Order and disorder: Protein structure informatics

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  • Published: Apr 15, 2019
  • Author: David Bradley
  • Channels: Chemometrics & Informatics
thumbnail image: Order and disorder: Protein structure informatics

Bioinformatics benchmarking

Aarhus University, Denmark researchers Jakob T. NIelsen and Frans Mulder present benchmark for testing protein disorder prediction programmes. NMR ensemble structure for the core domain of the protein p53 colored according to CheZOD Z-scores. (Image: Jakob Toudahl and Frans Mulder in Scientific Reports)

A bioinformatics approach to understanding disorder in proteins and the implications for disease builds on experimental data from nuclear magnetic resonance (NMR) spectroscopy.

Jakob Nielsen and Frans Mulder of Aarhus University in Denmark have used a comprehensive compilation of experimental NMR data to help them understand whether bioinformatics can be used more effectively to understand disorder in proteins. Structural disorder is critical to form and function in proteins. However, when it goes awry proteins can undergo aggregation and adhere to one another in ways that give rise to problems within the cells of the body or between cells. Such problems are keenly associated with neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease as well as a range of other conditions.

Biological relativity

It is rather difficult to disentangle the characteristics of polypeptides and regions within proteins that give them the tendency to fold incorrectly or otherwise become disordered. It is challenging and time-consuming to extract the necessary information and so bioinformatics methods have been widely used to predict protein disorder from given sequence of amino acids. Indeed, these programs have become indispensable in biomedical research. In recent years, several groups of bioinformatics experts have constructed algorithms to differentiate peptide sequences that will fold from those that do not. These algorithms might be based on various 'features', derived from physicochemical parameters, such as overall charge, localised charge characteristics, the hydrophobicity of a given amino acid or block of amino acids in the sequence, as well as information derived from evolutionary genetics studies that finds the molecular biological relatives.

Prediction programmes

There are now many such prediction programs around, but to know whether or not the theoretical treatment is strong enough to face experimental data and so be useful in the real world of plaques and tangles, it is necessary to have an experimental benchmark to validate and test the predictions a given algorithm might make and so allow it to be optimised for a given group of proteins and their propensity to disorder or otherwise.

In an effort to make such a calibrating benchmark, Nielsen and Mulder have generated and validated a representative experimental set of site-specific and continuous disorder, using published NMR chemical shift data for more than a hundred selected proteins. They next tested the performance of 26 widely used disorder prediction methods. Their results show that these algorithms can vary wildly in their predictions about the same proteins. The team details a thorough comparison in the journal Scientific Reports. They hope the expose will help protein scientists around the globe to make informed choices about which programme is best to use in a given context.

"Our analysis has important implications for the validity and the interpretation of protein disorder, as utilized, for example, in assessing the content of disorder in proteomes," the team writes.

Related Links

Sci Rep 2019, online: "Quality and bias of protein disorder predictors"

Article by David Bradley

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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