Towards rational improvement of protein stability: A combined computational and NMR spectroscopic approach.
Name of applicant
Yulian Gavrilov
Institution
University of Copenhagen
Amount
DKK 1,345,944
Year
2017
Type of grant
Reintegration Fellowships
What?
Proteins are complex biomolecules that are the workhorses for all biological processes. Proteins are also widely used in both technical and medical applications, such as enzymes in laundry detergents, or as protein pharmaceuticals. Most proteins need to fold into a well-defined three-dimensional structure to function. The stability of a protein determines how well the protein can perform this folding process, and having a detailed understanding of protein stability is thus key to our ability to optimize proteins, and understand how they can lose function in disease. The goal of this project is thus to improve our ability to predict the effect of mutations on protein stability.
Why?
The ability to predict how mutations change the stability of a protein can be used in medical genetics to better understand the mechanism of a range of genetic disorders. In biotechnology, mutations that increase the stability of a protein without affecting its function may be used to increase the resistance of enzymes, or protein pharmaceuticals to environmental factors (e.g. to heat). In this way, stabilizing mutations can improve the efficiency of medical products and increase their shelf life. The effect of mutations on protein stability was investigated before. However, known experimental methodology and theoretical state-of-the-art prediction methods fail to predict and describe the effect of many mutations. Thus, there is a possibility for further improvements in this field.
How?
My strategy is to combine experimental measurements of how atoms move and vibrate in the protein with advanced computer simulations. By analyzing several mutants of a protein in this way, I will be able to elucidate how the structure, the atomic motions and the stability depend on each other. This approach adds an extra layer to our understanding of protein stability, which will be built into models that more accurately can predict the effect of any mutation on the stability of a protein.