American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
(Joint Seminar with the Department of Statistics)
Columbia University
Department of Biostatistics Colloquium



Professor Jun Liu
Harvard University


I will describe some of our recent efforts in the development of Monte Carlo strategies (both MCMC and SMC) for simulating and optimizing molecular structures. I will illustrate these ideas using examples from Hydrophobic-Hydrophilic (HP) protein model (both 2-D and 3-D) optimization, protein side-chain entropy (SCE) estimation, and near-native structure (NNS) simulations. By applying the new SMC and MCMC schemes, we were able to achieve the best results for all the 2-D and 3-D HP structural optimization examples we can find in the literature. In particular, the new approach achieved better results for these HP models than a modified PERM algorithm and the equi-energy Sampler (Kou et al. 2006). For the SCE and NNS problems, we can characterize accurately many important ensemble properties of NNS and compute efficiently the SCE of a given structural backbone for any given energy function. We also found that widely used pairwise potential functions behaved surprisingly badly for stabilizing near native protein structures, and adding a term representing the SCE of the protein can help greatly in discriminating true native structures from decoys.

Based on joint work with Jinfeng Zhang and Sam Kou.

Date: Monday, April 16, 2007
Time: 12:00 to 1:00 P.M.
Location: Columbia University
Department of Statistics
School of Social Work Building
1255 Amsterdam Avenue
10th Floor - Room 903
New York, New York


Refreshments will be served at 11:30 A.M., Room 1025.

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