Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics
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 only the protein amino acid sequence, without taking into account likely emergent properties that are sequence context dependent, such as protein backbone dynamics. The DisoMine method predicts protein disorder with recurrent neural networks not directly from the amino acid sequence, but instead from more generic predictions of key biophysical properties, here 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 compares well to 10 state of the art predictors, also if these use evolutionary information. DisoMine is freely available through an interactive webserver at http://bio2byte.com/disomine/