Diverse Native Ensembles Dictate the Differential Functional Responses of Nuclear Receptor Ligand-Binding Domains

Abstract

Native states of folded proteins are characterized by a large ensemble of conformations whose relative populations and interconversion dynamics determine the functional output. This is more apparent in transcription factors that have evolved to be inherently sensitive to small perturbations, thus fine-tuning gene expression. To explore the extent to which such functional features are imprinted on the folding landscape of transcription factor ligand-binding domains (LBDs), we characterize paralogous LBDs of the nuclear receptor (NR) family employing an energetically detailed and ensemble-based Ising-like statistical mechanical model. We find that the native ensembles of the LBDs from glucocorticoid receptor, PPAγ, and thyroid hormone receptor display a remarkable diversity in the width of the native wells, the number and nature of partially structured states, and hence the degree of conformational order. Monte Carlo simulations employing the full state representation of the ensemble highlight that many of the functional conformations coexist in equilibrium, whose relative populations are sensitive to both temperature and the strength of ligand binding. Allosteric modulation of the degree of structure at a coregulator binding site on ligand binding is shown to arise via a redistribution of populations in the native ensembles of glucocorticoid and PPAγ LBDs. Our results illustrate how functional requirements can drive the evolution of conformationally diverse native ensembles in paralogs.

Thermodynamic Architecture and Conformational Plasticity of GPCRs

“…The nature and the strength of ligand binding (which is effectively a perturbation to the binding site) could therefore determine the functional output by restricting the accessible conformational space as seen in the energy landscapes of activated receptors. A similar conformational feature has been recently demonstrated in the diverse class of large nuclear receptor ligand binding domains, 55 indicating that ‘conformational selection’ and subsequent enthalpic compensation (via drug-protein contacts or GPCR-G-protein interactions) of entropic stability could be generic features underlying the energy landscapes of proteins and, hence, function.…”

“…Such a 2D landscape has been particularly successful in capturing functionally relevant substates in multiple large water-soluble proteins. 42,54,55 For example, consider the free-energy landscapes of Rhodopsin (GPCR1), the β 2 AR (GPCR3) and the Kappa-type opioid receptor (GPCR8) at 310 K (Figure 3). The native ensembles are broad in the GPCRs considered, but with differences in the extent and nature of structure.…”

Structural–Energetic Basis for Coupling between Equilibrium Fluctuations and Phosphorylation in a Protein Native Ensemble

“…A detailed description of the model can be found in earlier works. − Briefly, the WSME model discretizes the phase space accessible to a protein chain by assuming that every residue can sample either folded-like (binary variable 1 ) or unfolded-like (binary variable 0 ) conformations. The microstates will therefore be represented by strings of ones and zeros.…”

The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics

“…The free-energy landscapes obtained by the WSME model can be combined with Monte Carlo simulations using the Metropolis algorithm to simulate single-molecule trajectories and examine microscopic protein-folding kinetics [ 42 , 45 , 53 , 65 , 71 , 72 , 87 , 88 , 106 , 111 , 112 ]. Since the WSME model is a Gō-type coarse-grained model with a limited number of possible conformations, simulations of protein-folding reactions can be performed with low computational complexity.…”

“…In the previous sections, we showed that the folding mechanisms of small single-domain proteins are described well by the WSME model. By contrast, the folding mechanisms of multi-domain proteins have been less frequently studied because they have complex, multiple folding pathways and intermediates, making it difficult to theoretically predict the folding processes [ 53 , 75 , 78 , 81 , 91 , 106 , 108 , 112 , 113 , 114 , 115 , 116 ]. However, multi-domain proteins comprise the majority of the proteome, and more than 70% of eukaryotic proteins contain multiple domains [ 117 , 118 ].…”