Periodic Reporting for period 2 - PROT-STRUCT-DISEASE (Protein Structure, Molecular Mechanisms and Human Genetic Disease: Beyond the Loss-of-function Paradigm)
Período documentado: 2022-12-01 hasta 2024-05-31
The identification of damaging DNA variants is fundamental to the diagnosis, treatment and prevention of genetic disease. Computational methods for predicting variant effects are widely used for scoring and prioritising variants most likely to cause disease, but their current performance is not considered good enough to use as strong evidence in genetic diagnosis. Conversely, directly measuring the effects of variants using experiments can be powerful, but is time consuming and can be difficult to perform on a large scale. This project seeks to improve our ability to identify disease-causing variants through a combination of computational and experimental approaches, with a particular focus on the diverse molecular mechanisms by which mutations can cause disease, in contrast to the current overwhelming focus on simple loss-of-function effects.
In Aim 1, we will use structural bioinformatics techniques to explore the properties of dominant-negative, gain-of-function and loss-of-function variants, and learn how they are related to protein structure and disease phenotypes. In Aim 2, we will perform deep mutational scanning experiments (DMS) on human disease genes, enabling us to measure effects and elucidate molecular mechanisms for tens of thousands of single amino-acid substitutions. Finally, in Aim 3, we will seek to improve the prediction and prioritisation of pathogenic and other phenotypically important variants. This will involve assessing current approaches for predicting variant effects, implementing our own computational variant prioritisation pipeline and meta-predictor, and using our new understanding of molecular mechanisms to integrate computational predictors and DMS data with structural and other protein-level features. We will also demonstrate the power of our approach in application to sequencing data from clinical and population studies.
In Aim 1, we will use structural bioinformatics techniques to explore the properties of dominant-negative, gain-of-function and loss-of-function variants, and learn how they are related to protein structure and disease phenotypes. In Aim 2, we will perform deep mutational scanning experiments (DMS) on human disease genes, enabling us to measure effects and elucidate molecular mechanisms for tens of thousands of single amino-acid substitutions. Finally, in Aim 3, we will seek to improve the prediction and prioritisation of pathogenic and other phenotypically important variants. This will involve assessing current approaches for predicting variant effects, implementing our own computational variant prioritisation pipeline and meta-predictor, and using our new understanding of molecular mechanisms to integrate computational predictors and DMS data with structural and other protein-level features. We will also demonstrate the power of our approach in application to sequencing data from clinical and population studies.
We have made great progress since the start of this project. To address Aim 1, we performed a comprehensive study of the properties of loss-of-function, gain-of-function and dominant-negative missense variants. Building on preliminary results that were included in the project proposal, we showed how protein variants associated with different molecular mechanisms have profoundly different effects on protein structure, in particular in terms of their effects on protein stability. We also showed how essentially all currently available variant effect predictors underperform on gain-of-function and dominant-negative variants compared to loss-of-function variants. In another related study, we dove more deeply into the topic of dominant-negative mutations, showing how their likelihood of occurring is closely related to the mechanism by which a mutated protein assembles into a complex. All of this work is now published.
For Aim 2, we have now performed complete DMS experiments for two of our originally stated targets, with multiple other targets now in various stages of progress. Analysis of this data has highlighted striking differences in its utility for dominant vs recessive genetic disorders, and has inspired a new biallelic approach to data interpretation that shows great benefit for recessive disease. Publications describing this work are aimed to be submitted very soon.
With respect to Aim 3, we have completed a pair of closely related studies where we investigated the relationships between a large number of DMS experiments, and the outputs of computational methods for predicting either variant pathogenicity, or protein stability changes upon mutation. We were able to identify specific tools as performing best for either task, and also showed how the correspondence with protein stability effects depends greatly on the type of DMS experimental assay. We have also released a new computational method for predicting the molecular mechanisms most likely to be associated with different human genes, which is crucial for our ongoing efforts to establish an improved, mechanism-centric variant assessment pipeline.
For Aim 2, we have now performed complete DMS experiments for two of our originally stated targets, with multiple other targets now in various stages of progress. Analysis of this data has highlighted striking differences in its utility for dominant vs recessive genetic disorders, and has inspired a new biallelic approach to data interpretation that shows great benefit for recessive disease. Publications describing this work are aimed to be submitted very soon.
With respect to Aim 3, we have completed a pair of closely related studies where we investigated the relationships between a large number of DMS experiments, and the outputs of computational methods for predicting either variant pathogenicity, or protein stability changes upon mutation. We were able to identify specific tools as performing best for either task, and also showed how the correspondence with protein stability effects depends greatly on the type of DMS experimental assay. We have also released a new computational method for predicting the molecular mechanisms most likely to be associated with different human genes, which is crucial for our ongoing efforts to establish an improved, mechanism-centric variant assessment pipeline.
The work performed as part of this study is already having a considerable impact, increasing knowledge of the different protein-level mechanisms associated with genetic disease, and showing their importance for protein variant interpretation. This can be demonstrated by the fact that our first publication (published July 2022), on the protein structural properties of mutations associated with different molecular mechanisms, has already been cited 90 times.
Our results so far suggest that non-loss-of-function variants are even more prevalent, and more difficult to predict the effect of, than we thought at the start of the project. By the end of the project, the knowledge we will have learned, the tools we will have developed, and the experimental data we will have measured, will lead to a great improvement in our ability to identify damaging genetic variants and thus diagnose human genetic disease.
Our results so far suggest that non-loss-of-function variants are even more prevalent, and more difficult to predict the effect of, than we thought at the start of the project. By the end of the project, the knowledge we will have learned, the tools we will have developed, and the experimental data we will have measured, will lead to a great improvement in our ability to identify damaging genetic variants and thus diagnose human genetic disease.