  Accurate prediction of protein stability changes upon single-site variations
(DDG) is important for protein design, as well as our understanding of the
mechanism of genetic diseases. The performance of high-throughput computational
methods to this end is evaluated mostly based on the Pearson correlation
coefficient between predicted and observed data, assuming that the upper bound
would be 1 (perfect correlation). However, the performance of these predictors
can be limited by the distribution and noise of the experimental data. Here we
estimate, for the first time, a theoretical upper-bound to the DDG prediction
performances imposed by the intrinsic structure of currently available DDG
data. Given a set of measured DDG protein variations, the theoretically best
predictor is estimated based on its similarity to another set of experimentally
determined DDG values. We investigate the correlation between pairs of measured
DDG variations, where one is used as a predictor for the other. We analytically
derive an upper bound to the Pearson correlation as a function of the noise and
distribution of the DDG data. We also evaluate the available datasets to
highlight the effect of the noise in conjunction with DDG distribution. We
conclude that the upper bound is a function of both uncertainty and spread of
the DDG values, and that with current data the best performance should be
between 0.7-0.8, depending on the dataset used; higher Pearson correlations
might be indicative of overtraining. It also follows that comparisons of
predictors using different datasets are inherently misleading.
