Conserved quantitative stability/flexibility relationships (QSFR) in an orthologous RNase H pair

Abstract

Many reports qualitatively describe conserved stability and flexibility profiles across protein families, but biophysical modeling schemes have not been available to robustly quantify both. Here we investigate an orthologous RNase H pair by using a minimal distance constraint model (DCM). The DCM is an all atom microscopic model [Jacobs and Dallakyan, Biophys J 2005;88(2):903-915] that accurately reproduces heat capacity measurements [Livesay et al., FEBS Lett 2004;576(3):468-476], and is unique in its ability to harmoniously calculate thermodynamic stability and flexibility in practical computing times. Consequently, quantified stability/flexibility relationships (QSFR) can be determined using the DCM. For the first time, a comparative QSFR analysis is performed, serving as a paradigm study to illustrate the utility of a QSFR analysis for elucidating evolutionarily conserved stability and flexibility profiles. Despite global conservation of QSFR profiles, distinct enthalpy-entropy compensation mechanisms are identified between the RNase H pair. In both cases, local flexibility metrics parallel H/D exchange experiments by correctly identifying the folding core and several flexible regions. Remarkably, at appropriately shifted temperatures (e.g., melting temperature), these differences lead to a global conservation in Landau free energy landscapes, which directly relate thermodynamic stability to global flexibility. Using ensemble-based sampling within free energy basins, rigidly, and flexibly correlated regions are quantified through cooperativity correlation plots. Five conserved flexible regions are identified within the structures of the orthologous pair. Evolutionary conservation of these flexibly correlated regions is strongly suggestive of their catalytic importance. Conclusions made herein are demonstrated to be robust with respect to the DCM parameterization.

New insight into long‐range nonadditivity within protein double‐mutant cycles

“…A few factors possibly decreasing the reliability are: (i) the same cut-off Hbond energy, E c , was used for all proteins in the dataset, without optimizing E c for each protein structure; (ii) the variability in how the constraint topology is defined through hydrophobic, H-bond, and torsion constraints; (iii) only wild type protein rigidity characteristics are considered without concern for the structural perturbation the mutation will induce; and (iv) no account of electrostatic influences. In future work, we will attempt to improve the predictive power by employing a thermodynamic Gibbs ensemble of rigid cluster decompositions using the DCM, which provides thermodynamically averaged statistical information on residue to residue rigidity correlations that are conveyed in molecular cooperativity plots (25,26).…”

“…The only exception we are aware of that goes beyond the usual additivity assumption is the Distance Constraint Model (DCM) that augments concepts from constraint theory to a free energy decomposition scheme (22). The DCM is an all-atom statistical mechanical model that explicitly accounts for nonadditivity in conformational entropy; it has been able to successfully quantify protein stability and flexibility relationships (23)(24)(25)(26), as well as unfolding pathways and nonadditivity effects in free energy upon structural reconstitution of thioredoxin (25). The degree of nonadditivity upon reconstitution was found to linearly correlate with the degree of mechanical rigidity within the protein (25).…”

An Interfacial Thermodynamics Model for Protein Stability

“…Moreover, the DCM predicts substructures within a protein that are rigid or flexible, and identifies sets of atoms that are co-rigid or co-flexible within a correlated motion. Many studies on proteins using a minimal DCM (mDCM) have elucidated stability/flexibility relationships important to function [Livesay & Jacobs, 2006;Livesay, et al 2008;Mottonen, et al 2009;Verma, et al 2010] including the study of allostery [Mottonen, et al 2010]. The DCM provides a good estimate for conformational entropy in simple loop systems compared to exact calculations .…”

Statics of Biomacromolecules

“…It should be noted that the distance constraint model (DCM) [28,58] also relies on ensembles of constraint topologies, which are differently generated, however. Here, mean-field probabilities of hydrogen bonds and torsion constraints are used for Monte Carlo sampling to generate such an ensemble, however, assuming that the atom positions of the input structure are unique.…”

“…It should be noted that flexibility and rigidity are static properties -that is, a rigidity analysis determines those parts of a molecule that can potentially move, but says nothing about the direction or amplitude of a motion [22]. The approach has already been applied in several areas of computational biomacromolecular research, including the sampling of biomacromolecular conformational space [23][24][25][26], analyzing structural determinants of thermostability [27,28], identifying folding cores of proteins [29,30], assessing complex structural stability [31,32], linking flexibility and function [33], finding putative binding sites [34], understanding allostery [35,36], investigating large biomacromolecules such as the ribosome [35], and predicting thermodynamic properties [37].…”