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Int. J. Mol. Sci. 2015, 16(8), 19040-19054; doi:10.3390/ijms160819040

Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

1
School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK
2
Diamond Light Source Ltd., Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Lukasz Kurgan and Vladimir N. Uversky
Received: 21 May 2015 / Revised: 15 July 2015 / Accepted: 4 August 2015 / Published: 13 August 2015
View Full-Text   |   Download PDF [760 KB, uploaded 13 August 2015]   |  

Abstract

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution. View Full-Text
Keywords: intrinsic disorder; disorder prediction methods; types of disorder; structural bioinformatics intrinsic disorder; disorder prediction methods; types of disorder; structural bioinformatics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Atkins, J.D.; Boateng, S.Y.; Sorensen, T.; McGuffin, L.J. Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies. Int. J. Mol. Sci. 2015, 16, 19040-19054.

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