Combining efficient conformational sampling with a deformable elastic network model facilitates structure refinement at low resolution.
Structural studies of large proteins and protein assemblies are a difficult and pressing challenge in molecular biology. Experiments often yield only low-resolution or sparse data that are not sufficient to fully determine atomistic structures. We have developed a general geometry-based algorithm that efficiently samples conformational space under constraints imposed by low-resolution density maps obtained from electron microscopy or X-ray crystallography experiments. A deformable elastic network (DEN) is used to restrain the sampling to prior knowledge of an approximate structure. The DEN restraints dramatically reduce over-fitting, especially at low resolution. Cross-validation is used to optimally weight the structural information and experimental data. Our algorithm is robust even for noise-added density maps and has a large radius of convergence for our test case. The DEN restraints can also be used to enhance reciprocal space simulated annealing refinement.
Schröder
Gunnar F
GF
Department of Structural Biology, Stanford University Stanford, CA 94305, USA. gschroe@stanford.edu
Brunger
Axel T
AT
Levitt
Michael
M
D000465:Algorithms; D018360:Crystallography, X-Ray; D004548:Elasticity; D008958:Models, Molecular; D011487:Protein Conformation