Proteins are an essential component of all life, fulfilling remarkably diverse roles ranging from structure to catalysis to locomotion and beyond. Their broad diversity of function means that they have potential applications in numerous human endeavors. Over the years, numerous experimental and computational techniques have been developed to design proteins, with each approach having advantages and disadvantages, but they are rarely utilized together. With the recent expansion of high throughput screens and the corresponding growth of “big data,” it is an opportune time to combine these complementary approaches.
I will lead a research laboratory that integrates both computational and experimental protein methods, with a specialization in therapeutics. The unifying theme of this research will be the computational analysis of large, experimentally-obtained datasets to extract the biologically-relevant information and generate hypotheses that are validated in the laboratory. As the title suggests, the research will be broadly divided into two categories: the design of therapeutic proteins (e.g. antibodies, fibronectin type III domains, anticalins, etc.) and the discovery of therapeutically-relevant proteins (e.g. characterizing the complete repertoire of antigens bound by an individual’s antibodies). By integrating computational analysis with experimental testing, my research will facilitate the rapid development of disease diagnostics and treatments.
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