Development of computational tools for studying protein sequences, structures and signaling networks

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Cells are composed of protein signaling networks that perform biological functions such as regulating cell growth or catalyzing biochemical reactions. As a result, the malfunction of proteins often causes human illnesses such as Alzheimer's disease, heart disease and cancer. My long term research goal is to create synthetic protein signaling networks that will allow us to one day manipulate cell biology with the same precision as electrical circuits and computer networks. To accomplish this goal, my proposal will focus on developing computational tools for studying protein sequences, structures and signaling networks. First, to infer the function of a protein sequence, the Smith Waterman (SW) algorithm is used to find its similarity to proteins of known function. As sequence databases grow larger, faster sequence comparison approaches are required such as using accelerated field programmable gate array (FPGA) hardware. To make the FPGA solution more affordable, I will develop FPGA hardware for accelerating the SW algorithm using fewer resources while maintaining a comparable speed. Next, to study the protein signaling kinetics within cells, fluorescent protein biosensors are powerful tools but the design of these biosensors is often trial and error. Using a computational tool to model the conformational space of protein biosensors, I improved the design however the tool was not quantitative. To address that problem, I will include molecular factors that select preferred biosensor conformations. Lastly, to design synthetic protein networks or model larger existing networks, I will develop a computational tool for simulating the spatial and temporal kinetics of protein signaling networks. Together this work will yield insights into protein sequences and their networks that will ultimately aid in developing therapies for human illnesses.