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Structural and Chemical Biology @ Vanderbilt University |
Cryo-EM guided de novo Protein Fold Elucidation
Members: Steffen Lindert
all research projects
Using cryo-electron microscopy (cryoEM) numerous sub-nanometer resolution density maps of large macromolecular assemblies have been reported recently. Although generally no atomic detail is resolved in these density maps, at 7 Å resolution α-helices are observed as density rods. Primary goal of this project is the development of a computational protein structure prediction algorithm that incorporates the experimental cryoEM density map as restraints. The placement of helices is restricted to regions where density rods are observed in the cryoEM density map. The Monte Carlo based protein folding algorithm is further driven by knowledge based energy functions.
The method has been benchmarked with eleven highly α-helical proteins of known structure. The chosen proteins range in size from 250 to 350 residues. Starting with knowledge of the true secondary structure for these proteins, the method can identify the correct topology within the top scoring 10 models. With more realistic secondary structure prediction information, the correct topology is found within the top scoring 5 models for eight of the eleven proteins.
The algorithm has been applied to human adenovirus protein IIIa. This protein, for which there is no high resolution structure, is predicted to be highly α-helical. It is resolved in a 6.8Å resolution cryoEM adenovirus structure as a bundle of 14 α-helical density rods. A partial model where 11 out of 14 α-helices are predicted with a high confidence has been built for this crucial adenovirus capsid protein.
Members: Steffen Lindert
all research projects
Using cryo-electron microscopy (cryoEM) numerous sub-nanometer resolution density maps of large macromolecular assemblies have been reported recently. Although generally no atomic detail is resolved in these density maps, at 7 Å resolution α-helices are observed as density rods. Primary goal of this project is the development of a computational protein structure prediction algorithm that incorporates the experimental cryoEM density map as restraints. The placement of helices is restricted to regions where density rods are observed in the cryoEM density map. The Monte Carlo based protein folding algorithm is further driven by knowledge based energy functions.
The method has been benchmarked with eleven highly α-helical proteins of known structure. The chosen proteins range in size from 250 to 350 residues. Starting with knowledge of the true secondary structure for these proteins, the method can identify the correct topology within the top scoring 10 models. With more realistic secondary structure prediction information, the correct topology is found within the top scoring 5 models for eight of the eleven proteins.
The algorithm has been applied to human adenovirus protein IIIa. This protein, for which there is no high resolution structure, is predicted to be highly α-helical. It is resolved in a 6.8Å resolution cryoEM adenovirus structure as a bundle of 14 α-helical density rods. A partial model where 11 out of 14 α-helices are predicted with a high confidence has been built for this crucial adenovirus capsid protein.