The proposed research focuses on developing efficient Monte Carlo algorithms for sampling conformation of all-atom protein models. Such methods would be beneficial for drug design and predicting ligand binding constraints, solving X-ray and NMR structures of proteins, and in predicting the three-dimensional structure of proteins from their amino acid sequence. A flexible new algorithm, hierarchical Monte Carlo, will be investigated; the specific advantages of this method are that it tolerates large changes in the dihedral angle and accelerates energy evaluations, while still converging to a true Boltzmann distribution. The method will be tested and refined on Met-enkephalin and crambin by making ever large perturbations from the global energy minimum structure and tracking the method's efficiency in recovering the global minimum. The method will then be applied to large proteins such as ribonuclease, and to homology modelling, specifically of the serine proteases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32GM019399-01
Application #
2637495
Study Section
Biophysical Chemistry Study Section (BBCB)
Project Start
1998-09-30
Project End
Budget Start
1998-08-24
Budget End
1999-08-23
Support Year
1
Fiscal Year
1998
Total Cost
Indirect Cost
Name
Cornell University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
City
Ithaca
State
NY
Country
United States
Zip Code
14850