Dataset Information

Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins.

ABSTRACT: Resonance assignment is a prerequisite for almost any NMR-based study of proteins. It can be very challenging in some cases, however, due to the nature of the protein under investigation. This is the case with intrinsically disordered proteins, for example, whose NMR spectra suffer from low chemical shifts dispersion and generally low resolution. For these systems, sequence specific assignment is highly time-consuming, so the prospect of using automatic strategies for their assignment is very attractive. In this article we present a new version of the automatic assignment program TSAR dedicated to intrinsically disordered proteins. In particular, we demonstrate how the automatic procedure can be improved by incorporating methods for amino acid recognition and information on chemical shifts in selected amino acids. The approach was tested in silico on 16 disordered proteins and experimentally on ?-synuclein, with remarkably good results.

SUBMITTER: Piai A

PROVIDER: S-EPMC4824835 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

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Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins.

Piai Alessandro A Gonnelli Leonardo L Felli Isabella C IC Pierattelli Roberta R Kazimierczuk Krzysztof K Grudziąż Katarzyna K Koźmiński Wiktor W Zawadzka-Kazimierczuk Anna A

Journal of biomolecular NMR 20160218 3

Resonance assignment is a prerequisite for almost any NMR-based study of proteins. It can be very challenging in some cases, however, due to the nature of the protein under investigation. This is the case with intrinsically disordered proteins, for example, whose NMR spectra suffer from low chemical shifts dispersion and generally low resolution. For these systems, sequence specific assignment is highly time-consuming, so the prospect of using automatic strategies for their assignment is very at ...[more]

PMID: 26891900

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Project description:Intrinsically disordered proteins (IDPs) or proteins with disordered regions (IDRs) do not have a well-defined tertiary structure, but perform a multitude of functions, often relying on their native disorder to achieve the binding flexibility through changing to alternative conformations. Intrinsic disorder is frequently found in all three kingdoms of life, and may occur in short stretches or span whole proteins. To date most studies contrasting the differences between ordered and disordered proteins focused on simple summary statistics. Here, we propose an evolutionary approach to study IDPs, and contrast patterns specific to ordered protein regions and the corresponding IDRs.Two empirical Markov models of amino acid substitutions were estimated, based on a large set of multiple sequence alignments with experimentally verified annotations of disordered regions from the DisProt database of IDPs. We applied new methods to detect differences in Markovian evolution and evolutionary rates between IDRs and the corresponding ordered protein regions. Further, we investigated the distribution of IDPs among functional categories, biochemical pathways and their preponderance to contain tandem repeats.We find significant differences in the evolution between ordered and disordered regions of proteins. Most importantly we find that disorder promoting amino acids are more conserved in IDRs, indicating that in some cases not only amino acid composition but the specific sequence is important for function. This conjecture is also reinforced by the observation that for of our data set IDRs evolve more slowly than the ordered parts of the proteins, while we still support the common view that IDRs in general evolve more quickly. The improvement in model fit indicates a possible improvement for various types of analyses e.g. de novo disorder prediction using a phylogenetic Hidden Markov Model based on our matrices showed a performance similar to other disorder predictors.

Dataset Information

Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins.

Publications

Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins.

Similar Datasets

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