Panov, A. | bioRxiv Science

bioRxiv2020

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins - half the proteome - in 293T cells and includes 118,162 interactions among 14,586 proteins; the second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome at unprecedented scale, encoding protein function, localization, and complex membership. Their comparison validates thousands of interactions and reveals extensive customization of each network. While shared interactions reside in core complexes and involve essential proteins, cell-specific interactions bridge conserved complexes, likely rewiring each cells interactome. Interactions are gained and lost in tandem among proteins of shared function as the proteome remodels to produce each cells phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

systems biology↗

bioRxiv2019

Analysis of Independent Differences (AID) detects complex thermal proteome profiles independent of shape and identifies candidate panobinostat targets

Identifying global cellular targets of small molecules is a challenge for drug discovery. Thermal proteome profiling (TPP) is a recent technique that uses quantitative proteomics to identify all small molecule protein targets in a single experiment. One current TPP analysis method relies on two major assumptions: sigmoidal melting curve behavior and that intra-condition dependencies preclude an independent and identically distributed model. Herein, we use a previously published panobinostat TPP dataset to show that these assumptions do not hold true and present a novel, shape-independent method, named Analysis of Independent Differences (AID). For each temperature, AID models the differences between conditions of fractions of non-denatured protein as an independent Normal distribution, resulting in a Multivariate Normal observation for each protein. The log of a Multivariate Normal p-value ranks the proteins from most to least likely shifted, and individual Normal p-values within each protein allow for qualitative inspection. Applying AID to the panobinostat dataset revealed known targets in the top 3% of most shifted proteins, as well as candidate targets involved in myeloid leukocyte activation. AID detects complex melting profiles and can be extended to any number of temperature channels, ligand-protein or protein-protein interactions, or general curve data for deeper biological insight.

bioinformatics↗