Research Summary: Markov Models of Amino Acid Substitution to Study Proteins with Intrinsically Disordered Regions

ABSTRACT

Background

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.


Results

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.


Conclusions

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.

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Publisher: Public Library of Science

Date Published: 27-May-2011

Author(s): Szalkowski A., Anisimova M.

Link: https://doi.org/10.1371/journal.pone.0020488

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