Improving the prediction and treatment of human genetic disease by exploring the proteome-level impacts of dominant-negative mutations

Precision Medicine Project - Improving the prediction and treatment of human genetic disease by exploring the proteome-level impacts of dominant-negative mutations

Supervisor(s): Dr Georg Kustatscher & Prof Joe Marsh
Centre/Institute: Institute of Quantitative Biology

Background:

Most known disease-causing mutations occur in coding regions and affect the ways in which proteins are made. While some pathogenic mutations have structurally disruptive effects that induce a simple loss of function, it is increasingly recognized that many are associated with more complex, non-loss-of-function mechanisms. In particular, for proteins that assemble into complexes, assembly-mediated dominant-negative or gain-of-function effects are often observed (Backwell & Marsh, 2022). Paradoxically, despite their apparently milder impacts on protein structure, these mutations often have more severe phenotypic impacts than complete loss-of-function mutations. Moreover, dominant-negative and gain-of-function mutations tend to be much less well predicted by current computational approaches (Gerasimavicius et al, 2022).

Understanding the mechanisms underlying pathogenic protein mutations is extremely important because the potential for therapeutic strategies is heavily dependent on it: while loss-of-function mutations could be treated with gene replacement, for disorders associated with dominant-negative or gain-of-function effects, the mutant allele would need to be targeted, e.g. with gene editing or small molecule targeting.

Currently, we know very little about how dominant-negative mutations act at the molecular level, or how they impact other proteins with which they interact and assemble into complexes. In this project, we will seek to address this, using both experimental and computational approaches to explore the differential effects of loss-of-function and dominant-negative mutations on protein complexes, and then use this knowledge to develop predictive models.

Aims and Methodoly

  1. Aim 1: Explore the properties of mutations participating in protein complexes. We will identify known disease-causing mutations in protein complex subunits and, using our recently developed machine-learning approach (Badonyi and Marsh, 2024), predict their likely mechanism of action: loss-of-function, dominant-negative or gain-of-function.  We will explore the predicted impacts of mutations on protein structure and assembly, and prioritise proteins and mutations for further experimental characterisation.
  2. Aim 2: Experimentally characterise the effects of loss-of-function, dominant-negative and gain-of-function mutations on protein complexes. Based on work in Aim 1, we will identify complexes with multiple pathogenic mutations predicted to act via different molecular mechanisms occurring in different subunits of the same protein complexes. We will introduce these mutations individually in mammalian cell lines and use quantitative proteomics to compare the impacts of different mutations on the proteome (Munro et al, 2024). Specifically, using methods already established in the Kustatscher lab, we will determine both protein abundances and protein stability (turnover rate) for the mutated protein, the affected protein complex and the remaining proteins in the cell. In addition, we plan to use thermal proteome profiling to reveal how mutations affect protein complex integrity. Together with computational analyses such as AlphaFold-based interaction modelling, this could reveal basic principles of how different types of mutations affect protein complexes. 
  3. Aim 3: Develop a computational model to predict the impacts of protein mutations on protein abundances. We will combine existing computational methods for variant effect prediction and stability impacts with available protein interaction and gene expression datasets to develop a new machine learning model for predicting the impacts of loss-of-function and dominant-negative mutations on the abundances of other proteins participating in the same complex. This model will be validated and refined using the experimental data measured in Aim 2.

Training outcomes

  1. Gain experience in using current computational approaches for predicting variant effects and molecular mechanisms and understand how these are currently applied to clinical variant interpretation.
  2. Develop practical skills in various experimental techniques, including quantitative proteomics in mammalian cell lines, and in the processing and analysis of proteomics data
  3. Learn to integrate experimental data with computational approaches to build predictive model

References

Backwell, L., & Marsh, J. A. (2022). Diverse molecular mechanisms underlying pathogenic protein mutations: beyond the loss-of-function paradigm. Annual review of genomics and human genetics, 23(1), 475-498.

Badonyi, M., & Marsh, J. A. (2024). Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases. PloS one, 19(8), e0307312.

Gerasimavicius, L., Livesey, B. J., & Marsh, J. A. (2022). Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nature communications, 13(1), 3895.

Munro, V., Kelly, V., Messner, C.B. & Kustatscher, G. (2024). Cellular control of protein levels: A systems biology perspective. Proteomics. 2024 Jun;24(12-13):e2200220.

Apply Now

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  • The deadline for 25/26 applications is Monday 13th January 2025
  • Applicants must apply to a specific project. Please ensure you include details of the project on the Recruitment Form below, which you must submit to the research proposal section of your EUCLID application. 
  • Please ensure you upload as many of the requested documents as possible, including a CV, at the time of submitting your EUCLID application.  

 

Q&A Sessions

Supervisor(s) of each project will be holding a 30 minute Q&A session in the first two weeks of December. 

If you have any questions regarding this project, you are invited to attend the session on Monday 9th December at 11am GMT via Microsoft Teams. Click here to join the session.

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