Determining in vivo protein structures and understanding genetic interactions using deep mutagenesis
The goal of my research is to understand mutations and how they interact to alter phenotypes and disease. We recently initiated a new research direction that uses systematic (‘deep’) mutagenesis to quantify, understand, and predict how diverse mutations interact non-additively within and between molecules to affect phenotypes at multiple scales. Very excitingly, we have also shown that quantifying genetic interactions by deep mutagenesis provides sufficient information to determine the 3D structures of proteins. In this project we will leverage this experience in deep mutagenesis and computational modelling to address three specific aims:

1.	To develop simple generic experimental and computational methods to determine the in vivo structures of proteins using deep mutagenesis and to apply these methods to solve the structures of domains of unknown structure.

2.	To use deep mutagenesis and computational modelling to understand how mutations globally interact within and between molecules, when these interactions can – and cannot – be predicted from phenotypic measurements alone, and how these interactions alter in response to changes in gene expression.

3.	To use deep mutagenesis to understand the cellular toxicity of pathological prion-like domains and to reveal the in vivo structures of these ‘unstructured’ regions, as well as those of disordered proteins that function as agents of protein-based epigenetic inheritance. 

Taken together, this will provide rich insights into how mutations combine to alter phenotypes, a question of central importance to evolution, engineering, and human disease.  It will also develop methods that use deep mutagenesis to determine protein structures, including of intrinsically disordered proteins relevant to disease and epigenetic inheritance.  Our goal is to develop techniques that will allow labs across the world to use deep mutagenesis to solve protein structures, including potentially in large-scale systematic projects.