SGnn
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 c...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:271670 |
| Acesso em linha: | https://ddd.uab.cat/record/271670 https://dx.doi.org/urn:doi:10.3389/fmolb.2021.718301 |
| Access Level: | acceso abierto |
| Palavra-chave: | Stress granules Prion-like domains Protein aggregation Yeast prions Bioinformatics Machine learning |
| Resumo: | 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. |
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