subset |
|
subset of proteins common to all prediction methods applied
|
nprot |
|
number of proteins in subset of proteins common to all
prediction methods applied
|
Method |
|
name of prediction method
|
-lnP_MMT |
|
negative log of P-value assigned to model structure by MAMMOTH
|
Sgnf(-lnP_MMT) |
|
number of medels achieving -ln(P-value) score from MAMMOTH
above a value of 4.5
|
Z_CE |
|
Z-score assigned to a model structure by CE
|
Sgnf(Z_CE) |
|
number of models achieving CE Z-score above a value of 3.7
|
Q_LG1 |
|
quality score of a model structure from LG1 program
|
Sgnf(Q_LG1) |
|
number of medels achieving LG1 Q-score above a value of 2.0
|
Q_LG2 |
|
quality score of a model structure from LG2 program
|
Sgnf(Q_LG2) |
|
number of medels achieving LG2 Q-score above a value of 2.0
|
NXA_Gbl% |
|
percentage of equivalent residues within a distance of X Angstroms
in a global superposition of model and experimental
structures
|
NXA_ext_CE% |
|
percentage of target residues found within a distance of X
Angstroms in structural alignment of model and target
structures seeded by CE and extended iteratively
|
ModN% |
|
percent of target residues in model produced by method
|
score |
|
Let Nb - number of times server is signinficantly better
than other servers.
Let Nw - number of times server is signinficantly worse
than other servers.
Let N -number of times a comparison can be made.
Than: score = 10 * ( Nb - Nw )/N .
The significance is computed using Student t-distribution
|