Accurate prediction of protein stability changes upon single-site variations (ΔΔG) 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 ΔΔG prediction performances imposed by the intrinsic structure of currently available ΔΔG data.
Montanucci, L., Martelli, P.L., Ben-Tal, N., Fariselli, P. (2019). A natural upper bound to the accuracy of predicting protein stability changes upon mutations. BIOINFORMATICS, 35(9), 1513-1517 [10.1093/bioinformatics/bty880].
A natural upper bound to the accuracy of predicting protein stability changes upon mutations
Martelli, Pier Luigi;
2019
Abstract
Accurate prediction of protein stability changes upon single-site variations (ΔΔG) 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 ΔΔG prediction performances imposed by the intrinsic structure of currently available ΔΔG data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
