Mapping Dynamic Protein Interaction Landscapes in Saccharomyces cerevisiae Using a Novel Whole Network Enrichment Approach

Cellular physiology is dynamic in nature, especially in response to perturbation. Despite significant advances in genetics, biochemistry and cell biology, signal transduction networks and protein effectors that mediate specific cellular responses have mostly been annotated under static (i.e. basal) cellular conditions, both at the genetic and physical interaction level. These approaches provide little information as to the dynamics of a given pathway or its constituents under varying patho‐physiological contexts. Proteins that compose these cellular networks are largely organized into protein complexes that work in concert to enable desired cellular responses. Therefore, defining the architecture and response of these complexes to perturbation is vital to our mechanistic understanding of fundamental cellular biology, and may provide critical insight into disease pathology. We have developed and applied methods to annotate the architecture and dynamics of protein complex networks by utilizing a novel whole‐network affinity pull‐down method coupled to novel computational analysis tools. This method involves generation of TAP‐tagged node proteins, which are expressed, pooled and affinity‐purified in parallel. This approach allows for efficient system‐wide identification and quantification of protein complex members and novel interactors in response to perturbation in a highly efficient manner. This proteomic platform has been enabled through development and application of biochemical and proteomic methodologies to study network response to known perturbations in established protein networks. Specifically, as a proof‐of‐principle, the developed platform has been employed to study changes in nutrient sensing protein complexes in response to rapamycin treatment in Saccharomyces Cerevisiae . This concept has been extended to the DNA damage response as well as energy homeostasis networks in yeast, and the latter correlated to a homologous mammalian network for investigation of metabolic dysregulation in disease. By measuring the dynamics of protein networks, we have identified novel associations within and between components that have otherwise been missed in traditional protein‐by‐protein Affinity Purification‐Mass Spectrometry (AP‐MS) methodologies, and this is the first proteomic platform to enable dynamic interaction measurements at a network‐wide scale. Support or Funding Information NIH P41 GM103533, R01 MH67880