Cellular and Molecular Life Sciences

, Volume 74, Issue 17, pp 3069–3090 | Cite as

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

  • Fanchi Meng
  • Vladimir N. Uversky
  • Lukasz KurganEmail author
Multi-author Review


Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein–protein, protein–RNA, and protein–DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.


Intrinsic disorder Prediction Function of disordered proteins Protein–protein interactions Protein–RNA interactions Protein–DNA interactions MoRF SLiM 



This work was supported in part by the National Science Foundation (NSF) Grant 1617369 and the Qimonda Research Chair to L.K., and a scholarship from the China Scholarship Council to F.M.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research InstituteMorsani College of Medicine, University of South FloridaTampaUSA
  3. 3.Institute for Biological InstrumentationRussian Academy of SciencesPushchinoRussian Federation
  4. 4.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA

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