Title: Extraction and Using Geometrical Features from  3-dimensional  Images
for Protein Structure Prediction

Jing He
Assistant Professor
Department of Computer Science
CREST Center for Bioinformatics and Computational Biology
New Mexico State University


Abstract
Protein structure prediction is also known as the protein folding problem
that attracts the scientists from many disciplines. Generally, protein
structure prediction uses the amino acid sequence information of the
protein to build possible conformations and searches for one that has the
global minimum energy. However, current structure prediction methods are
still far from being mature. We have been investigating the problem of
combining the structure prediction methods with geometrical constraints
that can be obtained from the low resolution 3-dimensional image of protein
density. In this talk, I will present our two approaches to reduce the huge
topological search space of the secondary structures. The first approach is
built on top of Rosetta, a popular ab initio structure prediction software.
The second is through the direct measuring of the energy formed by the
secondary structures. For this approach, we have developed a multi-well
function based on a Lennard-Jones-like energy function using the
statistical data from the Protein Data Bank. For 43 of the 51 proteins
tested, the contact energy of the secondary structures in the native
topology is within the lowest 5% of that among all the possible topologies.
This result demonstrates that it is possible to use the secondary
structures, instead of the entire chain of the protein, to eliminate the
majority bad topologies of the structure.

Bio:
Dr.  He  obtained  her  B.S.  degree  in  Applied  Mathematics  from   Jilin
University, and M.S. degree in Applied Mathematics  from  Mew  Mexico  State
University. She worked in  the  area  of  3-dimensional  reconstruction  and
analysis of virus structures at Baylor College of Medicine  from  which  she
obtained her Ph.D. in Structural and  Computational  Biology  and  Molecular
Biophysics. Currently she is an Assistant Professor  at  the  Department  of
Computer Science.