Quantification of Conformational Entropy Unravels Effect of Disordered Flanking Region in Coupled Folding and Binding

Abstract

Intrinsic disorder (ID) constitutes a new dimension to the protein structure−function relationship. The ability to undergo conformational changes upon binding is a key property of intrinsically disordered proteins and remains challenging to study using conventional methods. A 1994 paper by R. S. Spolar and M. T. Record presented a thermodynamic approach for estimating changes in conformational entropy based on heat capacity changes, allowing quantification of residues folding upon binding. Here, we adapt the method for studies of intrinsically disordered proteins. We integrate additional data to provide a broader experimental foundation for the underlying relations and, based on >500 protein−protein complexes involving disordered proteins, reassess a key relation between polar and nonpolar surface area changes, previously determined using globular protein folding. We demonstrate the improved suitability of the adapted method to studies of the folded αα-hub domain RST from radical-induced cell death 1, whose interactome is characterized by ID. From extensive thermodynamic data, quantifying the conformational entropy changes upon binding, and comparison to the NMR structure, the adapted method improves accuracy for ID-based studies. Furthermore, we apply the method, in conjunction with NMR, to reveal hitherto undetected effects of interaction−motif context. Thus, inclusion of the disordered context of the DREB2A RST-binding motif induces structuring of the binding motif, resulting in major enthalpy−entropy compensation in the interaction interface. This study, also evaluating additional interactions, demonstrates the strength of the ID-adapted Spolar−Record thermodynamic approach for dissection of structural features of ID-based interactions, easily overlooked in traditional studies, and for translation of these into mechanistic knowledge.

αα-hub coregulator structure and flexibility determine transcription factor binding and selection in regulatory interactomes

“…Compared with the relaxation rate profiles of At RCD1–RST ( Fig. S2 ) ( 39 ), while mostly similar, marginally higher R 1 rates coupled with generally lower R 2 rates of At TAF4–RST suggested a faster global tumbling rate of the At TAF4–RST domain, possibly caused by a slightly more compact structure. This is in accordance with SAXS-derived R g s and structural alignments ( Fig.…”

“…However, the At TAF4–RST binding-induced α-helix was shorter than when in complex with At RCD1–RST ( 14 , 39 ). In addition, the secondary chemical shift of the highly conserved F259 ( 28 ), which forms extended structure in complex with At RCD1–RST ( 39 ), did not exhibit the same behavior in complex with At TAF4–RST. Peaks belonging to residues M258 and F259 were very weak or nonexistent indicating that the two residues were dynamic in the At TAF4–RST complex.…”

“…C , At TAF4–RST 15 N,H N CSPs ( gray bars ) induced upon binding of At DREB2A 243–272 shown along with At RCD1–RST ( hollow bars , data from Ref. ( 39 )) using TAF4–RST residue numbering. D , At DREB2A 15 N,H N CSPs for binding At TAF4–RST ( gray bars ) ( Fig.…”

“…S6 ) and At RCD1–RST ( hollow bars , data from Ref. ( 39 )). The sequence of At DREB2A is shown at the top .…”

Drugging Fuzzy Complexes in Transcription

“…Adding to the difficulty of finding "druggable" sites to target, fuzzy features are challenging to capture with modern structural approaches like crystallography, as a static "snapshot" of the protein does not adequately reflect its structural multiplicity. NMR spectroscopy and molecular dynamics have shown significant promise in examining fuzzy complexes and their conformational ensembles (Schneider et al, 2015;Delaforge et al, 2018;Theisen et al, 2021), but critical information about any stable or metastable "cryptic" druggable pockets (Vajda et al, 2018;Mizukoshi et al, 2020) is often not a point of focus, even when they may be observable (Scholes and Weinzierl, 2016). The molecular recognition data that remains critical to structure-based drug design is not available for fuzzy TF complexes, stymying many drug discovery efforts (Scott et al, 2016).…”