Our group, the King Lab at the University of Washington, computationally designs self-assembling protein nanoparticles for therapeutic applications. These nanoparticles make great candidates for therapeutics because of their ability to both encapsulate molecules as “cargo” and display antigens on their surface. One of the problems we face in vaccine and therapeutic design is targeted nanoparticle delivery, or making sure nanoparticles are delivered to a specific location, in vivo. Understanding where certain nanoparticles localize in vivo will be useful for determining what influences a nanoparticle’s interactions inside an organism. One approach our group is taking to examine nanoparticle biodistribution is miniprotein library display on synthetic nucleocapsids. These miniproteins are 20-50 amino acids long and were designed to produce stable, folded structures with surface patches that could facilitate binding to target receptors. Synthetic nucleocapsids, or nanoparticles designed to encapsulate their own RNA genomes, display these miniproteins in the library. The library was injected into healthy and tumor-ridden mice. RNA sequences were then obtained from blood, brain, heart, spleen, kidney, liver, lung, tumor, and dose samples. Using unsupervised and supervised learning algorithms such as Principal Component Analysis, Hierarchical Clustering, and Random Forests, I am building mathematical models that can analyze the biochemical properties of these library variants and determine why certain nanoparticles vary in biodistribution. I will also be analyzing another synthetic nucleocapsid library to answer similar questions about nanoparticle biodistribution. By constructing these models, I hope to provide tools that aid in experiments regarding targeted drug design.