors, validated by siRNA screening.