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Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations.

ABSTRACT: Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.

SUBMITTER: Duan J

PROVIDER: S-EPMC7335905 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DeltaDeltaG) upon single point mutation may also help the annotation process. The experimental DeltaDeltaG values are affected by uncertainty as measured by standard deviations. Most of the DeltaDeltaG values are nearly zero (about 32% of the DeltaDeltaG data set ranges from -0.5 to 0.5 kcal/mole) and both the value and sign of DeltaDeltaG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DeltaDeltaG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DeltaDeltaG<-1.0 kcal/mol), stabilizing mutations (DeltaDeltaG>1.0 kcal/mole) and neutral mutations (-1.0</=DeltaDeltaG</=1.0 kcal/mole). RESULTS: In this paper a support vector machine starting from the protein sequence or structure discriminates between stabilizing, destabilizing and neutral mutations. We rank all the possible substitutions according to a three state classification system and show that the overall accuracy of our predictor is as high as 56% when performed starting from sequence information and 61% when the protein structure is available, with a mean value correlation coefficient of 0.27 and 0.35, respectively. These values are about 20 points per cent higher than those of a random predictor. CONCLUSIONS: Our method improves the quality of the prediction of the free energy change due to single point protein mutations by adopting a hypothesis of thermodynamic reversibility of the existing experimental data. By this we both recast the thermodynamic symmetry of the problem and balance the distribution of the available experimental measurements of free energy changes. This eliminates possible overestimations of the previously described methods trained on an unbalanced data set comprising a number of destabilizing mutations higher than stabilizing ones.

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Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations.

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