The long-term aim of this project is to fold small proteins by extending the methods for simulation that Dr. Levitt developed over the past 30 years. He also plans to use information on known protein structures. The proposed research involves a variety of different theoretical and computational methods in a collaboration with an experimentalist with whom he has had a longstanding relationship.
Five specific aims are to be achieved by answering these questions in the affirmative: 1. Can the aggregation of small nonpolar solutes in water be quantified in terms of hydrophobic free-energy and its dependence on contact area? He will use molecular dynamics to simulate different concentrations of nonpolar molecules in water and determine the energetics of cluster formation. 2. Can b-hairpin and a-helix folding units be unfolded and refolded? Recent studies by his collaborator, Dr. Eaton, show that the C-terminal b-hairpin of protein G refolds on a microsecond time-scale. He will simulate this using his very efficient methods, which include explicit waters. 3. Can one generate nonnative decoy structures of proteins, which are so well-packed that they look like real native structure? Dr. Levitt will draw on his work with lattice and off-lattice models, side- chain modeling and energy refinement to generate tens of thousands of all-atom structures for small protein sequences. 4. Can energy functions derived from selected sets of protein structures correctly distinguish near-native structures from decoys? He will optimize these knowledge-based energy functions by testing them on his existing decoy sets and then improving decoy generation to make discrimination more difficult. 5. Can one fold some small proteins using just the amino acid sequence? He will combine predicted secondary structures with decoy generation and knowledge-based potentials in attempts to correctly predict folded structures before they are solved experimentally.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM041455-12
Application #
6180264
Study Section
Special Emphasis Panel (ZRG3-BBCB (02))
Program Officer
Flicker, Paula F
Project Start
1989-07-01
Project End
2002-06-30
Budget Start
2000-07-01
Budget End
2001-06-30
Support Year
12
Fiscal Year
2000
Total Cost
$179,280
Indirect Cost
Name
Stanford University
Department
Biology
Type
Schools of Medicine
DUNS #
800771545
City
Stanford
State
CA
Country
United States
Zip Code
94305
Sim, Adelene Y L; Minary, Peter; Levitt, Michael (2012) Modeling nucleic acids. Curr Opin Struct Biol 22:273-8
Moreno-Hernández, Sergio; Levitt, Michael (2012) Comparative modeling and protein-like features of hydrophobic-polar models on a two-dimensional lattice. Proteins 80:1683-93
Sim, Adelene Y L; Levitt, Michael; Minary, Peter (2012) Modeling and design by hierarchical natural moves. Proc Natl Acad Sci U S A 109:2890-5
Bray, Jenelle K; Weiss, Dahlia R; Levitt, Michael (2011) Optimized torsion-angle normal modes reproduce conformational changes more accurately than cartesian modes. Biophys J 101:2966-9
Chopra, Gaurav; Levitt, Michael (2011) Remarkable patterns of surface water ordering around polarized buckminsterfullerene. Proc Natl Acad Sci U S A 108:14455-60
Sim, Adelene Y L; Levitt, Michael (2011) Clustering to identify RNA conformations constrained by secondary structure. Proc Natl Acad Sci U S A 108:3590-5
Bernauer, Julie; Huang, Xuhui; Sim, Adelene Y L et al. (2011) Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation. RNA 17:1066-75
Samson, Abraham O; Levitt, Michael (2011) Normal modes of prion proteins: from native to infectious particle. Biochemistry 50:2243-8
Huang, Xuhui; Wang, Dong; Weiss, Dahlia R et al. (2010) RNA polymerase II trigger loop residues stabilize and position the incoming nucleotide triphosphate in transcription. Proc Natl Acad Sci U S A 107:15745-50
Minary, Peter; Levitt, Michael (2010) Conformational optimization with natural degrees of freedom: a novel stochastic chain closure algorithm. J Comput Biol 17:993-1010

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