Classifying disease-associated variants using measures of protein activity and stability

The decreased cost of human exome and genome sequencing provides new opportunities for diagnosing genetic disorders. In order to utilize this information, we need better and more robust methods for interpreting sequencing results, including determining whether specific missense variants are likely to be pathogenic. Using the protein PTEN (phosphatase and tensin homolog) as an example, we show how recent developments in both experiments and computational modeling can be used to determine whether a missense variant is likely to be pathogenic. Missense variants in PTEN can cause both autism spectrum disorder and increased risk of different forms of cancer, yet distinguishing such disease-associated variants from more harmless genetic variation is difficult. One approach relies on multiplexed experiments that enable determination of the effect of all possible missense variants in a cellular assay. Another approach is to use computational methods to predict variant effects. We compared two different multiplexed experiments and two computational methods to classify variant effects in PTEN. We distinguished between methods that focus on effects on protein stability and protein-specific methods that are more directly related to enzyme activity. Our results on PTEN suggest that ∼60% of pathogenic variants cause loss of function because they destabilize the folded protein, which is subsequently degraded. Methods that quantify a broader range of effects on PTEN activity perform better at predicting variant effects. Either experimental method performs better than the corresponding computational predictions, so that, for example, experiments that probe cellular abundance perform better at identifying pathogenic variants than predictions of thermodynamic stability. Our results suggest that loss of stability of PTEN is a key driver for disease, and we hypothesize that experiments and prediction methods that probe protein stability can be used to find variants with similar mechanisms in other genes.