@article{
author = "Necci, Marco and Piovesan, Damiano and Tosatto, Silvio C. E. and Davidović, Radoslav S. and Veljković, Nevena V.",
year = "2021",
abstract = "Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F max = 0.483 on the full dataset and F max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.",
journal = "Nature Methods",
title = "Critical assessment of protein intrinsic disorder prediction",
volume = "18",
number = "5",
pages = "472-481",
doi = "10.1038/s41592-021-01117-3"
}