Rapid Prediction and Analysis of Protein Intrinsic Disorder
Document Type
Article
Publication Date
2022
Digital Object Identifier (DOI)
https://doi.org/10.1002/pro.4496
Abstract
Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Protein Science, v. 31, issue 12, art. e4496
Scholar Commons Citation
Dayhoff, Guy W. II and Uversky, Vladimir N., "Rapid Prediction and Analysis of Protein Intrinsic Disorder" (2022). Molecular Medicine Faculty Publications. 1025.
https://digitalcommons.usf.edu/mme_facpub/1025
