Intrachain interaction topology can identify functionally similar intrinsically disordered proteins

• Published in: Biophysical journal Vol. 120; no. 10; pp. 1860 - 1868
• Main Authors: Huihui, Jonathan, Ghosh, Kingshuk
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 18-05-2021; The Biophysical Society
• Subjects: Intrinsically Disordered Proteins - genetics; Protein Conformation; Receptors, Notch; Sequence Alignment; Static Electricity
• ISSN: 0006-3495; 1542-0086
• DOI: 10.1016/j.bpj.2020.11.2282

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 because of 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 SCDMs can be used for functional classification of IDPs with specific application in three protein families—Ste50, PSC, and RAM—in which electrostatics is known to be important. For both the Ste50 and PSC protein family, the set of metrics appropriately classifies proteins in functional and nonfunctional 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 a high-dimensional set of metrics—manifest in self-interaction maps and topology—in functional annotation of IDPs even when there is low sequence homology, providing the much-needed alternate to a traditional sequence alignment tool.