Statistics and Data Science Seminar
Prof. Jie Liang
Department of Bioengineering, UIC
Entropy, folding, and function of biomolecules through Monte Carlo sampling
Abstract: The three dimensional structures of biomolecules such as proteins and
RNAs are the basis of their biological functions. For RNA molecule,
conformational entropy is important for stability and
folding. However, it is challenging to either measure or compute
conformational entropy associated with long loops. We develop
optimized discrete $k$-state models of RNA backbone and estimate
entropy of hairpin, bulge, internal loop, and multibranch loop of long
length using an efficient sequential Monte Carlo sampling method. The
estimated entropy indicate that the Jacobson-Stockmayer model has
large errors for bulge, internal, and multibranch loops. For protein,
we study the transition state ensemble. By generating effective
samples under various experimentally derived constraints, we
characterize the transition state ensemble (TSE) during protein
folding. As TSE is short-lived, the size and shape of conformations
in TSE have been elusive. For the protein acylphosphatase, we found
TSE has diverse conformations. In contrast to previous results, we
found overall TSE can be very different from native structure of
proteins (with RMSD>12A). To predict protein functions, we develop a
method by matching local surfaces based on estimated evolutionary
information specific to individual binding region via a Bayesian Monte
Carlo approach using a continuous-time Markov model. Our method
provides a probabilistic model which characterizes protein binding
activities that may involve multiple substrates or ligands. (Joint
work with Rong Chen, Ming Lin, Zheng Ouyang, Jeffrey Tseng, and Jian
Zhang) (please visit http://www.uic.edu/~jliang for further
information).
Wednesday April 2, 2008 at 3:30 PM in SEO 712