Disease-linked mutations cause exposure of a protein quality control degron

Kampmeyer, Caroline; Larsen-Ledet, Sven; Wagnkilde, Morten Rose; Michelsen, Mathias; Iversen, Henriette K.M.; Nielsen, Sofie V.; Lindemose, Søren; Caregnato, Alberto; Ravid, Tommer; Stein, Amelie; Teilum, Kaare; Lindorff‐Larsen, Kresten; Hartmann‐Petersen, Rasmus

doi:10.1016/j.str.2022.05.016

Cited by 24 publications

(32 citation statements)

References 63 publications

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“…Most notably, we find that the presence of the negatively charged side chains in Asp and Glu counteract degradation whereas the positively charged side chains in Lys and Arg have a more neutral effect. These results complement our recent observation that Hsp70 binding motifs, which are often hydrophobic and positively charged, can act as degrons 17, 27 . The effects of hydrophobic, positively and negatively charged residues are also validated by experiments that show that replacing hydrophobic residues with Glu in a degron can prevent degradation, whereas replacing the same amino acids with Arg has a substantially smaller effect 18 .…”

Section: Discussionsupporting

confidence: 90%

“…Our simple, but accurate prediction algorithm to identify degrons from sequence may help understand the complex relationship between sequence variation and abundance. We and others have previously shown that many disease-causing missense variants are thermodynamically destabilized 27,[37][38][39][40][41][42][43] . The results presented here provide a key missing part of the puzzle, namely a quantitative model for which exposed sequences are PQC targets, and we hope our work may help develop quantitative models that combine protein stability and recognition via the PQC.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of quality-control degradation signals in yeast proteins

Johansson

Mashahreh

Hartmann‐Petersen

et al. 2022

Preprint

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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.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Prediction of quality-control degradation signals in yeast proteins

Johansson

Mashahreh

Hartmann‐Petersen

et al. 2022

Preprint

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“…In our experimental systems, the APPY chaperone-binding motif is artificially grafted onto the Ura3-HA-GFP (yeast) and GFP (human) reporters, and since the chaperones cannot refold and bury these fragments, this effectively uncouples degradation from folding, thus allowing us to study the consequences of exposing these regions. Although this model system differs from PQC degron-exposure in a natural full-length misfolded protein, which while still reliant on Hsp70 is indisputably more complex (Kampmeyer et al, 2022), our findings lead us to suggest a model (Fig. 8).…”

Section: Discussionmentioning

confidence: 80%

HSP70-binding motifs function as protein quality control degrons

Abildgaard

Petersen

Larsen

et al. 2021

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Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for degradation through the ubiquitin-proteasome system (UPS). To uncover how PQC degrons function, we performed a screen in Saccharomyces cerevisiae by fusing a library of flexible tetrapeptides to the C-terminus of the Ura3-HA-GFP reporter. The identified degrons exhibited high sequence variation but with marked hydrophobicity. Notably, the best scoring degrons constitute predicted Hsp70-binding motifs. When directly tested, a canonical Hsp70 binding motif (RLLL) functioned as a dose-dependent PQC degron that was targeted by Hsp70, Hsp110, Fes1, several Hsp40 J-domain co-chaperones and the PQC E3 ligase Ubr1. Our results suggest that multiple PQC degrons overlap with chaperone-binding sites and that PQC-linked degradation achieves specificity via chaperone binding. Thus, the PQC system has evolved to exploit the intrinsic capacity of chaperones to recognize misfolded proteins, thereby placing them at the nexus of protein folding and degradation.

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“…Whole cell lysates for western blotting were prepared from exponential phase cultures using glass beads and trichloroacetic acid (TCA) as described before ( Kampmeyer et al, 2022 ). Briefly, 1.2 • 10 8 cells in exponential phase were harvested and washed in water by centrifugation (3000 g, 5 min.).…”

Section: Methodsmentioning

confidence: 99%

Discovering functionally important sites in proteins

Cagiada

Bottaro

Lindemose

et al. 2022

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Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by predicting and validating the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

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Disease-linked mutations cause exposure of a protein quality control degron

Cited by 24 publications

References 63 publications

Prediction of quality-control degradation signals in yeast proteins

Prediction of quality-control degradation signals in yeast proteins

HSP70-binding motifs function as protein quality control degrons

Discovering functionally important sites in proteins

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