because the model is inappropriate, the calculated molar mass
of 27.4 kDa is definitively wrong, half the theoretical value for
the monomer. This is because boundary spreading is interpreted by the program—in the framework of the incorrect
model—as caused by diffusion, while heterogeneity affects
more predominantly the shape of the boundaries. In conclusion, the non-interacting species model provides valuable
results, but only when this model is appropriate.
3.3 Heteroassociation Data
Analysis
with BTG2-PABP
Mixtures Data Sets
We illustrate here c(s) analysis in SEDFIT and integration of the
sedimentation coefficient distributions in GUSSI to determine s w
and s fast values, reflecting the properties of the interacting system.
Isotherm analysis in SEDPHAT.
1. Perform a c(s) analysis in SEDFIT with SV data for each mixture of the titration series and save the corresponding c(s)
distribution in GUSSI format. Open the program SEDFIT
and perform the c(s) analysis with SV data for the first mixture.
Excellent fit of the raw data is essential for the accuracy of
parameters determined in next steps. The c(s) distribution is
exported to GUSSI using Plot/Gussi c(s) plot, then accept the
GUSSI terms in the GUSSI window that appears, select File/
Save data only, choose the correct directory, give a name to the
file and save. A file *.gcofs is created. Then File/quit. Proceed
the same way for all SV data.
2. Superimpose all the c(s) distributions corresponding to the
titration series and transform experimental c(s) in corrected c
(s) distributions (Fig. 3). Open GUSSI, accept terms and press
OK in the GUSSI window with default c(s) selection. In the
GUSSI c(s) module window, select File/Add Distribution and
load the GUSSI file *.gcofs corresponding to the first c(s)
distribution (corresponding to the lowest concentration of
A). Repeat the loading procedure in ascending order of concentration for all the c(s) distributions. Select Axis/Standardization/Standardization, then Axis/Standardization/Modify
Standardization Parameters, and enter experimental conditions
in Standardization window for density, viscosity, partial specific
volume (values at experimental temperature and 20
C), and
temperature. Then press propagate to all distributions and
finally press commit. The experimental c(s) distributions are
transformed to corrected c(s) distributions. Then File/Save
GUSSI State to save the corrected c(s) superimposition file.
This *.gussi file can be recalled if needed.
3. Integrate all the superimposed corrected c(s) distributions to
generate the signal-weighted average s w isotherm file (Fig. 4).
In GUSSI, select Integration/Make Isotherm/Hetero/sw,
press no for the exclusion zone selection (see Note 8), and
Heterogeneity and Affinity Interaction Analysis by Sedimentation Velocity
165
of 27.4 kDa is definitively wrong, half the theoretical value for
the monomer. This is because boundary spreading is interpreted by the program—in the framework of the incorrect
model—as caused by diffusion, while heterogeneity affects
more predominantly the shape of the boundaries. In conclusion, the non-interacting species model provides valuable
results, but only when this model is appropriate.
3.3 Heteroassociation Data
Analysis
with BTG2-PABP
Mixtures Data Sets
We illustrate here c(s) analysis in SEDFIT and integration of the
sedimentation coefficient distributions in GUSSI to determine s w
and s fast values, reflecting the properties of the interacting system.
Isotherm analysis in SEDPHAT.
1. Perform a c(s) analysis in SEDFIT with SV data for each mixture of the titration series and save the corresponding c(s)
distribution in GUSSI format. Open the program SEDFIT
and perform the c(s) analysis with SV data for the first mixture.
Excellent fit of the raw data is essential for the accuracy of
parameters determined in next steps. The c(s) distribution is
exported to GUSSI using Plot/Gussi c(s) plot, then accept the
GUSSI terms in the GUSSI window that appears, select File/
Save data only, choose the correct directory, give a name to the
file and save. A file *.gcofs is created. Then File/quit. Proceed
the same way for all SV data.
2. Superimpose all the c(s) distributions corresponding to the
titration series and transform experimental c(s) in corrected c
(s) distributions (Fig. 3). Open GUSSI, accept terms and press
OK in the GUSSI window with default c(s) selection. In the
GUSSI c(s) module window, select File/Add Distribution and
load the GUSSI file *.gcofs corresponding to the first c(s)
distribution (corresponding to the lowest concentration of
A). Repeat the loading procedure in ascending order of concentration for all the c(s) distributions. Select Axis/Standardization/Standardization, then Axis/Standardization/Modify
Standardization Parameters, and enter experimental conditions
in Standardization window for density, viscosity, partial specific
volume (values at experimental temperature and 20
C), and
temperature. Then press propagate to all distributions and
finally press commit. The experimental c(s) distributions are
transformed to corrected c(s) distributions. Then File/Save
GUSSI State to save the corrected c(s) superimposition file.
This *.gussi file can be recalled if needed.
3. Integrate all the superimposed corrected c(s) distributions to
generate the signal-weighted average s w isotherm file (Fig. 4).
In GUSSI, select Integration/Make Isotherm/Hetero/sw,
press no for the exclusion zone selection (see Note 8), and
Heterogeneity and Affinity Interaction Analysis by Sedimentation Velocity
165