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. Author manuscript; available in PMC: 2014 Aug 11.
Published in final edited form as: J Mol Model. 2010 Jul 3;17(4):899–911. doi: 10.1007/s00894-010-0784-7

Table 2. The hyperboloid parameters for FBPase and ATCase derived by the nonlinear fitting method.

FBPase AVG(stdv) a (largest) AVG(stdv) b (medium) AVG(stdv) c (smallest) trials used In estimation*
T state (1rdz) 179.85(0.08) 57.23(0.04) 55.67(0.01) 5
R state (1cnq) 189.28(0.08) 56.47(0.04) 56.19(0.01) 5
Difference -9.43 0.76 -0.52
% change -4.98 1.33 -0.92
ATCase
ASP domain AVG(stdv) a (largest) AVG(stdv) b (medium) AVG(stdv) c (smallest) trials used in estimation*
T state (1za1) 88.28(0.05) 49.35(0.18) 31.59(0.03) 4
R state (1d09) 83.59(0.05) 48.51(0.35) 31.48(0.06) 4
Difference 4.69 0.84 0.10
% change 5.31 1.70 0.03
CP domain
T state (1za1) 132.91(0.02) 56.05(0.26) 39.91(0.06) 4
R state (1d09) 132.10(0.03) 57.15(0.02) 39.39(0.01) 4
Difference 0.81 -1.10 0.52
% change 0.60 -1.96 1.30
reg domain
T state (1za1) 261.54(0.01) 90.88(0.01) 73.87(0.01) 4
R state (1d09) 257.19(0.01) 95.34(0.14) 71.90(0.05) 4
Difference 4.35 -4.46 1.96
% change 1.66 -4.90 2.66
*

In most cases, the range of error was rather small, so that numerous runs were unnecessary. We determined heuristically that 4–6 calculations were sufficient to obtain reliable parameter values. Convergence was reached with an F value of 0.0054 where F=fi+1fi is the convergence between cycles when improving the fit