简介: |
Short Bio:
Jianyang (Michael) Zeng is currently a postdoctoral associate, advised by Dr. Bruce Donald in the Department of Computer Science at Duke University and the Duke University Medical Center. He received his Ph.D. in Computer Science from Duke University in 2011. He received both his B.S. and M.S. degrees from Zhejiang University, Hangzhou, China. His primary research interests lie in Bioinformatics and Computational Biology, Structural Genomics and Systems Biology, and Protein and Drug Design. His other research interests include Machine Learning, Parallel and Distributed Computation, and Robotics. His doctoral work has been published in several leading conferences in Computational Biology and peer-reviewed journals in Nuclear Magnetic Resonance (NMR) methodology.
Abstract:
High-throughput protein 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. In this talk, I will describe our recent success stories about the applications of computational techniques in addressing several bottlenecks in NMR structure determination. First, I will talk about 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 (PDB ID: 2KIQ). Second, I will present a Bayesian approach to determine protein side-chain conformations by integrating the likelihood function derived from unassigned NOE data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures. Third, I will describe an automated side-chain resonance assignment algorithm that does not require any explicit through-bond experiment to facilitate side-chain resonance assignment. All our algorithms have been tested on real NMR data. 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 process. |
