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These two transformations thus allow better isolation of locally disrupted regions.
The RMSDs calculated in the least flexible regions, buried, or located in the alpha helices and beta sheets are small and have lower variability (0.21 Å ± 0.19, 0.22 Å ± 0.19, 0.19 Å ± 0.14, respectively) compared to all RMSDs (0.24 Å ± 0.25). Local disruptions of the main chain in these regions are thus more difficult to isolate, but as we take into account the flexibility of the proteins in our transformations, we can extract disruptions in these regions that, although small, are significant because the variability is low. For example, 17% of the residues are in beta strands in the whole sample. Among the segments with the largest 5% RMSD, the proportion of residues located in beta strands falls to 9%, while for the smallest 5% p-rank, it rises to 17%. We have thus effectively eliminated the bias due to the intrinsic rigidity of the beta sheets.
2.7. Conclusion
The methodology presented allows the identification of disturbed regions in protein structures by taking into account biases due to experimental variations and protein flexibility. Now that we know that mutations do indeed disrupt the main chain and that these disruptions are measurable with current techniques, it would be interesting to model them, especially to improve the predictions of ΔΔG, for which the carbon chain is rigid.
Two models exist for the accommodation of the main chain under the effect of amino acid substitution. The first (Davis et al. 2006) is derived from the observation of alternative atomic positions in ultra-high resolution crystallographic structures. It has been successfully used to improve Rosetta’s calculation of ΔΔG (Lauck et al. 2010). The second (Bordner and Abagyan 2004) was constructed from data collected on 2,141 pairs of protein structures, only differing by a single point mutation. This model also improved Gibbs’ prediction of free energy after a mutation. The selection method presented allows the identification of fragments where the main chain was more disrupted than expected. Using this database instead of the previous ones should improve the models.
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