Abstract
Proteins bearing prion-like domains (PrLDs) are essential players in stress granules (SG) assembly. Analysis of data on heat stress-induced recruitment of yeast PrLDs to SG suggests that this propensity might be connected with three defined protein biophysical features: aggregation propensity, net charge, and the presence of free cysteines. These three properties can be read directly in the PrLDs sequences, and their combination allows to predict protein recruitment to SG under heat stress. On this basis, we implemented SGnn, an online predictor of SG recruitment that exploits a feed-forward neural network for high accuracy classification of the assembly behavior of PrLDs. The simplicity and precision of our strategy should allow its implementation to identify heat stress-induced SG-forming proteins in complete proteomes.
Highlights
Biomolecular condensates are a group of diverse membraneless organelles formed by the association of proteins that undergo liquid to liquid or liquid to solid phase transitions in the cellular milieu (Banani et al, 2017; Woodruff et al, 2018; Shiina, 2019)
They demonstrated that the Abbreviations: PrLD, prion-like domain; LLPS, liquid-liquid phase separation; NCPR, net charge per residue; AUC, area under the curve; ROC, receiver operating characteristic curve; Stress granules (SG), stress granules; PrD, prion domains; Feed-Forward Neural Network (FFNN), feed-forward neural network
Protein composition-based strategies have shown to be accurate in predicting the assembly behavior of PrLDs in front of heat stress (Boncella et al, 2020)
Summary
Biomolecular condensates are a group of diverse membraneless organelles formed by the association of proteins that undergo liquid to liquid or liquid to solid phase transitions in the cellular milieu (Banani et al, 2017; Woodruff et al, 2018; Shiina, 2019). Stress granules (SG) are a subclass of biological condensates which form in response to different cellular stresses and disassemble when the stress is released, in a dynamic and highly regulated process involving liquid-liquid phase separation (LLPS) reactions (Protter and Parker, 2016; Mahboubi and Stochaj, 2017) They are constituted by selected proteins and mRNAs stalled in translation initiation (Protter and Parker, 2016). Ross and coworkers studied the recruitment of a set of Saccharomyces cerevisiae PrLDs into SG when the cells were heat-stressed (Boncella et al, 2020) They demonstrated that the Abbreviations: PrLD, prion-like domain; LLPS, liquid-liquid phase separation; NCPR, net charge per residue; AUC, area under the curve; ROC, receiver operating characteristic curve; SG, stress granules; PrD, prion domains; FFNN, feed-forward neural network
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