Preliminary Exam Talks
Novel Algorithms for Automated NMR Assignment and Protein Structure Determination
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Abstract
High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. Unfortunately, current NMR structure determination is still limited by the lengthy time required to process and analyze the experimental data. A major bottleneck in protein structure determination via NMR is the interpretation of NMR data, including the assignment of chemical shifts and nuclear Overhauser effect (NOE) restraints from the experimental data. The development of automated and efficient procedures for analyzing NMR data and assigning experimental restraints will thereby enable high-throughput protein structure determination and advance structural proteomics research. The goal of this dissertation project is to address several key computational bottlenecks in NMR structure determination. We will extend and improve several NMR structure determination techniques recently developed by our lab, and develop novel algorithms on the following areas: (a) automated assignment of NOE restraints using a novel pattern-matching technique; (b) high-resolution protein structure determination starting with a global fold computed from exact solutions to the residual dipolar coupling (RDC) equations; (c) automated protein side-chain resonance assignment without any specific NMR experiments for measuring the side-chain through-bond interactions. We will test our algorithms on experimental NMR data with collaborators in Dr. Pei Zhou's and our labs. The new protein structures determined by our algorithms will be deposited into the Protein Data Bank.