LAUSR

Functionally similar IDPs (Intrinsically Disordered Proteins) often have little sequence similarity. This is in stark contrast to folded proteins and poses a challenge for the inverse problem: functional classification of IDPs using sequence alignment. The problem is further compounded due to the lack of structure in IDPs, preventing structural alignment as an alternate tool for classification. Recent advances in heteropolymer theory unveiled a powerful set of sequence patterning metrics bridging molecular interaction with chain conformation. Focusing only on charge patterning, these set of metrics yield a 'sequence charge decoration matrix' (SCDM). SCDMs can potentially identify functionally similar IDPs, not apparent from sequence alignment alone. Here we illustrate how these information rich 'molecular blueprints' encoded in SCDM can be used for functional classification of IDPs with specific application in three protein families: Ste50, PSC, and RAM, where electrostatics is known to be important. For both the Ste50 and PSC protein family, the set of metrics appropriately classify proteins in functional and non-functional groups in agreement with experiment. Furthermore, our algorithm groups synthetic variants of the disordered RAM region of the Notch receptor protein - important in gene expression - in reasonable accordance with classification based on experimentally measured binding constants of RAM and transcription factor. Taken together, the novel classification scheme reveals the critical role of high dimensional set of metrics - manifest in self-interaction maps/topology - in functional annotation of IDPs even when there is low sequence homology, providing the much needed alternate to a traditional sequence alignment tool.