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

By Jennifer D. Atkins, Samuel Y. Boateng, Thomas Sorensen, Liam J. McGuffin. 2015

61 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD server provides a single point of access to an integrated suite of six component methods: IntFOLD-TS, for tertiary structure prediction (9–11,18,19); ModFOLD, for 3D model Accuracy Self-Estimate (ASE) scoring (9,20); ReFOLD, for 3D model refinement (9,21); DISOclust, for disorder predict”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, unknown): “Given the large number and diversity of these methods, several relevant reviews and comparative studies were released in the last decade [30,34,32,33,35-40,27,31].”