15th Community Wide Experiment on the
Critical Assessment of Techniques for Protein Structure Prediction
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TS Analysis : Z-score based relative group performance
Results Home Table Browser
  GDT_TS   Assessor's formulae

    Models:

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

    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.
    • TBM-easy
    • TBM-hard
    • TBM/FM
    • 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)
1 229 Yang-Server 108 81.5826 2 0.7739 2 90.4273 1 0.8373 2
2 162 UM-TBM 109 84.2212 1 0.7727 3 89.2119 2 0.8185 3
3 278 PEZYFoldings 107 71.6735 4 0.7072 5 85.7980 3 0.8019 4
4 439 Yang 108 74.9323 3 0.7123 4 83.5439 4 0.7736 5
5 074 DFolding 109 64.7737 5 0.5943 6 73.3653 5 0.6731 6
6 180 McGuffin 109 63.6277 6 0.5837 7 71.4404 6 0.6554 7
7 367 MULTICOM 109 56.5633 7 0.5189 11 62.0521 7 0.5693 11
8 003 MULTICOM_human 109 55.2044 8 0.5065 12 61.2526 8 0.5620 12
9 119 Kiharalab 109 43.4423 12 0.3986 19 59.8846 9 0.5494 13
10 035 Manifold-E 109 41.7627 14 0.3831 22 59.8034 10 0.5487 14
11 248 Manifold 109 39.0830 16 0.3586 27 58.8145 11 0.5396 15
12 475 MULTICOM_refine 109 48.9045 9 0.4487 14 55.4969 12 0.5091 18
13 185 BAKER 109 40.6822 15 0.3732 24 54.6916 13 0.5018 19
14 120 MULTICOM_egnn 109 46.4159 10 0.4258 16 51.6856 14 0.4742 22
15 158 MULTICOM_deep 109 44.9761 11 0.4126 18 50.8957 15 0.4669 23
16 086 MULTICOM_qa 109 43.4334 13 0.3985 20 49.7756 16 0.4567 26
17 288 DFolding-server 109 32.7186 19 0.3002 34 49.6723 17 0.4557 27
18 225 ShanghaiTech 109 27.4582 23 0.2519 39 47.4094 18 0.4349 31
19 037 Wallner 109 32.9775 18 0.3025 33 47.3299 19 0.4342 32
20 008 bench 108 36.1081 17 0.3529 28 47.1906 20 0.4370 30
21 494 Venclovas 74 -30.8850 71 0.5286 9 46.9084 21 0.6339 8
22 461 colabfold_human 109 26.9492 25 0.2472 41 46.8694 22 0.4300 33
23 320 Elofsson 92 4.7664 50 0.4214 17 46.0068 23 0.5001 20
24 446 ColabFold 109 24.8584 27 0.2281 45 45.4936 24 0.4174 37
25 462 MultiFOLD 109 28.3184 22 0.2598 38 45.4193 25 0.4167 38
26 166 RaptorX 109 32.1278 20 0.2948 35 45.2730 26 0.4153 39
27 360 MUFold_H 109 32.0652 21 0.2942 36 44.7614 27 0.4107 40
28 092 Agemo_mix 109 19.2837 31 0.1769 55 42.7895 28 0.3926 46
29 204 Asclepius 108 16.6693 35 0.1729 56 42.7708 29 0.3960 44
30 125 UltraFold_Server 109 24.9870 26 0.2292 44 40.2374 30 0.3692 51
31 054 UltraFold 107 18.9045 32 0.2141 49 40.1783 31 0.3755 48
32 298 MUFold 109 27.0788 24 0.2484 40 39.3851 32 0.3613 52
33 399 BeijingAIProtein 103 9.5923 43 0.2096 50 38.6737 33 0.3755 49
34 477 DMP 96 -3.2927 57 0.2365 42 37.7502 34 0.3932 45
35 208 B11L 102 9.2353 44 0.2278 46 37.7327 35 0.3699 50
36 098 GuijunLab-Assembly 109 20.9119 29 0.1919 53 36.5020 36 0.3349 58
37 188 GuijunLab-DeepDA 109 24.3599 28 0.2235 47 35.7210 37 0.3277 59
38 403 server_126 109 17.8889 33 0.1641 58 35.6521 38 0.3271 60
39 466 Shennong 105 12.9163 40 0.1992 52 35.6204 39 0.3392 57
40 383 server_124 109 14.9450 38 0.1371 64 35.5106 40 0.3258 62
41 131 Kiharalab_Server 109 5.1380 49 0.0471 75 35.4815 41 0.3255 63
42 169 GuijunLab-Human 107 17.5596 34 0.2015 51 34.6685 42 0.3240 64
43 353 hFold 106 12.0391 41 0.1702 57 34.0257 43 0.3210 65
44 270 NBIS-AF2-standard 109 19.8415 30 0.1820 54 33.5259 44 0.3076 67
45 245 FoldEver 109 14.9175 39 0.1369 65 33.4768 45 0.3071 69
46 398 ChaePred 107 6.1070 47 0.0945 68 32.5078 46 0.3038 70
47 342 hFold_human 109 16.2522 37 0.1491 61 32.0422 47 0.2940 73
48 261 server_122 109 8.1776 45 0.0750 70 31.8057 48 0.2918 76
49 441 OpenFold 108 -1.0622 53 0.0087 81 31.6056 49 0.2926 74
50 433 OpenFold-SingleSeq 108 -1.0749 54 0.0086 82 31.5949 50 0.2925 75
51 151 IntFOLD7 109 1.6283 52 0.0149 79 31.4564 51 0.2886 79
52 423 trComplex 108 -3.6089 58 -0.0149 83 31.4031 52 0.2908 77
53 018 server_123 109 5.6091 48 0.0515 74 31.3231 53 0.2874 80
54 264 server_125 109 7.5296 46 0.0691 72 31.1459 54 0.2857 82
55 385 FoldEver-Hybrid 101 -1.8439 56 0.1402 63 31.0252 55 0.3072 68
56 187 TRFold 108 -4.5202 60 -0.0233 85 30.6111 56 0.2834 83
57 165 FTBiot0119 109 -12.0418 63 -0.1105 96 29.4161 57 0.2699 86
58 481 GuijunLab-Meta 107 11.4414 42 0.1443 62 28.9333 58 0.2704 84
59 282 GuijunLab-Threader 109 16.2762 36 0.1493 60 28.3258 59 0.2599 88
60 434 Coqualia 104 -8.9084 62 0.0105 80 28.0596 60 0.2698 87
61 374 Zheng 45 -104.5021 85 0.5222 10 26.9568 61 0.5990 9
62 089 GuijunLab-RocketX 108 3.7742 51 0.0535 73 26.8706 62 0.2488 89
63 239 Yang-Multimer 45 -103.4047 84 0.5466 8 26.7316 63 0.5940 10
64 455 Seder2022easy 106 -20.0327 69 -0.1324 98 26.2406 64 0.2476 90
65 073 DFolding-refine 106 -42.4461 73 -0.3438 107 25.6803 65 0.2423 92
66 227 GinobiFold 106 -16.5136 68 -0.0992 95 25.6652 66 0.2421 93
67 097 Graphen_Medical 73 -60.2285 77 0.1613 59 25.4882 67 0.3492 56
68 133 ShanghaiTech-TS-SER 105 -13.8865 65 -0.0561 91 25.0175 68 0.2383 94
69 215 XRC_VU 80 -48.2996 75 0.1213 67 24.2973 69 0.3037 71
70 117 QUIC 103 -4.5390 61 0.0724 71 24.1650 70 0.2346 95
71 269 AP_1 109 -16.3755 67 -0.1502 102 23.9602 71 0.2198 97
72 011 GinobiFold-SER 105 -12.3106 64 -0.0411 89 23.6828 72 0.2256 96
73 354 hks1988 109 -4.0883 59 -0.0375 87 23.5785 73 0.2163 99
74 450 ManiFold-serv 109 -1.8258 55 -0.0168 84 23.5185 74 0.2158 100
75 390 NBIS-AF2-multimer 50 -99.6903 82 0.3662 25 23.1522 75 0.4630 24
76 071 RaptorX-Multimer 45 -107.4831 86 0.4559 13 23.0416 76 0.5120 17
77 147 SHT 96 -61.9503 78 -0.3745 109 19.8583 77 0.2069 101
78 348 Takeda-Shitaka_Lab 45 -111.5976 87 0.3645 26 19.3303 78 0.4296 34
79 275 Bhattacharya 109 -15.6184 66 -0.1433 101 18.9781 79 0.1741 106
80 216 Seder2022hard 94 -36.1140 72 -0.0650 93 18.9283 80 0.2014 102
81 443 BAKER-SERVER 109 -24.4907 70 -0.2247 104 18.7219 81 0.1718 107
82 150 Grudinin 44 -118.1109 89 0.2702 37 17.7327 82 0.4030 42
83 067 ESM-single-sequence 93 -77.1062 80 -0.4850 111 17.2761 83 0.1858 103
84 350 ClusPro 42 -124.2096 92 0.2331 43 16.1462 84 0.3844 47
85 478 Agemo 109 -117.3215 88 -1.0763 118 15.3181 85 0.1405 111
86 234 Panlab 109 -54.7534 76 -0.5023 112 14.8179 86 0.1359 113
87 314 Pierce 27 -153.4536 99 0.3906 21 14.3622 87 0.5319 16
88 276 PICNIC 102 -66.3790 79 -0.5135 113 14.1868 88 0.1391 112
89 444 CoDock 47 -120.2064 90 0.0807 69 14.0886 89 0.2998 72
90 257 WL_team 93 -95.5792 81 -0.6836 115 13.2297 90 0.1423 110
91 091 UNRES 103 -47.1022 74 -0.3408 106 11.8975 91 0.1155 116
92 291 Kozakov-Vajda 28 -155.9715 100 0.2153 48 11.4342 92 0.4084 41
93 123 RostlabUeFOFold 82 -174.1983 104 -1.4658 123 10.6813 93 0.1303 115
94 205 Zou 41 -141.6418 96 -0.1376 100 9.9637 94 0.2430 91
95 493 Shen-CAPRI 34 -152.3645 98 -0.0695 94 9.1778 95 0.2699 85
96 447 DELCLAB 97 -126.0688 93 -1.0523 117 7.7223 96 0.0796 120
97 427 MESHI_server 76 -124.1846 91 -0.7656 116 7.1159 97 0.0936 117
98 064 SHORTLE 51 -138.3643 94 -0.4385 110 6.6710 98 0.1308 114
99 312 Fernandez-Recio 34 -150.9856 97 -0.0290 86 5.6696 99 0.1668 108
100 362 MESHI 70 -102.1163 83 -0.3445 108 5.6360 100 0.0805 119
101 199 TB_model_prediction 13 -187.4785 107 0.3478 29 5.1933 101 0.3995 43
102 219 Pan_Server 104 -138.6449 95 -1.2370 120 4.9093 102 0.0472 122
103 140 EMBER3D 92 -187.7409 108 -1.6711 127 4.6327 103 0.0504 121
104 132 TensorLab 11 -192.2700 113 0.3391 30 4.5969 104 0.4179 36
105 456 AIchemy_LIG2 13 -191.6843 109 0.0243 76 4.5704 105 0.3516 53
106 347 AIchemy_LIG3 13 -191.6843 109 0.0243 76 4.5704 105 0.3516 53
107 325 AIchemy_LIG 13 -191.6843 109 0.0243 76 4.5704 105 0.3516 53
108 397 bio3d 2 -209.8542 124 2.0729 1 4.1458 108 2.0729 1
109 304 Manifold-X 18 -184.1292 106 -0.1183 97 3.9193 109 0.2177 98
110 122 zax 8 -199.2902 115 0.3387 31 3.8237 110 0.4780 21
111 052 Gonglab-THU 109 -199.7542 116 -1.8326 128 2.9646 111 0.0272 124
112 498 Spider 33 -192.8253 114 -1.2371 121 2.9178 112 0.0884 118
113 370 wuqi 87 -157.5609 101 -1.3053 122 2.9169 113 0.0335 123
114 315 Cerebra 109 -200.1598 117 -1.8363 129 2.8639 114 0.0263 125
115 338 Convex-PL 6 -205.2559 120 0.1240 66 2.7441 115 0.4574 25
116 201 UTMB 6 -204.1357 119 0.3107 32 2.6430 116 0.4405 28
117 352 KORP-PL 8 -203.3719 118 -0.1715 103 2.6114 117 0.3264 61
118 333 FALCON0 107 -170.4753 102 -1.5558 124 2.4421 118 0.0228 126
119 368 FALCON2 107 -170.4753 102 -1.5558 124 2.4421 118 0.0228 126
120 472 ddquest 5 -206.0897 122 0.3821 23 2.1940 120 0.4388 29
121 046 Manifold-LC-E 15 -192.1909 112 -0.2794 105 2.1872 121 0.1458 109
122 236 noxelis 5 -205.8676 121 0.4265 15 2.1324 122 0.4265 35
123 460 Convex-PL-R 6 -206.2286 123 -0.0381 88 1.8465 123 0.3078 66
124 212 BhageerathH-Pro 103 -178.6356 105 -1.6178 126 1.3764 124 0.0134 128
125 014 FEIGLAB 3 -212.1694 127 -0.0565 92 0.8682 125 0.2894 78
126 412 MeilerLab 3 -212.1340 126 -0.0447 90 0.8601 126 0.2867 81
127 366 GatorsML 3 -215.4603 130 -1.1534 119 0.5397 127 0.1799 105
128 088 coco 2 -214.2662 129 -0.1331 99 0.3648 128 0.1824 104
129 280 ACOMPMOD 78 -210.3682 125 -1.9022 130 0.1762 129 0.0023 129
130 285 PerezLab_Gators 3 -213.5678 128 -0.5226 114 0.0000 130 0.0000 130
131 006 Sun_Tsinghua 22 -217.3346 131 -1.9698 131 0.0000 130 0.0000 130
132 488 CSRC_ICM 1 -218.0000 132 -2.0000 132 0.0000 130 0.0000 130
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.
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