UCSF

Computational protein design is poised to bring forth many advances in areas ranging from personalized medicine to biofuel production. While protein engineers have made huge strides towards the design of structure, proteins with new functions remain extremely difficult to design in silico, owing largely to the precise geometric requirements they demand. In this dissertation, I present two new methods for the design of functional proteins that focus specifically on enabling the precise sidechain geometries that are required for functions such as binding and catalysis. Pull Into Place (PIP) accurately places key amino acid sidechains by creating and stabilizing new irregular backbone geometries that are compatible with a provided functional interaction, utilizing new fragment-based local structure design and prediction methods. We experimentally validate PIP on a ketosteroid isomerase model system by redesigning its active site loop such that it uses a glutamate, rather than an aspartate, to perform proton extraction on its substrate, a task requiring accuracy on the scale of a carbon-carbon bond.Where PIP enables one or a few functional sidechain interactions requiring irregular backbone structures, Helical ELements Interface eXplorer (HELIX), a new method for de novo protein-protein interface design, instead attempts to connect highly regular secondary structure elements to facilitate many functional sidechain interactions at once. It does this by matching tertiary structural motifs from the PDB with a library of proteins containing systematically reshaped α-helices. HELIX results in computational designs with a high degree of shape complementarity, many of which contain difficult-to-design polar interaction networks. Together, these two works encompass several new tools and approaches that enable the design of new functions.