1. ¹College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
2. ²Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
3. ³Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
4. ⁴Institute for Intrinsically Disordered Protein Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA
5. ⁵Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
6. ⁶Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
7. ⁷Institute for Biological Instrumentation, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia

Correspondence: Kejun Wang, Tel: +86-451-8256-9906; Fax: +86-451-8256-9906 E-mail: wangkejun@hrbeu.edu.cn; A Keith Dunker, E-mail: kedunker@iupui.edu

Abstract

The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional "sequencestructurefunction" paradigm. Accurate prediction of a protein's predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.