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Biophysical and Mechanistic Models for Disease-Causing Protein Variants
Amelie Stein1, Douglas M Fowler2, Rasmus Hartmann-Petersen1
1Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Trends in Biochemical Sciences
|February 5, 2019
Summary
Decreasing DNA sequencing costs reveal many protein variants. This review explores how experimental and computational methods predict variant consequences, focusing on protein stability
Area of Science:
- Genomics
- Proteomics
- Biophysics
- Computational Biology
Background:
- Decreasing DNA sequencing costs are revolutionizing medicine and science.
- Millions of missense variants are identified, but their consequences remain largely unknown.
- Deep mutational scanning experiments allow high-throughput analysis of protein variants.
Purpose of the Study:
- To review experimental and computational approaches for determining missense variant consequences.
- To focus on the role of protein stability changes in disease.
- To provide a framework for understanding and predicting variant effects on protein stability.
Main Methods:
- Review of experimental deep mutational scanning.
- Integration of biophysical models.
- Application of computational prediction methods.
Main Results:
- Missense variants can alter protein sequences, impacting function and stability.
- Changes in protein stability are a key factor in disease causation.
- A framework is emerging to link sequence changes to cellular protein stability.
Conclusions:
- Combining experimental and computational approaches is crucial for understanding variant effects.
- Predicting the impact of missense variants on protein stability is key to advancing precision medicine.
- Further research is needed to fully elucidate the complex relationship between protein sequence, stability, and cellular function.
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