Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast

Abstract

BackgroundA hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction “degree” of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.ResultsWe find that protein “stickiness”, measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein’s high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.ConclusionsOur work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

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“…In living organisms, functional protein–protein interactions compete with a much larger number of promiscuous nonfunctional interactions. Several cellular properties can help to prevent unwanted protein interactions, including regulation of gene expression, cellular compartmentalization, and high specificity and affinity of functional interactions . Based on the AA frequencies in interface patches versus the solvent‐accessible protein surface, the “stickiness” scale for each of the 20 AAs can be defined as follows: …”

Integrating Phenotypic Plasticity Within an Ecological Genomics Framework: Recent Insights from the Genomics, Evolution, Ecology, and Fitness of Plasticity

“…Upon exposure to a novel environment, this genetic variation produces novel phenotypic variation, and may facilitate adaptation. Developmental noise The production of alternative phenotypes by a single genotype under identical environmental conditions (Raser and O'Shea 2005) due to molecular stochasticity in the birth and death rates of transcripts, the effects of low-abundance regulatory proteins, the stickiness of proteins, and random fluctuations in promoter behavior (Raser and O'Shea 2005;Brettner and Masel 2012;Singh et al 2012). Dominant plasticity In niche complementarity, occurs when a superior competitor with high resource use plasticity alters the resources it uses depending on the competitive environment.…”

A Comprehensive Immunoreceptor Phosphotyrosine-based Signaling Network Revealed by Reciprocal Protein–Peptide Array Screening

“…(40). However, interactions driven by post-translational modifications such as phosphorylation, which comprises a significant part of the PPI network, have proven difficult to decipher systematically by high throughput methods such as yeast two-hybrid (15) and AP-MS (16). Although systematic yeast two-hybrid screen has been applied to human open-reading frames (ORFs) (15), it is not suitable for deciphering interactions mediated by phosphorylation.…”

“…A variety of different high-throughput methods have been developed for the identification of protein-protein or protein-peptide interactions to date. These include yeast two-hybrid (15), affinity purification coupled to mass spectrometry (AP-MS) (16), phage display (17), and protein (18) and peptide arrays (19). We employed protein and peptide arrays in our study because of their simplicity and amenability to post-translational modifications.…”