Computational Prediction of Intrinsic Disorder in Proteins
Document Type
Article
Publication Date
2017
Keywords
intrinsic disorder, intrinsically disordered protein, prediction
Digital Object Identifier (DOI)
https://doi.org/10.1002/cpps.28
Abstract
Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides details about three accurate and recently released methods. We demonstrate the use of these methods to predict intrinsic disorder for several illustrative proteins, provide insights into how predictions should be interpreted, and quantify and discuss predictive performance. Predictions can be freely and conveniently obtained using webservers. We point to the availability of databases that provide access to annotations of intrinsic disorder determined by structural studies and putative intrinsic disorder pre-computed by computational methods. Lastly, we also summarize experimental methods that can be used to validate computational predictions. © 2017 by John Wiley & Sons, Inc.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Current Protocols in Protein Science, v. 88, issue 1, p. 2.16.1-2.16.14
Scholar Commons Citation
Meng, Fanchi; Uversky, Vladimir; and Kurgan, Lukasz, "Computational Prediction of Intrinsic Disorder in Proteins" (2017). Molecular Medicine Faculty Publications. 322.
https://digitalcommons.usf.edu/mme_facpub/322
