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

International Journal of Molecular Sciences

Jennifer D. Atkins¹  Samuel Y. Boateng¹  Thomas Sorensen³  Liam J. McGuffin¹  Lukasz Kurgan² 

[1] School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK; E-Mails:School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK;;Diamond Light Source Ltd., Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK; E-Mail:

关键词: intrinsic disorder;    disorder prediction methods;    types of disorder;    structural bioinformatics;   

DOI  :  10.3390/ijms160819040

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.