Markov models of amino acid substitution to study proteins with intrinsically disordered regions

Background

Intrinsically disordered proteins (IDPs) or proteins with disordered regions(IDRs) do not have a well-defined tertiary structure, but perform amultitude of functions, often relying on their native disorder to achievethe binding flexibility through changing to alternative conformations.Intrinsic disorder is frequently found in all three kingdoms of life, andmay occur in short stretches or span whole proteins. To date most studiescontrasting the differences between ordered and disordered proteins focusedon simple summary statistics. Here, we propose an evolutionary approach tostudy IDPs, and contrast patterns specific to ordered protein regions andthe corresponding IDRs.

Results

Two empirical Markov models of amino acid substitutions were estimated, basedon a large set of multiple sequence alignments with experimentally verifiedannotations of disordered regions from the DisProt database of IDPs. Weapplied new methods to detect differences in Markovian evolution andevolutionary rates between IDRs and the corresponding ordered proteinregions. Further, we investigated the distribution of IDPs among functionalcategories, biochemical pathways and their preponderance to contain tandemrepeats.

Conclusions

We find significant differences in the evolution between ordered anddisordered regions of proteins. Most importantly we find that disorderpromoting amino acids are more conserved in IDRs, indicating that in somecases not only amino acid composition but the specific sequence is importantfor function. This conjecture is also reinforced by the observation that for of our data set IDRs evolve more slowly than theordered parts of the proteins, while we still support the common view thatIDRs in general evolve more quickly. The improvement in model fit indicatesa possible improvement for various types of analyses e.g. denovo disorder prediction using a phylogenetic Hidden MarkovModel based on our matrices showed a performance similar to other disorderpredictors.