Résumé
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•Intrinsically disordered proteins (IDPs) are largely unstructured proteins that are central to many biological processes.•Here we present Disomine, a novel predictor of protein disordered regions based on Recurrent Neural Network and protein backbone dynamics predictions.•Disomine is a lighting fast methods that takes as input just the target protein sequence, making it suitable for proteome-wide screening of IDPs.•We benchmarked Disomine with many state-of-the-art disorder predictors, showing that it has competitive performance.•DisoMine is freely available through an interactive webserver at http://bio2byte.com/disomine.
The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use the protein amino acid sequence, without taking into account likely sequence-dependent emergent properties, such as protein backbone dynamics.
Here we present DisoMine, a method that predicts protein’long disorder’ with recurrent neural networks from simple predictions of protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine is a top performer in its category and compares well to disorder prediction approaches using evolutionary information.
DisoMine is freely available through an interactive webserver at https://bio2byte.be/disomine/