Exploiting the Link between Protein Rigidity and Thermostability for Data‐Driven Protein Engineering

Abstract

Understanding and exploiting the relationship between microscopic structure and macroscopic stability is important for developing strategies to improve protein stability at high temperatures. The thermostability of proteins has been repeatedly linked to an enhanced structural rigidity of the folded native state. In the current study, the rigidity of protein structures from mesophilic and thermophilic organisms along a thermal unfolding trajectory is directly probed. In order to perform this, protein structures were modeled as constraint networks, and the rigidity in these networks was quantified using the Floppy Inclusion and Rigid Substructure Topography (FIRST) method. During the thermal unfolding, a phase transition was observed that defines the rigidity percolation threshold and corresponds to the folded‐unfolded transition in protein folding. Using concepts from percolation theory and network science, a higher phase transition temperature was observed for ca. two‐thirds of the proteins from thermophilic organisms compared to their mesophilic counterparts, when applied to a data set of 20 pairs of homologues. From both the analysis of the microstructure of the constraint networks and monitoring the macroscopic behavior during the thermal unfolding, direct evidence was found for the “corresponding states” concept, which states that mesophilic and thermophilic enzymes are in corresponding states of similar flexibility at their respective optimal temperature. Finally, the current approach facilitated the identification of structural features from which a destabilization of the structure originates upon thermal unfolding. These predictions show a good agreement with the experimental data. Therefore, the information might be exploited in data‐driven protein engineering by pointing to residues that should be varied to obtain a protein with higher thermostability.

Statics of Biomacromolecules

“…Notably, P 1 curves of proteins are similar to P 1 curves observed for network models of glasses and amorphous solids [59,60]. Likewise, homologous proteins have P 1 curves of very similar shape (Figure 18.3a) [27,60].…”

“…CNA functions as a frontend to the FIRST software and allows: (i) the setting up of a variety of constraint network representations for rigidity analysis (see also below); (ii) processing of the results obtained from FIRST; and (iii) calculating the different indices for characterizing biomacromolecular stability, both globally and locally (see Section 18.2.5). CNA can be used to carry out thermal unfolding simulations by gradually removing noncovalent constraints from the initial network representation (see above) [27,29,[51][52][53]. That is, for a given network state s ¼ f(T), hydrogen bonds (including salt bridges) with an energy E HB > E cut,s are removed from the network [54].…”

“…This follows the idea that stronger hydrogen bonds will break at higher temperatures than weaker ones. To convert the original, geometry-based hydrogen bond energy scale E HB [54] into a temperature scale T, Radestock and Gohlke proposed a simple linear fit by comparing computed phase-transition temperatures for pairs of homologous mesophilic and thermophilic proteins with experimental melting temperatures [27]. The number of hydrophobic contacts is kept constant during the thermal unfolding, because the strength of hydrophobic interactions remains constant or even increases with increasing temperature [55].…”

“…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].…”

An Interfacial Thermodynamics Model for Protein Stability

“…Nevertheless, the RCD is useful to understand long-time scales, where small amplitude conformational deviations in substructures within a protein are neglected, such as the compression, elongation, bending or twisting of an alpha-helix. The rapid calculations for the RCD by FIRST (requiring tiny fractions of a second) has proved to be useful in making comparative studies across protein families, and to elucidate common structural features regarding flexibility important to function Rader, et al 2004;Fuxreiter, et al 2005;Costa, et al 2006;Radestock & Gohlke 2008;Mamonova et al 2008;Rader, 2010;Heal, et al 2011;Radestock & Gohlke 2011]. It has also been shown there is a statistically significant correlation between the propagation of rigidity between two mutation sites within a protein to non-additive effects in free energy cycles describing double mutant studies [Istomin, et al 2008].…”