Decoding the selectivity of intrinsically disordered region interactions

A substantial portion of the protein kingdom consists of intrinsically disordered regions (IDRs) that do not fold into well-defined 3D structures yet perform numerous biological functions and are associated with a broad range of diseases. It has been a long-standing enigma how different IDRs successfully execute their specific functions. Further putting a spotlight on IDRs are recent discoveries of functionally relevant biomolecular assemblies, which in many cases form through liquid-liquid phase separation. At the molecular level, the formation of these biomolecular assemblies is largely driven by weak, multivalent, but selective IDR-IDR interactions, which are distinct from the folding-upon-binding process that a number of IDRs are known to undergo while forming complexes with fixed stoichiometries. Deciphering the multivalent interaction behaviors of IDRs in the context of biomolecular assembly formation, especially their selectivity of interaction partners, is key to understanding IDR functions. Emerging experimental and computational studies have indicated that amino acid sequence-derived features of IDRs may encode molecular recognition between different IDRs. However, the currently available information is far from sufficient to build a comprehensive sequence-interaction-function paradigm for IDRs. It remains completely elusive how the interaction selectivity of an IDR is encoded in its sequence and how the selectivity impacts the cellular functions of IDRs. 

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Figure adapted from Chong et al. (2021), J. Mol. Biol. 433, 166724

We will probe the IDR sequence-function relationships in the context of mammalian transcriptional regulation using a combination of quantitative single-cell imaging, proteomic, and bioinformatic approaches. We aim to reveal the interaction selectivity of a host of different IDRs involved in transcription, identify sequence-derived features of IDRs that determine the selectivity, and understand the functional consequences of the selective interactions. The findings we make about transcription protein IDRs are likely applicable to IDRs that are involved in other cellular processes besides transcription. Our ultimate goal is to build a model that predicts the interaction partners of an IDR from its amino acid sequence and achieve de novo design of IDRs with any defined functions.