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Normal Mode Flexible Fitting of High-Resolution Structures of Biological Molecules Toward SAXS Data.

Gorba C, Tama F - Bioinform Biol Insights (2010)

Bottom Line: We present a method to reconstruct a three-dimensional protein structure from an atomic pair distribution function derived from the scattering intensity profile from SAXS data by flexibly fitting known x-ray structures.For computational efficiency, the protein and water molecules included in the protein first hydration shell are coarse-grained.Illustrative results of our flexible fitting studies on simulated SAXS data from five different proteins are presented.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and Biochemistry, The University of Arizona, 1041 E. Lowell Street, Tucson, AZ, 85721.

ABSTRACT
We present a method to reconstruct a three-dimensional protein structure from an atomic pair distribution function derived from the scattering intensity profile from SAXS data by flexibly fitting known x-ray structures. This method uses a linear combination of low-frequency normal modes from an elastic network description of the molecule in an iterative manner to deform the structure to conform optimally to the target pair distribution function derived from SAXS data. For computational efficiency, the protein and water molecules included in the protein first hydration shell are coarse-grained. In this paper, we demonstrate the validity of our coarse-graining approach to study SAXS data. Illustrative results of our flexible fitting studies on simulated SAXS data from five different proteins are presented.

No MeSH data available.


(a) Progress of fitting process measured by fitness scoring function for the LAO binding protein. (b) Progress of RMSD and relative change in the scoring function over the fitting process. The progress of the RMSD is shown in black and the relative change of scoring functions in gray. The relative change reaches zero when the RMSD is close to the minimum.
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f2-bbi-2010-043: (a) Progress of fitting process measured by fitness scoring function for the LAO binding protein. (b) Progress of RMSD and relative change in the scoring function over the fitting process. The progress of the RMSD is shown in black and the relative change of scoring functions in gray. The relative change reaches zero when the RMSD is close to the minimum.

Mentions: Figure 2a shows the behavior of the scoring function as a function of iteration step. The scoring function converges slowly toward 0. However, after the RMSD value decreases to a minimum, it starts to increase again (Fig. 2b) even though the scoring function does not improve much. This indicates that the data is over fitted. Due to the low-resolution nature of the SAXS data, the scoring function may continue to increase while additional displacements do not necessarily decrease the RMSD, i.e. a continued increase of the scoring function does not always improve the quality of the model. Therefore, one needs to define a more stringent parameter than the convergence of the scoring function in order to decide whether the refinement has found a reasonable model, or if the additional displacements observed are only artifacts.


Normal Mode Flexible Fitting of High-Resolution Structures of Biological Molecules Toward SAXS Data.

Gorba C, Tama F - Bioinform Biol Insights (2010)

(a) Progress of fitting process measured by fitness scoring function for the LAO binding protein. (b) Progress of RMSD and relative change in the scoring function over the fitting process. The progress of the RMSD is shown in black and the relative change of scoring functions in gray. The relative change reaches zero when the RMSD is close to the minimum.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2901630&req=5

f2-bbi-2010-043: (a) Progress of fitting process measured by fitness scoring function for the LAO binding protein. (b) Progress of RMSD and relative change in the scoring function over the fitting process. The progress of the RMSD is shown in black and the relative change of scoring functions in gray. The relative change reaches zero when the RMSD is close to the minimum.
Mentions: Figure 2a shows the behavior of the scoring function as a function of iteration step. The scoring function converges slowly toward 0. However, after the RMSD value decreases to a minimum, it starts to increase again (Fig. 2b) even though the scoring function does not improve much. This indicates that the data is over fitted. Due to the low-resolution nature of the SAXS data, the scoring function may continue to increase while additional displacements do not necessarily decrease the RMSD, i.e. a continued increase of the scoring function does not always improve the quality of the model. Therefore, one needs to define a more stringent parameter than the convergence of the scoring function in order to decide whether the refinement has found a reasonable model, or if the additional displacements observed are only artifacts.

Bottom Line: We present a method to reconstruct a three-dimensional protein structure from an atomic pair distribution function derived from the scattering intensity profile from SAXS data by flexibly fitting known x-ray structures.For computational efficiency, the protein and water molecules included in the protein first hydration shell are coarse-grained.Illustrative results of our flexible fitting studies on simulated SAXS data from five different proteins are presented.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and Biochemistry, The University of Arizona, 1041 E. Lowell Street, Tucson, AZ, 85721.

ABSTRACT
We present a method to reconstruct a three-dimensional protein structure from an atomic pair distribution function derived from the scattering intensity profile from SAXS data by flexibly fitting known x-ray structures. This method uses a linear combination of low-frequency normal modes from an elastic network description of the molecule in an iterative manner to deform the structure to conform optimally to the target pair distribution function derived from SAXS data. For computational efficiency, the protein and water molecules included in the protein first hydration shell are coarse-grained. In this paper, we demonstrate the validity of our coarse-graining approach to study SAXS data. Illustrative results of our flexible fitting studies on simulated SAXS data from five different proteins are presented.

No MeSH data available.