Prediction of quality-control degradation signals in yeast proteins

Abstract

Effective proteome homeostasis is key to cellular and organismal survival, and cells therefore contain efficient quality control systems to monitor and remove potentially toxic misfolded proteins. Such general protein quality control to a large extent relies on the efficient and robust delivery of misfolded or unfolded proteins to the ubiquitin-proteasome system. This is achieved via recognition of so-called degradation motifs-degrons-that are assumed to become exposed as a result of protein misfolding. Despite their importance, the nature and sequence properties of quality-control degrons remain elusive. Here, we have used data from a yeast-based screen of 23,600 17-residue peptides to build a predictor of quality-control degrons. The resulting model, QCDPred (Quality Control Degron Prediction), achieves good accuracy using only the sequence composition of the peptides as input. Our analysis reveals that strong degrons are enriched in hydrophobic amino acids and depleted in negatively charged amino acids, in line with the expectation that they are buried in natively folded proteins. We applied QCDPred to the entire yeast proteome, enabling us to analyse more widely the potential effects of degrons. As an example, we show a correlation between cellular abundance and degron potential in disordered regions of proteins. Together with recent results on membrane proteins, our work suggest that the recognition of exposed hydrophobic residues is a key and generic mechanism for proteome homeostasis.

Conserved degronome features governing quality control associated proteolysis

“…We, therefore, opted to employ a machine-learning algorithm that we have established by using the PSI data to train a logistic regression model (Johansson et al, 2022). QCDPred provided each amino acid with a unique value, based on its predicted contribution to degron potency (Johansson et al, 2022). Consequently, protein stability maps were formed for all proteins comprising the tested library that attained 100% coverage (N = 306), each includes experimental PSI values for each peptide, average PSI values for each amino acid, and QCDPred probability scores ( Supplemental Figure 2 ).…”

“…This implies that for most QCAP degrons to become active, the protein must be structurally perturbed so that the degron is exposed. Indeed, we have found that for disordered proteins and regions there is a correlation between the presence of predicted degrons and the abundance and half-lives of the proteins (Johansson et al, 2022). Altogether, our data indicate that QCAP degrons are enriched in bulky hydrophobic TMD-like entities.…”

“…Employing various search tools, we could not observe linear amino acid sequence motifs or C- terminal amino acid preferences within the examined degrons, (see also (Geffen et al, 2016). We, therefore, opted to employ a machine-learning algorithm that we have established by using the PSI data to train a logistic regression model (Johansson et al, 2022). QCDPred provided each amino acid with a unique value, based on its predicted contribution to degron potency (Johansson et al, 2022).…”

“…A breakthrough in degron discovery has been provided by the development of the pioneering GPS-peptidome methods to study protein degradation and identify degron motifs (Koren et al, 2018; Lin et al, 2018; Tokheim et al, 2021). By implementing GPS-P in yeast we have here identified a large cohort of authentic degrons that was subsequently used to train a degron prediction algorithm (Johansson et al, 2022). Thus, this large group of peptides that determine a variety of half-lives, provide consensus degron features that are compatible with TMD properties.…”

“…By employing a peptide library fused to yGPS-P, we have identified multiple degron sequences that were subsequently analyzed using a machine learning algorithm. The resulting computer program, termed Quality Control Degron Prediction (QCDPred) (Johansson et al, 2022) revealed amino acid and secondary structure preferences in QCAP degrons. The determined degron features were highly dependent on the overall hydrophobicity, and consistently transmembrane domains (TMDs) exhibit extreme degron potency, signifying their critical role in the degradation of integral membrane proteins prevented from entering the secretory pathway.…”