SGnn is a predictor for the recruitment of prion-like domains (PrLDs) to stress granules upon heat stress.
SGnn (Stress Granules neural network) is a web application that predicts prion-like domain (PrLD) recruitment to stress granules (SG) upon heat stress.
Prion-like proteins have traditionally attracted our attention due to their link to neurodegenerative diseases. However, they have also been identified in a myriad of organisms developing physiological functions. The formation of diverse types of phase-separated assemblies in response to different stimuli is a well-known example of such functionalities. Stress granules are dynamic ribonucleoprotein assemblies formed in response to distinct types of stress, which can reverse to the initial soluble state once the stress is released. A significant fraction of the proteins associated with stress granules contains prion-like domains (PrLDs).
In a recent in vivo study, Boncella and coworkers (2020) characterized yeast PrLDs recruitment to heat-induced stress granules. We analyzed the determinants behind this phenomenon, showing that aggregation propensity, net charge, and cysteine content might be important players in the process. Based on this evidence, we trained a Feed-forward Neural Network to discriminate the assembly competence of PrLDs.
The SGnn web server implements this approach to predict the recruitment of PrLDs to heat-induced stress granules.
Under the "Submission" section, upload a valid FASTA file or paste the sequence(s) in FASTA format in the textbox area. SGnn was trained on previously defined PrLDs. Therefore, it is not intended to work with complete protein sequences. However, tools such as PrionW, PrionScan or PLAAC can predict and delimit the PrLDs in your sequence(s).
Clicking on the "Example" button will preload a set of PrLDs, with experimentally validated propensities to populate heat-induced stress granules.
SGnn will analyze the sequence net charge, cysteine content, and aggregation propensity. This last step is performed using AGGRESCAN software. AGGRESCAN only accepts 20 proteinogenic amino acids and proteins shorter than 6000 residues. Therefore, SGnn will only accept sequences that meet such parameters.
SGnn will display a table with the sequence IDs, the outcome, and the calculated parameters: AGGRESCAN score (Na4vSS), the net charge per residue (NCPR), and the % of Cysteines. On the right side of the screen, an image will summarize the prediction.
PrLD ID: Sequence identifier, tag line.
Predicted recruitment to stress granules: It informs whether SG recruitment upon heat stress is predicted (positive) or not (negative).
Aggrescan score (Na4vSS): Aggregation score obtained from AGGRESCAN software.
NCPR: Net charge per residue.
% Cysteine: Percentage of cysteine resisdues in each sequence.
Users can alternatively download the numerical calculations in machine-readable JSON format or the whole project data in a compressed ZIP folder by clicking the upper left links.
1.- Iglesias V, Santos J, Santos-Suárez J, Pintado-Grima C and Ventura S (2021). SGnn: A Web Server for the Prediction of Prion-Like Domains Recruitment to Stress Granules Upon Heat Stress. Front. Mol. Biosci. 8:718301. doi: 10.3389/fmolb.2021.718301
2.- Boncella, A.E. et al. Composition-based prediction and rational manipulation of prion-like domain recruitment to stress granules. PNAS 2020, 117 (11) 5826-5835.
3.- Conchillo-Sole, O. et al. AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinformatics 2007, 8, 65.
4.- Batlle, C. et al. Prion-like proteins and their computational identification in proteomes. Expert Rev. Proteomics. 2017, 8, 65.