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

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