15th Community Wide Experiment on the
Critical Assessment of Techniques for Protein Structure Prediction
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Interdomain Predictions Analysis : Group performance based on combined z-scores
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The final ranking was based on the models designated as "1" according to the formula: Z-score(F1) + Z-score(Jaccard score) + Z-score(QS_best).
    #     GR
    name		     GR
    code		     Targets Count     SUM Zscore
    (>0.0)		     Rank SUM Zscore
    (>0.0)		     AVG Zscore
    (>0.0)		     Rank AVG Zscore
    (>0.0)
1 UM-TBM 162 20 35.5277 1 1.7764 1
2 Yang-Server 229 20 24.9602 2 1.2480 2
3 Yang 439 20 19.7115 3 0.9856 4
4 PEZYFoldings 278 15 18.0578 4 1.2039 3
5 Manifold 248 19 14.9308 5 0.7858 5
6 Venclovas 494 19 14.5386 6 0.7652 6
7 server_124 383 20 14.0810 7 0.7040 7
8 DFolding 074 20 13.1098 8 0.6555 9
9 bench 008 20 12.0811 9 0.6041 12
10 BAKER-SERVER 443 20 12.0030 10 0.6002 13
11 Manifold-E 035 19 11.6732 11 0.6144 10
12 DFolding-server 288 19 11.5870 12 0.6098 11
13 server_126 403 20 10.9291 13 0.5465 16
14 Shennong 466 20 10.1011 14 0.5051 17
15 RaptorX 166 20 9.3845 15 0.4692 20
16 IntFOLD7 151 19 9.0429 16 0.4759 19
17 BAKER 185 20 8.8620 17 0.4431 25
18 server_123 018 20 8.5973 18 0.4299 27
19 Asclepius 204 19 8.5891 19 0.4521 22
20 MultiFOLD 462 18 8.2488 20 0.4583 21
21 DFolding-refine 073 19 8.1694 21 0.4300 26
22 B11L 208 18 8.0841 22 0.4491 23
23 DMP 477 14 7.7417 23 0.5530 15
24 MUFold_H 360 20 7.5603 24 0.3780 29
25 hFold 353 16 7.1212 25 0.4451 24
26 OpenFold-SingleSeq 433 19 7.0733 26 0.3723 30
27 OpenFold 441 19 7.0733 26 0.3723 30
28 ShanghaiTech 225 20 7.0584 28 0.3529 35
29 ManiFold-serv 450 19 6.9819 29 0.3675 32
30 Graphen_Medical 097 14 6.8295 30 0.4878 18
31 AP_1 269 20 6.8095 31 0.3405 41
32 Elofsson 320 19 6.6876 32 0.3520 36
33 Agemo_mix 092 19 6.6477 33 0.3499 38
34 Panlab 234 20 6.5871 34 0.3294 44
35 McGuffin 180 19 6.5509 35 0.3448 39
36 TRFold 187 17 6.4502 36 0.3794 28
37 MULTICOM_refine 475 19 6.4210 37 0.3379 42
38 server_122 261 20 6.1989 38 0.3099 45
39 BeijingAIProtein 399 17 6.1858 39 0.3639 33
40 UltraFold 054 17 6.1858 39 0.3639 34
41 UltraFold_Server 125 18 6.1858 39 0.3437 40
42 server_125 264 20 6.0672 42 0.3034 47
43 Agemo 478 17 5.9827 43 0.3519 37
44 FTBiot0119 165 20 5.9501 44 0.2975 49
45 ChaePred 398 20 5.7616 45 0.2881 51
46 WL_team 257 19 5.7417 46 0.3022 48
47 Kiharalab_Server 131 20 5.7358 47 0.2868 52
48 trComplex 423 17 5.6135 48 0.3302 43
49 hFold_human 342 18 5.5688 49 0.3094 46
50 Kiharalab 119 20 5.4940 50 0.2747 54
51 MULTICOM_deep 158 19 5.4897 51 0.2889 50
52 Seder2022easy 455 19 5.3198 52 0.2800 53
53 GuijunLab-DeepDA 188 19 4.8945 53 0.2576 55
54 XRC_VU 215 7 4.8243 54 0.6892 8
55 ColabFold 446 20 4.7985 55 0.2399 56
56 colabfold_human 461 20 4.7985 55 0.2399 56
57 GuijunLab-Assembly 098 19 4.3734 57 0.2302 60
58 Wallner 037 18 4.1617 58 0.2312 59
59 FoldEver 245 19 3.9173 59 0.2062 62
60 MULTICOM 367 18 3.9011 60 0.2167 61
61 MULTICOM_human 003 18 3.6862 61 0.2048 63
62 GuijunLab-Meta 481 19 3.6577 62 0.1925 64
63 MULTICOM_qa 086 20 3.5934 63 0.1797 66
64 GuijunLab-Human 169 19 3.4694 64 0.1826 65
65 FoldEver-Hybrid 385 14 3.3126 65 0.2366 58
66 MULTICOM_egnn 120 20 3.1465 66 0.1573 70
67 GinobiFold 227 17 2.7459 67 0.1615 68
68 Coqualia 434 17 2.7459 67 0.1615 68
69 Cerebra 315 17 2.5507 69 0.1500 71
70 MUFold 298 20 2.4377 70 0.1219 77
71 GuijunLab-RocketX 089 19 2.4240 71 0.1276 75
72 GuijunLab-Threader 282 18 2.4147 72 0.1342 73
73 Bhattacharya 275 19 2.3720 73 0.1248 76
74 SHT 147 20 2.2983 74 0.1149 78
75 BhageerathH-Pro 212 14 2.2781 75 0.1627 67
76 GinobiFold-SER 011 16 2.2577 76 0.1411 72
77 FALCON2 368 20 2.2265 77 0.1113 79
78 FALCON0 333 20 2.2265 77 0.1113 79
79 hks1988 354 20 2.1535 79 0.1077 81
80 NBIS-AF2-standard 270 20 2.1141 80 0.1057 84
81 Pan_Server 219 19 2.0335 81 0.1070 82
82 Gonglab-THU 052 17 1.8164 82 0.1068 83
83 DELCLAB 447 18 1.1885 83 0.0660 89
84 ESM-single-sequence 067 9 1.1811 84 0.1312 74
85 UNRES 091 14 1.1657 85 0.0833 86
86 QUIC 117 20 1.1187 86 0.0559 91
87 PICNIC 276 20 1.1002 87 0.0550 92
88 ShanghaiTech-TS-SER 133 16 0.8613 88 0.0538 93
89 Seder2022hard 216 10 0.5910 89 0.0591 90
90 SHORTLE 064 1 0.5900 90 0.5900 14
91 wuqi 370 9 0.4176 91 0.0464 94
92 MESHI_server 427 2 0.1894 92 0.0947 85
93 EMBER3D 140 10 0.1591 93 0.0159 95
94 Manifold-LC-E 046 1 0.0809 94 0.0809 87
95 Manifold-X 304 1 0.0809 94 0.0809 87
96 RostlabUeFOFold 123 4 0.0346 96 0.0087 96
97 MESHI 362 1 0.0000 97 0.0000 97
98 ACOMPMOD 280 1 0.0000 97 0.0000 97
The cummulative z-scores in this table are calculated according to the following procedure (example for the "first" models):
1. Calculate zscores 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 zscores on the reduced dataset;
4. Assign z-scores below the penalty threshold (either -2.0 or 0.0) to the value of this threshold.
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