Sedimentation equilibrium analysis of protein interactions with global implicit mass conservation constraints and systematic noise decomposition.
Sedimentation equilibrium is a powerful tool for the characterization of protein self-association and heterogeneous protein interactions. Frequently, it is applied in a configuration with relatively long solution columns and with equilibrium profiles being acquired sequentially at several rotor speeds. The present study proposes computational tools, implemented in the software SEDPHAT, for the global analysis of equilibrium data at multiple rotor speeds with multiple concentrations and multiple optical detection methods. The detailed global modeling of such equilibrium data can be a nontrivial computational problem. It was shown previously that mass conservation constraints can significantly improve and extend the analysis of heterogeneous protein interactions. Here, a method for using conservation of mass constraints for the macromolecular redistribution is proposed in which the effective loading concentrations are calculated from the sedimentation equilibrium profiles. The approach is similar to that described by Roark (Biophys. Chem. 5 (1976) 185-196), but its utility is extended by determining the bottom position of the solution columns from the macromolecular redistribution. For analyzing heterogeneous associations at multiple protein concentrations, additional constraints that relate the effective loading concentrations of the different components or their molar ratio in the global analysis are introduced. Equilibrium profiles at multiple rotor speeds also permit the algebraic determination of radial-dependent baseline profiles, which can govern interference optical ultracentrifugation data, but usually also occur, to a smaller extent, in absorbance optical data. Finally, the global analysis of equilibrium profiles at multiple rotor speeds with implicit mass conservation and computation of the bottom of the solution column provides an unbiased scale for determining molar mass distributions of noninteracting species. The properties of these tools are studied with theoretical and experimental data sets.
Vistica
Jennifer
J
Protein Biophysics Resource, Division of Bioengineering & Physical Science, ORS, OD, National Institutes of Health, Bethesda, MD 20892, USA.
Dam
Julie
J
Balbo
Andrea
A
Yikilmaz
Emine
E
Mariuzza
Roy A
RA
Rouault
Tracey A
TA
Schuck
Peter
P
D046911:Macromolecular Substances; D008433:Mathematics; D008962:Models, Theoretical; D011485:Protein Binding; D011506:Proteins / Q000032:analysis* / Q000302:isolation & purification*; D012984:Software; D014461:Ultracentrifugation / Q000379:methods
D046911:Macromolecular Substances; D011506:Proteins