Biology – Quantitative Biology – Biomolecules
Scientific paper
2006-11-09
Biology
Quantitative Biology
Biomolecules
17 pages, 4 figures
Scientific paper
Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validation is typically limited to individual modes for a single protein. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational variance. Normal modes from four simple elastic network model potentials and from the CHARMM forcefield are calculated for a data set of 83 diverse, ultrahigh resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, the methods that consider all atoms have a clear edge at prediction of directionality, and the CHARMM potential produces the best agreement. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. This was illustrated by computing the dynamic correlation matrices from different potentials for a PDZ domain structure. Comparison of normal mode results with anisotropic temperature factors opens the possibility of using ultrahigh resolution crystallographic data as a quantitative measure of molecular flexibility. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity. Comparison of the dynamic correlation matrices suggests that a combination of topological and chemical potentials may help identify residues in which chemical forces make large contributions to intramolecular coupling.
Bannen Ryan M.
Cui Qiang
Kondrashov Dmitry A.
Phillips George N. Jr.
Van Wynsberghe Adam W.
No associations
LandOfFree
Protein structural variation in computational models and crystallographic data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Protein structural variation in computational models and crystallographic data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Protein structural variation in computational models and crystallographic data will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-408203