Background Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. Results The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ??G within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. Conclusions We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.
Performance of Web tools for predicting changes in protein stability caused by mutations
Eugenio Del Prete;Angelo Facchiano
2021
Abstract
Background Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. Results The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ??G within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. Conclusions We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.