Proteins Structure Function and Bioinformatics · 2018 · 23 citations · 52 references
Life SciencesBiochemistryBioinformaticsProtein FoldingStructural BioinformaticsStructural BiologyMutation ProcessesMutational SignaturesProtein StructureProtein Structure ModelingStructural VariationAmino Acid SubstitutionAmino Acid Substitution EffectsDistortionsP-valuesPoint Mutation EffectsPoint MutationsProtein Structural ChangesProtein Structural Variability
A structural database of 11 families of chains differing by a single amino acid substitution has been built. Another structural dataset of 5 families with identical sequences has been used for comparison. The RMSD computed after a global superimposition of the mutated protein on each native one is smaller than the RMSD calculated among proteins of identical sequences. The effect of the perturbation is very local, and not necessarily the highest at the position of the mutation. A RMSD between mutated and native proteins is computed over a 3-residue or a 7-residue window at each position. To separate the effects of structural fluctuations due to point mutations from other sources, pair RMSD have been translated into P values which themselves are included in a score called P-RANK. This score allows highlighting small backbone distortions by comparing these RMSD between mutated and native positions to the RMSD at the same positions in the absence of a mutation. It results from the P-RANK that 38% of all mutations produce a significant effect on the displacement. When compared with a random distribution of RMSD at un-mutated positions, we show that, even if the RMSD is greater when the mutation is in loops than in regular secondary structure, the relative effect is more important for regular secondary structures and for buried positions. We confirm the absence of correlation between RMSD and the predicted variation of free energy of folding but we found a small correlation between high RMSD and the error in the prediction of ΔΔG.
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