Abstract / Description of output
Many methodologically diverse computational methods have been applied to the growing challenge
of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a
perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach
is to use protein structural information to model the physical impacts of variants and predict their
likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of
stability predictors in reproducing thermodynamically accurate values and evaluated their ability to
distinguish between known pathogenic and benign mutations. Here, we take an alternate approach,
and explore how well stability predictor scores correlate with functional impacts derived from deep
mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein
stability-based tools against mutant protein fitness values from 49 independent DMS datasets,
covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the
strongest correlations with DMS-based functional scores, similar to their previous top performance in
distinguishing between pathogenic and benign variants. For both methods, performance is
considerably improved when considering intermolecular interactions from protein complex
structures, when available. Furthermore, using these two predictors, we derive a ‘Foldetta’ consensus
score, which improves upon the performance of both, and manages to match dedicated variant effect
predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability
effects show consistently higher correlations with certain DMS experimental phenotypes, particularly
those based upon protein abundance, and, in certain cases, can significantly outcompete sequencebased variant effect prediction methodologies for predicting functional scores from DMS experiments.
of predicting and interpreting the effects of protein variants. As many pathogenic mutations have a
perturbing effect on protein stability or intermolecular interactions, one highly interpretable approach
is to use protein structural information to model the physical impacts of variants and predict their
likely effects on protein stability and interactions. Previous efforts have assessed the accuracy of
stability predictors in reproducing thermodynamically accurate values and evaluated their ability to
distinguish between known pathogenic and benign mutations. Here, we take an alternate approach,
and explore how well stability predictor scores correlate with functional impacts derived from deep
mutational scanning (DMS) experiments. In this work, we compare the predictions of 9 protein
stability-based tools against mutant protein fitness values from 49 independent DMS datasets,
covering 170,940 unique single amino acid variants. We find that FoldX and Rosetta show the
strongest correlations with DMS-based functional scores, similar to their previous top performance in
distinguishing between pathogenic and benign variants. For both methods, performance is
considerably improved when considering intermolecular interactions from protein complex
structures, when available. Furthermore, using these two predictors, we derive a ‘Foldetta’ consensus
score, which improves upon the performance of both, and manages to match dedicated variant effect
predictors in reflecting variant functional impacts. Finally, we also highlight that predicted stability
effects show consistently higher correlations with certain DMS experimental phenotypes, particularly
those based upon protein abundance, and, in certain cases, can significantly outcompete sequencebased variant effect prediction methodologies for predicting functional scores from DMS experiments.
| Original language | English |
|---|---|
| Journal | Protein Science |
| Early online date | 27 May 2023 |
| DOIs | |
| Publication status | E-pub ahead of print - 27 May 2023 |