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 24 37.8507 1 1.5771 1
2 Yang-Server 229 24 27.2108 2 1.1338 3
3 Yang 439 20 19.7115 3 0.9856 4
4 PEZYFoldings 278 15 18.0578 4 1.2039 2
5 server_124 383 24 16.2284 5 0.6762 8
6 Manifold 248 19 14.9308 6 0.7858 5
7 Manifold-E 035 23 14.5759 7 0.6337 10
8 Venclovas 494 19 14.5386 8 0.7652 6
9 DFolding 074 20 13.1098 9 0.6555 9
10 server_126 403 24 12.7975 10 0.5332 16
11 bench 008 20 12.0811 11 0.6041 12
12 BAKER-SERVER 443 24 12.0303 12 0.5013 17
13 DFolding-server 288 19 11.5870 13 0.6098 11
14 IntFOLD7 151 23 11.4107 14 0.4961 18
15 Shennong 466 24 10.7753 15 0.4490 23
16 server_123 018 24 10.6511 16 0.4438 24
17 MultiFOLD 462 22 10.6166 17 0.4826 20
18 RaptorX 166 24 9.9124 18 0.4130 27
19 BAKER 185 20 8.8620 19 0.4431 25
20 Asclepius 204 19 8.5891 20 0.4521 21
21 Kiharalab_Server 131 24 8.4352 21 0.3515 39
22 UltraFold_Server 125 22 8.2293 22 0.3741 31
23 DFolding-refine 073 19 8.1694 23 0.4300 26
24 B11L 208 18 8.0841 24 0.4491 22
25 ManiFold-serv 450 23 7.8702 25 0.3422 42
26 hFold 353 20 7.8019 26 0.3901 28
27 server_122 261 24 7.7880 27 0.3245 46
28 DMP 477 14 7.7417 28 0.5530 15
29 server_125 264 24 7.6563 29 0.3190 47
30 MUFold_H 360 20 7.5603 30 0.3780 30
31 OpenFold-SingleSeq 433 19 7.0733 31 0.3723 32
32 OpenFold 441 19 7.0733 31 0.3723 32
33 ShanghaiTech 225 20 7.0584 33 0.3529 36
34 MULTICOM_refine 475 23 6.9950 34 0.3041 49
35 Graphen_Medical 097 14 6.8295 35 0.4878 19
36 AP_1 269 20 6.8095 36 0.3405 43
37 Elofsson 320 19 6.6876 37 0.3520 37
38 Agemo_mix 092 19 6.6477 38 0.3499 40
39 Panlab 234 20 6.5871 39 0.3294 45
40 McGuffin 180 19 6.5509 40 0.3448 41
41 TRFold 187 17 6.4502 41 0.3794 29
42 BeijingAIProtein 399 17 6.1858 42 0.3639 34
43 UltraFold 054 17 6.1858 42 0.3639 34
44 MULTICOM_deep 158 23 6.1738 44 0.2684 55
45 Agemo 478 17 5.9827 45 0.3519 38
46 FTBiot0119 165 20 5.9501 46 0.2975 51
47 ChaePred 398 20 5.7616 47 0.2881 52
48 WL_team 257 19 5.7417 48 0.3022 50
49 GuijunLab-Assembly 098 23 5.6965 49 0.2477 56
50 trComplex 423 17 5.6135 50 0.3302 44
51 GuijunLab-DeepDA 188 23 5.6052 51 0.2437 57
52 hFold_human 342 18 5.5688 52 0.3094 48
53 Kiharalab 119 20 5.4940 53 0.2747 54
54 Seder2022easy 455 19 5.3198 54 0.2800 53
55 ColabFold 446 22 5.1993 55 0.2363 60
56 XRC_VU 215 7 4.8243 56 0.6892 7
57 colabfold_human 461 20 4.7985 57 0.2399 58
58 FoldEver 245 23 4.6957 58 0.2042 64
59 GuijunLab-Meta 481 23 4.6656 59 0.2029 65
60 Wallner 037 18 4.1617 60 0.2312 61
61 MULTICOM_qa 086 24 4.1110 61 0.1713 68
62 MULTICOM 367 18 3.9011 62 0.2167 62
63 MULTICOM_egnn 120 24 3.8306 63 0.1596 71
64 MULTICOM_human 003 18 3.6862 64 0.2048 63
65 GinobiFold-SER 011 20 3.5591 65 0.1780 67
66 GuijunLab-Human 169 19 3.4694 66 0.1826 66
67 NBIS-AF2-standard 270 24 3.3399 67 0.1392 74
68 FoldEver-Hybrid 385 14 3.3126 68 0.2366 59
69 MUFold 298 24 3.3105 69 0.1379 75
70 GuijunLab-Threader 282 22 3.1286 70 0.1422 73
71 GuijunLab-RocketX 089 23 3.0600 71 0.1330 76
72 Manifold-LC-E 046 5 2.9837 72 0.5967 13
73 GinobiFold 227 17 2.7459 73 0.1615 69
74 Coqualia 434 17 2.7459 73 0.1615 69
75 BhageerathH-Pro 212 18 2.7166 75 0.1509 72
76 Cerebra 315 21 2.5507 76 0.1215 79
77 Bhattacharya 275 19 2.3720 77 0.1248 78
78 ShanghaiTech-TS-SER 133 20 2.3314 78 0.1166 80
79 SHT 147 20 2.2983 79 0.1149 81
80 FALCON2 368 24 2.2265 80 0.0928 86
81 FALCON0 333 24 2.2265 80 0.0928 86
82 Pan_Server 219 23 2.1610 82 0.0940 85
83 hks1988 354 20 2.1535 83 0.1077 82
84 Gonglab-THU 052 17 1.8164 84 0.1068 83
85 DELCLAB 447 18 1.1885 85 0.0660 90
86 ESM-single-sequence 067 9 1.1811 86 0.1312 77
87 UNRES 091 14 1.1657 87 0.0833 88
88 QUIC 117 20 1.1187 88 0.0559 93
89 PICNIC 276 20 1.1002 89 0.0550 94
90 Seder2022hard 216 10 0.5910 90 0.0591 92
91 SHORTLE 064 1 0.5900 91 0.5900 14
92 wuqi 370 9 0.4176 92 0.0464 95
93 ACOMPMOD 280 5 0.2975 93 0.0595 91
94 MESHI_server 427 2 0.1894 94 0.0947 84
95 EMBER3D 140 10 0.1591 95 0.0159 96
96 Manifold-X 304 1 0.0809 96 0.0809 89
97 RostlabUeFOFold 123 4 0.0346 97 0.0087 97
98 MESHI 362 1 0.0000 98 0.0000 98
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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