| 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 |