CASP1 - CASP9
(* results recalculated with new /updated structure comparison programs in 2019)
Menu
`
TS Analysis : Z-score based relative group performance

    Models:

    • Ranking on the models designated as "1"
    • Ranking on best models

    Groups:

    • All groups on 'all groups' targets
    • Server groups on 'all groups' + 'server only' targets

    Formula and Domains:

      The ranking of groups is based on the analysis of zscores for GDT_TS.
    • easy (TBM)
    • medium (TBM/FM)
    • hard (FM)
 
 
  #   GR
  code 		   GR
  name		   Domains   
  Count		   SUM Zscore   
  (>-2.0)		   Rank SUM Zscore   
  (>-2.0)		   AVG Zscore   
  (>-2.0)		   Rank AVG Zscore   
  (>-2.0)		   SUM Zscore   
  (>0.0)		   Rank SUM Zscore   
  (>0.0)		   AVG Zscore   
  (>0.0)		   Rank AVG Zscore   
  (>0.0)
1 426 Zhang-Server 164 109.4000 1 0.6671 1 111.0290 1 0.6770 1
2 409 pro-sp3-TASSER 164 80.3750 2 0.4901 3 85.9140 2 0.5239 3
3 425 BAKER-ROBETTA 164 77.4150 3 0.4720 4 83.6360 3 0.5100 4
4 438 RAPTOR 153 76.5690 4 0.5005 2 83.5950 4 0.5464 2
5 182 METATASSER 164 70.4010 5 0.4293 5 81.8990 5 0.4994 5
6 322 Phyre_de_novo 164 69.7090 6 0.4251 6 79.6520 6 0.4857 6
7 012 HHpred5 164 47.6710 15 0.2907 15 78.8310 7 0.4807 7
8 122 HHpred4 164 51.3240 11 0.3130 11 74.6750 8 0.4553 8
9 013 MULTICOM-REFINE 164 60.6240 8 0.3697 8 74.0060 9 0.4513 9
10 020 MULTICOM-CLUSTER 164 61.8900 7 0.3774 7 74.0030 10 0.4512 10
11 443 MUProt 164 57.9060 10 0.3531 10 72.1630 11 0.4400 11
12 256 SAM-T08-server 164 58.8870 9 0.3591 9 72.1290 12 0.4398 12
13 154 HHpred2 164 49.1200 12 0.2995 12 69.1830 13 0.4218 13
14 131 MULTICOM-RANK 164 48.6580 13 0.2967 13 67.6720 14 0.4126 15
15 429 Pcons_multi 161 45.8200 16 0.2846 16 66.9470 15 0.4158 14
16 408 MUSTER 164 48.1730 14 0.2937 14 66.1850 16 0.4036 16
17 069 MULTICOM-CMFR 164 45.5980 17 0.2780 17 63.3170 17 0.3861 17
18 351 FALCON 156 30.3750 22 0.1947 22 59.2390 18 0.3797 18
19 166 FEIG 158 26.8580 24 0.1700 26 57.4430 19 0.3636 20
20 048 PS2-server 160 33.6770 20 0.2105 20 57.3580 20 0.3585 21
21 436 Pcons_dot_net 154 37.2790 18 0.2421 18 56.6580 21 0.3679 19
22 385 PSI 164 36.0420 19 0.2198 19 55.2880 22 0.3371 23
23 116 fais-server 158 27.1700 23 0.1720 25 53.6260 23 0.3394 22
24 235 Phyre2 163 31.7810 21 0.1950 21 52.3350 24 0.3211 26
25 142 FFASsuboptimal 158 26.2460 25 0.1661 27 50.9850 25 0.3227 25
26 270 Phragment 163 25.9390 26 0.1591 28 50.8620 26 0.3120 27
27 427 3DShot2 164 22.7510 28 0.1387 29 50.3230 27 0.3068 32
28 220 FALCON_CONSENSUS 158 -4.2220 41 -0.0267 41 48.8980 28 0.3095 31
29 415 keasar-server 149 19.9330 32 0.1338 31 48.7650 29 0.3273 24
30 495 BioSerf 162 18.7900 34 0.1160 34 47.7370 30 0.2947 34
31 193 CpHModels 155 16.7200 35 0.1079 36 46.9090 31 0.3026 33
32 135 pipe_int 151 20.7640 30 0.1375 30 46.7420 32 0.3095 30
33 186 Poing 163 20.2320 31 0.1241 32 46.2130 33 0.2835 37
34 100 nFOLD3 160 12.5370 38 0.0784 38 45.9920 34 0.2874 36
35 007 FFASstandard 157 18.9470 33 0.1207 33 45.9810 35 0.2929 35
36 247 FFASflextemplate 156 13.7490 37 0.0881 37 43.0760 36 0.2761 38
37 143 Pcons_local 155 0.7590 40 0.0049 40 40.2850 37 0.2599 41
38 296 3D-JIGSAW_AEP 122 22.9960 27 0.1885 23 37.9010 38 0.3107 29
39 349 mGenTHREADER 142 15.4190 36 0.1086 35 37.8800 39 0.2668 40
40 085 Frankenstein 138 -4.9080 42 -0.0356 42 36.9670 40 0.2679 39
41 449 3D-JIGSAW_V3 118 21.6450 29 0.1834 24 36.7790 41 0.3117 28
42 477 SAM-T06-server 164 1.1530 39 0.0070 39 36.7170 42 0.2239 42
43 316 forecast 160 -47.4510 49 -0.2966 47 30.4250 43 0.1902 43
44 421 SAM-T02-server 153 -16.1020 44 -0.1052 43 28.9450 44 0.1892 44
45 157 3Dpro 157 -31.9170 47 -0.2033 45 27.1730 45 0.1731 46
46 019 FUGUE_KM 147 -26.5520 45 -0.1806 44 25.1700 46 0.1712 47
47 243 Pushchino 125 -29.2890 46 -0.2343 46 22.8960 47 0.1832 45
48 454 LOOPP_Server 142 -51.3500 50 -0.3616 49 21.8430 48 0.1538 48
49 002 ACOMPMOD 150 -57.4350 52 -0.3829 50 21.0470 49 0.1403 50
50 318 panther_server 136 -45.7520 48 -0.3364 48 19.0830 50 0.1403 49
51 462 MUFOLD-Server 137 -64.8300 53 -0.4732 51 16.8980 51 0.1233 51
52 404 MUFOLD-MD 134 -159.3800 58 -1.1894 57 13.4660 52 0.1005 53
53 164 FOLDpro 164 -164.2240 59 -1.0014 55 11.8370 53 0.0722 54
54 281 huber-torda-server 100 -53.7330 51 -0.5373 52 10.2940 54 0.1029 52
55 095 rehtnap 147 -105.8200 55 -0.7199 54 8.1040 55 0.0551 55
56 073 Distill 162 -103.0720 54 -0.6362 53 7.2320 56 0.0446 57
57 450 mariner1 119 -130.6330 56 -1.0978 56 5.7360 57 0.0482 56
58 262 schenk-torda-server 146 -256.1290 60 -1.7543 59 0.4250 58 0.0029 58
59 053 mahmood-torda-server 77 -136.5630 57 -1.7735 60 0.1390 59 0.0018 59
60 274 BHAGEERATH 5 -8.7520 43 -1.7504 58 0.0000 60 0.0000 60
The cummulative z-scores in this table are calculated according to the following procedure (example for the "first" models):
1. Calculate z-scores from the raw scores for all "first" models (corresponding values from the main result table);
2. Remove outliers - models with zscores below the tolerance threshold (set to -2.0);
3. Recalculate z-scores on the reduced dataset;
4. Assign z-scores below the penalty threshold (either -2.0 or 0.0) to the value of this threshold.
Protein Structure Prediction Center
Sponsored by the US National Institute of General Medical Sciences (NIH/NIGMS)
Please address any questions or queries to:
© 2007-2026, University of California, Davis