Protein stability is affected in several diseases and is of substantial interest in efforts to correlate genotypes to phenotypes. Superoxide dismutase 1 (SOD1) is a suitable test case for such correlations due to its abundance, stability, available crystal structures and thermochemical data, and physiological importance. In this work, stability changes of SOD1 mutations were computed with five methods, CUPSAT, I-Mutant2.0, I-Mutant3.0, PoPMuSiC, and SDM, with emphasis on structural sensitivity as a potential issue in structure-based protein calculation. The large correlation between experimental literature data of SOD1 dimers and monomers (r = 0.82) suggests that mutations in separate protein monomers are mostly additive. PoPMuSiC was most accurate (typical MAE ∼ 1 kcal/mol, r ∼ 0.5). The relative performance of the methods was not very structure-dependent, and the more accurate methods also displayed less structural sensitivity, with the standard deviation from different high-resolution structures down to ∼0.2 kcal/mol. Structures of variable resolution and number of protein copies locally affected specific sites, emphasizing the use of state-relevant crystal structures when such sites are of interest, but had little impact on overall batch estimates. Protein-interaction effects (as a mimic of crystal packing) were small for the more accurate methods. Thus, batch computations, relevant to, e.g., comparisons of disease/nondisease mutant sets or different clades in phylogenetic trees, are much more significant than single mutant calculations and may be the only meaningful way to computationally bridge the genotype–phenotype gap of proteomics. Finally, mutations involving glycine were most difficult to model, of relevance to future method improvement. This could be due to structure changes (glycine has a low structural propensity) or water colocalization with glycine.
Journal of Physical Chemistry B, 2014, Vol 118, Issue 7, p. 1799-1812