Protein Structure Prediction
Introduction
In protein structure prediction one attempts to predict the three-dimensional structure of a protein based on the primary structure, the sequence of amino acids. The structure ultimately determines the function of a protein—that's why we are interested in it.
The motion planning methods for robots developed in our lab specifically address robots with many degrees of freedom. A molecule can also be modeled as a robot which can twist and bend. Large molecules, such as proteins, have thousands of such joints. We are investigating if our methods can successfully be applied to "molecular robots".
In one of our projects we are applying search techniques motivated by concepts from robot motion planning to protein structure prediction. Currently, Rosetta, originally developed in Professor David Baker's laboratory, is considered to be the most accurate protein prediction software. Below we compare the results we have gotten with our method for protein L, which relies on Rosetta to perform energy calculations for protein conformations, with Rosetta's results and the native structure. This protein consists of 60 amino acids and thus has 120 degrees of freedom.
 |  |  |
| Predicted by Rosetta | Native | Predicted by our method |
People
TJ Brunette
Oliver Brock
Funding
This work is funded by
- NIH in the NIGMS division: Predicting Protein Structure with Guided Conformation Space Search, award number NIGMS 1R01GM076706
- NSF: Computational Biology Facility for Western Massachusetts, award number CSE CNS 0551500
- UMass Amherst under the Biomedical Innovation Initiative: Cellular Misfolding and Serpinopathies
We thank all of our funding agents.
Publications
Brock, Oliver and TJ Brunette.
Predicting Protein Structure with Guided Conformation
Space Search.
Technical Report 05-63.
Computer Science Department,
University of Massachusetts Amherst,
November 2005.
pdf
Brunette, TJ and Oliver Brock. Improving Protein Structure Prediction with Model-Based Search. Bioinformatics 21(Suppl. 1):66-74, June 2005.
Special Issue for the Internaitonal Conference on
Intelligent Systems for Molecular Biology (ISMB), Detroit, USA
pdf
Brunette, TJ and Oliver Brock. Model-Based Search to Determine Minima in Molecular Energy Landscapes. Technical Report 04-48. Computer Science Department, University of Massachusetts Amherst, September 2004. pdf
