Natural proteins often assemble into various complex geometric structures based on their interactions with each other. The King Lab at the University of Washington's Institute for Protein Design uses the way these proteins behave to develop computational models that enable the design of novel self-assembling protein cages, or nanoparticles. The designed particles are capable of holding and transporting molecules or displaying antigens on their surface, making them effective vaccine candidates. My project involves recovering the solubility of one of these protein cages known as T33_dn2. T33_dn2 is a tetrahedral protein cage comprised of four copies each of two trimeric components known as T33_dn2A and T33_dn2B. While both components can be expressed individually through E.coli before being assembled in vitro, they can also be expressed bicistronically and assemble in vivo. Currently, the use of T33_dn2 as a vaccine scaffold is limited because T33_dn2B is insoluble, and only seems to be stabilized in solution when associating with T33_dn2A. When expressed bicistronically, however, the cage has an extremely low yield. For a protein to be developed into a vaccine, it must be soluble. To recover the solubility and yield of T33_dn2B, I am testing ten plasmid variants of bicistronic T33_dn2. The “original” plasmid consists of one gene coding for a high-expressing cleavable SUMO protein attached to T33_dn2A and another coding for T33_dn2B. The additional nine variants have single point mutations at specific locations on the T33_dn2A gene intended to affect binding strength. After expression, introducing wildtype T33_dn2A in vitro will allow for the formation of T33_dn2. I will be presenting the results of these expression, purification, and assembly tests.

Although DNA is best known as the molecule that encodes the genetic information of all living things, they can also be utilized as chemical building blocks. Using DNA as the building material of choice, I am working on constructing a complex DNA circuit, in specific, a binary neuron network that utilizes DNA strand displacement reactions to compute a winner-take-all or majority voting operation. Winner-take-all computation is just one type of competitive neural network model, mirroring the lateral inhibition and competition seen in biological neurons of the brain. DNA circuits are efficient at collecting and responding to information within a biochemical environment; processing information locally and producing specific outputs in response to changing environmental conditions. DNA strand displacement is the process by which two DNA strands that are partially or fully complementary hybridize to one another, thereby displacing one or more pre-hybridized strands. Strand displacement reactions are facilitated by a "toehold" domain, a region of exposed DNA on a double-stranded gate complex that is complementary to an input strand.The winner-take-all function can be broken down into sub-functions that use four distinct seesaw DNA gate motifs: weights, thresholding, annihilator, and catalytic amplifier. Additionally, we aim to combine spatial separation in microfluidic droplets with a stricter choice of network architecture to address previously seen issues of scalability. By isolating each computational primitive in droplets, DNA species can be re-used for all primitives of the same network layer. Recently, we have experimentally tested a 5-input neuron and used manual pipetting to simulate droplet operations. While the leak caused problems for patterns close to the decision boundary, we could successfully compute well-separated patterns. Next, we plan on optimizing the sequence design to reduce leak and scale up the network size by automating the network execution on a microfluidic droplet device.