New Strategies for Protein Folding Joseph F. Danzer, Derek A. Debe, Matt J. Carlson, William A. Goddard III Materials and Process Simulation Center California.

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Presentation transcript:

New Strategies for Protein Folding Joseph F. Danzer, Derek A. Debe, Matt J. Carlson, William A. Goddard III Materials and Process Simulation Center California Institute of Technology

…-HIS-CYS-ALA-ALA-GLY-GLU-ASP-... Protein Tertiary Structure Prediction Given a Protein’s Primary Structure -- Amino Acid Sequence Can We Determine It’s 3D Structure What Local Structural Units Does It Form?  -Helix (Cylinder)  -Sheets (Ribbon) How Do Those Structural Units Pack Together?

With a 6 (  ) state representation, 6 50 or states for a 50 residue protein Assuming protein may sample 1state/ps, years to fold Conformational Search Problem –Given the exponentially large number of possible states, how do we generate a correct state? Recognition Problem –How do we differentiate correct from incorrect folds? Structure Prediction is a Two Fold Problem

Restrained Generic Protein (RGP) Direct Monte Carlo Highly efficient, off-lattice residue buildup procedure for generating ensembles of protein conformations that comply with a set of user defined distance restraints.  l  l = 3.8 Å;  = 120; Typically  = 0, 60, 120, 180, 240, 300. (6 states per residue) Generic Protein Model Each residue is a 5.5 Å sphere Fixed geometry connects residues

Restraint Implementation At residue addition step i, the maximal position of residue i+n in the (z,r) plane is known. Satisfies pairwise restraints with >90% efficiency with negligible computational cost. Leads to a simple set of trigonometric conditions for restraint satisfaction.

RGP Ensemble Generation Inter-residue restraints Secondary structure prediction Static Residue Burial Selection <10 4 topologies <500 topologies Intact Peptide Backbone Dynamic Residue Burial Selection Additional Restraints <20 topologies Additional Refinement <10 topologies <5 topologies Amino Acid Sequence Generate-and-Select Hierarchy Local Structure Refinement

Secondary Structure Prediction-PHD Burkhard Rost & Chris Sander, J. Mol. Biol. 232, 584 (1993). LexA Repressor

Myoglobin

Inter-Residue Restraints If tertiary structure is unknown, How can we generate distance restraints? Experimentally determined disulfide bond connectivity Use PHD prediction algorithm to generate loose restraints 1 1. Burkhard Rost & Chris Sander, J. Mol. Biol. 232, 584 (1993). PHD predicts whether each residue will be buried or exposed to solvent Assume the residues with greatest burial form a hydrophobic core Generate a few loose restraints (4-10 Å) between these residues Tests on two proteins (3icb,1lea) using loose restraints were done

Local Structure Refinement Dynamic Monte Carlo –Make small local deformations to the backbone structureMake small local deformations to the backbone structure –Overall topology must be kept intactOverall topology must be kept intact –Use simple energy function to determine if deformation is accepted or rejectedUse simple energy function to determine if deformation is accepted or rejected Fragment Sewing –Isites 1 library is a database of structural fragments widely observed in the Protein Data Bank.Isites 1 library is a database of structural fragments widely observed in the Protein Data Bank. –Based on sequence homology, Isites will generate a list of fragments whose structures are likely to be found in the proteinBased on sequence homology, Isites will generate a list of fragments whose structures are likely to be found in the protein –Local structure can be refined by sewing these fragments into the overall structureLocal structure can be refined by sewing these fragments into the overall structure 1. C. Bystroff & D. Baker, J. Mol. Bol. 281, 565 (1998).

Dynamic Monte Carlo Local deformations are made by modifying the position of a single residue. Energy function properly orients side chains. Hydrophilic groups point outward and hydrophobic groups point inward. Axis of rotation Circle defines allowed movement based on fixed geometry of model C-  Atoms Hydrophilic Side Chain Hydrophobic Side Chain

Fragment Sewing Rest of protein Segment’s original structure New structure after sewing Overall topology is still intact, but now local structure has  -helical structure rather than a random coil.