Calculation of hydrodynamic parameters of proteins from crystallographic data using multibody approaches
Bead models, composed of overlapping spheres of different or identical radii, were generated from the crystal structures of selected proteins (citrate synthase, lactate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase). Starting from the atomic coordinates taken from the Brookhaven Protein Data Bank, different methods (“running mean” or “cubic grid”) were used for reducing the initially large number of coordinates to a practicable size. The structure of the multibody models was checked by comparing the pair distance distribution function and the radius of gyration of the reduced models with the corresponding quantities of an initial model. For models consisting of beads of unequal size both reduction methods may be used. The concept of equal beads was found to be compatible with the “running mean” method, whereas the use of the “cubic grid” method for generating models composed of equal spheres led to quite distorted structures. The program HYDRO (by García de la Torre et al. (1994) Biophys J 67:530–531) was applied for predictions of the sedimentation coefficient s, diffusion coefficient D, and intrinsic viscosity [η]. Reliable and numerically stable results were only obtained after the program had been modified in order to improve the treatment of overlapping unequal spheres. The hydrodynamic parameters thus predicted were compared with experimental values as well as with the results from whole-body approaches. In general, a good conformity of predicted and observed values for s and D, including the extent of changes due to conformational alterations, was obtained by multibody approaches if appropriate refinements of the models with respect to radius of gyration and volume were applied. By contrast, the values predicted for [η] usually exceeded the experimental results considerably.
Key wordsProteins analytical ultracentrifugation viscometry scattering crystal structure parameter predictions modeling multibody approaches
Unable to display preview. Download preview PDF.
- 1.Harding SE (1989) In: Harding SE, Rowe AJ (eds) Dynamic Properties of Biomolecular Assemblies. Royal Society of Chemistry, Cambridge UK, pp 32–56Google Scholar
- 2.García del la Torre J (1989) In: Harding SE, Rowe AJ (eds) Dynamic Properties of Biomolecular Assemblies. Royal Society of Chemistry, Cambridge UK, pp 3–31Google Scholar
- 3.Glatter O (1972) Acta Phys Austriaca 36:307–315Google Scholar
- 4.Glatter O (1982) In: Glatter O, Kratky O (eds) Small Angle X-ray Scattering. Academic Press, London, pp 119–165Google Scholar
- 9.Durchschlag H, Zipper P (1997) Progr Colloid Polym Sci 107:43–57Google Scholar
- 14.Glatter O (1980) Acta Phys Austriaca 52:243–256Google Scholar
- 19.Durchschlag H, Zipper P (1997) J Appl Cryst (in press)Google Scholar
- 21.Pessen H, Kumosinski TF (1993) In: Baianu IC, Pessen H, Kumosinski TF (eds) Physical Chemistry of Food Processes, Vol 2: Advanced Techniques, Structures and Applications. Van Nostrand Reinhold, New York, pp 274–306Google Scholar
- 25.García de la Torre J (1997) Personal communicationGoogle Scholar