Insights into protein structure and function from disorder-complexity space.

Intrinsically disordered proteins have a wide variety of important functional roles. However, the relationship between sequence and function in these proteins is significantly different than that for well-folded proteins. In a previous work, we showed that the propensity to be disordered can be recognized based on sequence composition alone. Here that analysis is furthered by examining the relationship of disorder propensity to sequence complexity, where the metrics for these two properties depend only on composition. The distributions of 40 amino acid peptides from both ordered and disordered proteins are graphed in this disorder-complexity space. An analysis of Swiss-Prot shows that most peptides have high complexity and relatively low disorder. However, there are also an appreciable number of low complexity-high disorder peptides in the database. In contrast, there are no low complexity-low disorder peptides. A similar analysis for peptides in the PDB reveals a much narrower distribution, with few peptides of low complexity and high disorder. In this case, the bounds of the disorder-complexity distribution are well defined and might be used to evaluate the likelihood that a peptide can be crystallized with current methods. The disorder-complexity distributions of individual proteins and sets of proteins grouped by function are also examined. Among individual proteins, there is an enormous variety of distributions that in some cases can be rationalized with regard to function. Groups of functionally related proteins are found to have distributions that are similar within each group but show notable differences between groups. Finally, a pattern matching algorithm is used to search for proteins with particular disorder-complexity distributions. The results suggest that this approach might be used to identify relationships between otherwise dissimilar proteins.

Reduced amino acid alphabet is sufficient to accurately recognize intrinsically disordered protein.

Intrinsically disordered proteins are an important class of proteins with unique functions and properties. Here, we have applied a support vector machine (SVM) trained on naturally occurring disordered and ordered proteins to examine the contribution of various parameters (vectors) to recognizing proteins that contain disordered regions. We find that a SVM that incorporates only amino acid composition has a recognition accuracy of 87+/-2%. This result suggests that composition alone is sufficient to accurately recognize disorder. Interestingly, SVMs using reduced sets of amino acids based on chemical similarity preserve high recognition accuracy. A set as small as four retains an accuracy of 84+/-2%; this suggests that general physicochemical properties rather than specific amino acids are important factors contributing to protein disorder.