PEDS Advance Access published online on June 26, 2009
Protein Engineering Design and Selection, doi:10.1093/protein/gzp030
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Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details
Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel
2 To whom correspondence should be addressed. E-mail: gideon.schreiber{at}weizmann.ac.il
Methods for protein modeling and design advanced rapidly in recent years. At the heart of these computational methods is an energy function that calculates the free energy of the system. Many of these functions were also developed to estimate the consequence of mutation on protein stability or binding affinity. In the current study, we chose six different methods that were previously reported as being able to predict the change in protein stability (
G) upon mutation: CC/PBSA, EGAD, FoldX, I-Mutant2.0, Rosetta and Hunter. We evaluated their performance on a large set of 2156 single mutations, avoiding for each program the mutations used for training. The correlation coefficients between experimental and predicted G values were in the range of 0.59 for the best and 0.26 for the worst performing method. All the tested computational methods showed a correct trend in their predictions, but failed in providing the precise values. This is not due to lack in precision of the experimental data, which showed a correlation coefficient of 0.86 between different measurements. Combining the methods did not significantly improve prediction accuracy compared to a single method. These results suggest that there is still room for improvement, which is crucial if we want forcefields to perform better in their various tasks.
Keywords: computational protein design/energy functions/estimating protein stability/protein engineering
Received June 2, 2009; revised June 2, 2009; accepted June 3, 2009.
1 The two authors contributed equally to this work.