||<p>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 NMR spectra.
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. In this dissertation, we present the following
novel algorithms for automating NMR assignment and protein structure determination.
First, we develop a novel high-resolution structure determination algorithm that starts
with a global fold calculated from the exact and analytic solutions to the residual
dipolar coupling (RDC) equations. Our high-resolution structure determination protocol
has been applied to solve the NMR structures of the FF Domain 2 of human transcription
elongation factor CA150 (RNA polymerase II C-terminal domain interacting protein),
which have been deposited into the Protein Data Bank. Second, we propose an automated
side-chain resonance and NOE assignment algorithm that does not require any explicit
through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY.
Third, we present a Bayesian approach to determine protein side-chain rotamer conformations
by integrating the likelihood function derived from unassigned NOE data, with prior
information (i.e., empirical molecular mechanics energies) about the protein structures.
Fourth, we develop a loop backbone structure determination algorithm that exploits
the global orientational restraints from sparse RDCs and computes an ensemble of loop
conformations that not only close the gap between two end residues but also satisfy
the NMR data restraints. Finally, to facilitate NMR structure determination for large
proteins, we develop a novel algorithm for predicting the Ha chemical shifts by exploiting
the dependencies between chemical shifts of different backbone atoms and integrating
the attainable structural information. All the algorithms developed in this dissertation
have been tested on experimental NMR data with collaborators in Dr. Pei Zhou's and
our labs. The promising results demonstrate that our algorithms can be successfully
applied to high-quality protein structure determination. Since our algorithms reduce
the time required in NMR assignment, it can accelerate the protein structure determination