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Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies

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Atkins, J. D., Boateng, S. Y., Sorensen, T. and McGuffin, L. J. (2015) Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies. International Journal of Molecular Sciences, 16 (8). pp. 19040-19054. ISSN 1422-0067

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To link to this item DOI: 10.3390/ijms160819040

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:41337
Uncontrolled Keywords:intrinsic disorder; disorder prediction methods; types of disorder; structural bioinformatics
Additional Information:Special Issue: In-Silico Prediction and Characterization of Intrinsic Disorder in Proteins
Publisher:MDPI

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