analytical ultracentrifugation direct boundary modeling with sedfit

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This website provides information on Sedfit, a software for the analysis of analytical ultracentrifugation and other hydrodynamic data, written at the National Institutes of Health and distributed without charge for research use. The program can be downloaded from here, and it is also available directly from, or it can be sent as an email attachment from the author on request). This website was written by the author in his private capacity, and no official support or endorsement of NIH is intended and should be inferred.   See the disclaimer.

As documentation please refer to the following books, which have specific information on SEDFIT and SEDPHAT functions as side-bars nested into the description of theory and experiment of analytical ultracentrifugation (AUC):

A general introduction to the study of protein interactions by analytical ultracentrifugation can be found at the website of our lab at NIH (DMAS/LCIMB/NIBIB).  This includes an introduction to the general principles of AUC and detailed experimental protocols.  

 The main purpose of this website is to provide additional information for using the software, such as an online help reference information, and background information (tutorials) on direct boundary modeling, FAQs, references, examples and more. 



for upcoming workshops: see







Please join the Sedfit Users Group email list (reaches currently > 900 AUC users) for technical advice and discussions with other users on software issues and applications. You can also sign up to the SEDPHAT-L listserv for discussion of applications of SEDPHAT and questions of global analysis of ITC, AUC, DLS, SPR, and other biophysical techniques.

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The main features of Sedfit include: 

bulletanalysis of data from:  sedimentation velocity, dynamic light scattering, and sedimentation equilibrium.  Sedimentation velocity data can be acquired using absorbance or interference optics, in conventional loading configuration or synthetic boundary, analytical zone centrifugation, and different cell geometries bulletcontinuous size-distributions c(s) with many variants of prior knowledge for sedimentation velocity analysis with maximum entropy regularization bulletsize-and-shape distributions c(s,M) bulletBayesian incorporation of prior for enhanced resolution of distributions bulletLamm equation models for: discrete non-interacting species, self-associating systems (1-2, 1-3, 1-2-4, 1-4-8, 1-m-n), non-ideal sedimentation --> see extension for global analysis of interacting systems in Sedphat bulletapparent sedimentation coefficient distribution ls-g*(s) and van Holde-Weischet analysis G(s) (both for absorbance and interference data) bulletall sedimentation velocity models for direct boundary modeling with algebraic noise elimination bulletcorrections for water compressibility (and compressible organic solvents) bulletcontinuous size-distribution models for dynamic light scattering and sedimentation equilibrium  bullet

numerous statistical functions, loading options, and general purpose tools bullet

extension to global analysis in Sedphat

Sedfit is closely related to Sedphat, which provides global modeling capability for both sedimentation equilibrium and sedimentation velocity data.  It also can serve as a platform for the global analysis of a variety of isotherms from different biophysical disciplines.  horizontal rule


bulletaccounts for finite time of absorbance scanning bulletExtended multi-threading support for faster analysis on computers with multi-core processors bulletBayesian prior knowledge for enhanced resolution of c(s) (links to paper and how to use in SEDFIT) bulletfaster Lamm equation solutions (description) bulletnew optimization tools: Marquardt-Levenberg and simulated annealing (how to use in SEDFIT) bulletreal-time testing for attainment of sedimentation equilibrium (how to use in SEDFIT) bullet multi-thread support bulletnew user interface (new color scheme, wizard messages, graphical editing of data points and scans, dragging limits with the mouse, quick change of cells, pre-set limits for automatic integration) bulletversion 9.4: new specialized c(s) models (wormlike chain, floating vbar), extended calculator, display, and other utility functions (improved 6-channel data support) bulletversion 9.3: size-and-shape distributions c(s,f) and c(s,M), and the scale-relationship free general c(s,*) bulletsounds, serialization of analysis (apply the same analysis automatically to data from several cells) bulletversion 9.2:  optional speeding up of c(s) analyses by slight radial pre-averaging of scans, faster Lamm equation solutions with permeable bottom model, c(s) model with user-defined M-s relationship, optional updating of display while fiitting, restoring previous c(s) analyses, loading data via drag-and-drop, importing SEDPHAT xp-files, and other new and improved utility functions. bulletnew tutorials on  solvent compressibility, and self-forming density gradients bulletnew Sedphat hybrid bimodal c(s)-discrete species model combining 2 c(s) distributions with discrete species, for example allowing to link molar mass into integral multiples bulletversion 8.9: Export data to Sedphat, and spawn Sedphat automatically with current data (and c(s) hybrid model). bulletnew in version 8.8 (03/04): maintenance update with a few bug fixes and added flexibility bulletnew in version 8.7 (09/03): sedimentation in inhomogeneous solvents, like sedimenting co-solutes and compressible solvents, including corrections for water compressibility; extended Monte-Carlo error analysis for weight-average s-values or concentrations of trace components; differential second moment method for sw; faster c(s) fitting; improved ellipsoid shape calculator; bimodal c(s) with two f/f0 parameters, back-transform for g(s*), several new utilities and bug fixes bulletnew mode for REALTIME analysis



bulletBayesian analysis of trace components (AAPS Journal 10 (2008) 481-493) bulletBayesian prior knowledge (Biomacromolecules 8 (2007) 2011-2024) bulletnew Lamm equation solutions (Computer Physics Communications in press) bulletsize-and-shape distributions c(s,M) and scale-free general c(s,*) (Biophysical Journal 90 (2006) 4651-4661 full text ) bulletinterpreting c(s) for reacting systems:  Biophysical Journal 89:651-666 and Biophysical Journal 89:619-634 bullettutorial review of analytical ultracentrifugation in protein science (Protein Science 11 (2002) 2067-2079)   bulletnew review of the strategies and numerical methods for sedimentation coefficient distributions in Methods in Enzymology (in press) bulletstudies of strategies for using sedimentation velocity for protein self-association: Analytical Biochemistry 320:104-124 bulletmacromolecular sedimentation in the presence of sedimenting co-solutes (dynamic density gradients): Biophysical Chemistry 108:187-200 bulletcorrections for solvent compressibility Biophysical Chemistry 108:201-214 horizontal rule

A snapshot from the direct boundary modeling of data from a sample of IgG, analyzed with a c(s) distribution of Lamm equation solutions illustrates the rich information contained in sedimentation velocity data. 

This example also illustrates some of the main ideas of Sedfit: loading data from the entire sedimentation process, use of systematic noise decomposition (and subtraction), modeling with finite element solutions of the Lamm equation.  If we expand the scale of the continuous sedimentation distribution c(s) with maximum entropy regularization shown above, it can be seen that the c(s) analysis reveals the presence of several oligomeric species and a smaller species:

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Similarly, the analysis of data from a BSA sample

shows the presence of dimer and trimer in the sedimentation coefficient distribution c(s)

Transformation to a molar mass distribution (assuming that all species have a similar frictional ratio) is consistent with the oligomeric BSA species:

Analyses free of scale-relation ship assumptions of similar frictional ratios are available, such as the two-dimensional size-and-shape distribution c(s,f), c(s,M), and the general c(s,*).  

Sedfit offers several different options for utilizing different prior knowledge on the sample under study (such as the number of discrete species, a self-association model, etc.).  More information on these examples can be found in the step-by-step tutorial (BSA) and the example for using Sedfit (IgG). 

For questions please contact the Sedfit Users Group email list .

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search SEDFIT and SEDPHAT websites

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The information contained herein is provided as a service with the understanding that the author makes no warranties, either expressed or implied, concerning the accuracy, completeness, reliability, or suitability of the information.


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