Naganathan Athi N.

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The functioning of proteins is intimately tied to their fluctuations in the native ensemble. The structural-energetic features that determine fluctuation amplitudes and hence the shape of the underlying landscape, which in turn determine the magnitude of the functional output, are often confounded by multiple variables. Here, we employ the FF1 domain from human p190A RhoGAP protein as a model system to uncover the molecular basis for phosphorylation of a buried tyrosine, which is crucial to the transcriptional activity associated with transcription factor TFII-I. Combining spectroscopy, calorimetry, statistical-mechanical modeling, molecular simulations, and in vitro phosphorylation assays, we show that the FF1 domain samples a diverse array of conformations in its native ensemble, some of which are phosphorylation-competent. Upon eliminating unfavorable charge-charge interactions through a single charge-reversal (K53E) or charge-neutralizing (K53Q) mutation, we observe proportionately lower phosphorylation extents due to the altered structural coupling, damped equilibrium fluctuations, and a more compact native ensemble. We thus establish a conformational selection mechanism for phosphorylation in the FF1 domain with K53 acting as a “gatekeeper”, modulating the solvent exposure of the buried tyrosine. Our work demonstrates the role of unfavorable charge-charge interactions in governing functional events through the modulation of native ensemble characteristics, a feature that could be prevalent in ordered protein domains.

The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances - a measure of the extent of percolation on perturbation - and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.

The high flexibility of long disordered or partially structured loops in folded proteins allows for entropic stabilization of native ensembles. Destabilization of such loops could alter the native ensemble or promote alternate conformations within the native ensemble if the ordered regions themselves are held together weakly. This is particularly true of downhill folding systems that exhibit weak unfolding cooperativity. Here, we combine experimental and computational methods to probe the response of the native ensemble of a helical, downhill folding domain PDD, which harbors an 11-residue partially structured loop, to perturbations. Statistical mechanical modeling points to continuous structural changes on both temperature and mutational perturbations driven by entropic stabilization of partially structured conformations within the native ensemble. Long time-scale simulations of the wild-type protein and two mutants showcase a remarkable conformational switching behavior wherein the parallel helices in the wild-type protein sample an antiparallel orientation in the mutants, with the C-terminal helix and the loop connecting the helices displaying high flexibility, disorder, and non-native interactions. We validate these computational predictions via the anomalous fluorescence of a native tyrosine located at the interface of the helices. Our observations highlight the role of long loops in determining the unfolding mechanisms, sensitivity of the native ensembles to mutational perturbations and provide experimentally testable predictions that can be explored in even two-state folding systems.

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 PPAγLBDs. Our results illustrate how functional requirements can drive the evolution of conformationally diverse native ensembles in paralogs.

Single domain proteins fold via diverse mechanisms emphasizing the intricate relationship between energetics and structure, which is a direct consequence of functional constraints and demands imposed at the level of sequence. On the other hand, elucidating the interplay between folding mechanisms and function is challenging in large proteins, given the inherent shortcomings in identifying metastable states experimentally and the sampling limitations associated with computational methods. Here, we show that free energy profiles and surfaces of large systems (>150 residues), as predicted by a statistical mechanical model, display a wide array of folding mechanisms with ubiquitous folding intermediates and heterogeneous native ensembles. Importantly, residues around the ligand binding or enzyme active site display a larger tendency to partially unfold and this manifests as intermediates or excited states along the folding coordinate in ligand binding domains, transcription repressors, and representative enzymes from all the six classes, including the SARS-CoV-2 receptor binding domain (RBD) of the spike protein and the protease Mpro. It thus appears that it is relatively easier to distill the imprints of function on the folding landscape of larger proteins as opposed to smaller systems. We discuss how an understanding of energetic-entropic features in ordered proteins can pinpoint specific avenues through which folding mechanisms, populations of partially structured states and function can be engineered.

Paralogous proteins play a vital role in evolutionary adaptation of organisms and species divergence. One outstanding question is the molecular basis for how folding mechanisms differ in paralogs that not only exhibit similar topologies but also evolve under near-identical selection pressures. Here, we address this question by studying a paralogous protein pair from enterobacteria, Hha and Cnu, combining experiments, simulations and statistical modeling. We find that Hha is less stable and folds an order of magnitude slower than Cnu despite similar packing and topological features. Differences in surface charge–charge interactions, however, promote a N-terminal biased unfolding mechanism in Hha unlike Cnu that unfolds via the C terminus. Our work highlights how electrostatic frustration contributes to the population of heterogeneous native ensembles in paralogs and the avenues through which evolutionary topological constraints could be overcome by modulating charge–charge interactions.

The nature and distribution of charged residues on the surface of proteins play a vital role in determining the binding affinity, selectivity and kinetics of association to ligands. When it comes to DNA-binding domains (DBDs), these functional features manifest as anisotropic distribution of positively charged residues on the protein surface driven by the requirement to bind DNA, a highly negatively charged polymer. In this work, we compare the thermodynamic behavior of nine different proteins belonging to three families - LacR, engrailed and Brk - some of which are disordered in solution in the absence of DNA. Combining detailed electrostatic calculations and statistical mechanical modeling of folding landscapes at different distances and relative orientations with respect to DNA, we show that non-specific electrostatic interactions between the protein and DNA can promote structural transitions in DBDs. Such quinary interactions that are strictly agnostic to the DNA sequence induce varied behaviors including folding of disordered domains, partial unfolding of ordered proteins and (de-)population of intermediate states. Our work highlights that the folding landscape of proteins can be tuned as a function of distance from DNA and hints at possible reasons for DBDs exhibiting complex kinetic-thermodynamic behaviors in the absence of DNA.