Biology subjects

Jepsen, M. M.

Publications and source records attributed to Jepsen, M. M..

2 recordsLinked to original sources

bioRxiv2022

Accurate protein stability predictions from homology models

Calculating changes in protein stability ({Delta}{Delta}G) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate {Delta}{Delta}Gs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of {Delta}{Delta}G values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested {Delta}{Delta}Gs and templates for homology modeling across a broad range of sequence identities, and selected three methods for {Delta}{Delta}G calculations to test. We find that {Delta}{Delta}G-values predicted from homology models compare equally well to experimental {Delta}{Delta}Gs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using {Delta}{Delta}Gs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

biophysics↗

bioRxiv2019

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

Decreased cost of human exome and genome sequencing provides new opportunities for diagnosing genetic disorders, but we need better and more robust methods for interpreting sequencing results including determining whether and by which mechanism a specific missense variants may be pathogenic. Using the protein PTEN (phosphatase and tensin homolog) as an example, we show how recent developments in both experiments and computational modelling can be used to determine whether a missense variant is likely to be pathogenic. One approach relies on multiplexed experiments that enable determination of the effect of all possible individual missense variants in a cellular assay. Another approach is to use computational methods to predict variant effects. We compare two different multiplexed experiments and two computational methods to classify variant effects in PTEN. We distinguish 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 destabilise 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 e.g. 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.

biophysics↗