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Proteins are said to be “marginally stable”: typically, there is a difference of 3–7 kcal/mol in free folding energy (ΔG) between folded and unfolded conformations. Amino acid side chain substitutions thus have a significant effect on protein stability: the effect of a single mutation is on average −0.95 kcal/mol according to the Protherm database (Gromiha and Sarai 2010), which in 2017 included the measurements from ΔΔG for 1,866 proteins with their structure. ΔΔG is the difference between the free folding energy of the native and the mutated protein.

This low protein stability is assumed to be either the result of a balance between function and stability (DePristo et al. 2005), or the result of a balance between destabilizing mutations and highly unstable proteins (Taverna and Goldstein 2002; Bloom et al. 2007; Zeldovich et al. 2007).

Two principles must be kept in mind:

 – function is often dependent on a dynamic effect of structure;

 – any protein must be degradable at a cost that is not restrictive for the cell.

Many algorithms and web servers have been developed to provide an estimate of the variation of Gibbs’ free energy (ΔΔG) under the effect of a point mutation: FoldX (Guerois et al. 2002) and Rosetta (Kellogg et al. 2011) are among the best known. SPROUTS (Lonquety et al. 2009) is a web server combining the results of several methods. These methods try to predict whether a given mutation will be destabilizing, neutral or stabilizing. The comparison between predicted and experimental energy variations gives globally satisfactory results (Figure 2.5(a)): the correlation between the predictions of FoldX and the measurements of Protherm is 0.59. Nevertheless, the difference can be quite large in some cases (Lonquety et al. 2009). It is interesting to note that the prediction results are quite different when comparing wild to mutant and mutant to wild, in absolute value (Figure 2.5(b)).

Figure 2.5. Comparison of predicted ΔΔG by FoldX and those experimentally measured (Protherm)

COMMENT ON FIGURE 2.5. – a) Prediction by FoldX of ΔΔG for 130 proteins present in Protherm and belonging to the 11 families in which at least 20 different point mutant structures are known (see section 2.6 for a description of this dataset). b) Prediction by FoldX of ΔΔG for the families in which at least 20 different point mutant structures are known. The abscissa shows the predicted value of ΔΔG from the native to the mutant, and the ordinate shows the predicted value of ΔΔG from the mutant to the native.