Researchers have sought methods to design proteins that are highly stable and retain their biological activities. The recent dramatic increase in genome sequencing data provides scientists with sufficient data for sequence-based protein design. One method for protein design that has shown success for stabilizing proteins uses consensus sequences. Although consensus sequences have been found to be more stable and biologically active, the implicit assumption of that residue’s frequencies are independent. In protein structure, residues are coupled to one-another in a large interconnected network of interactions. The goal of this project is to employ a robust method to design proteins that incorporates these residue interactions. By using Restricted Boltzmann Machines to learn residue sequence-structure encodings, we can potentially discover sequences of proteins with improved stabilities, solubilities and shelf-lives. Developing such a methodology has applications in pharmaceuticals, biotechnology, and chemical industries.

Olin Shipstead (WSE)
Mentor: Scot Miller (Environmental Health & Engineering, WSE)

We propose to use a national network of atmospheric satellite observations to estimate natural gas leakage from the oil and natural gas industry. I would trace the ethane observations back in time (using atmospheric dispersion models run in reverse) to determine ethane emissions rates. With estimated ethane emission rates, we could then evaluate government inventories of emissions – what the government thinks is being emitted — against our emissions calculations. Through these analyses, we will understand how large these emissions are, where they occur, and which oil and natural gas regions leak the most fugitive emissions. In addition, we will evaluate the strengths and weaknesses of using ethane as part of a long-term monitoring strategy.