4.3 Article

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

Journal

ENGINEERING IN LIFE SCIENCES
Volume 8, Issue 5, Pages 507-522

Publisher

WILEY
DOI: 10.1002/elsc.200800043

Keywords

Network; Percolation; Rigidity analysis; Thermophiles

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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.

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