16th 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   Assessors' formula

    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:

    • CASP16 formula for ALL domains:
           1/6*(GDT_HA + reLLG_const + ASE) + 1/16*(LDDT + GDC_SC + AL0_P + MolProbity) + 1/8*(SG + CADaa)
    #     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)
1 052 Yang-Server 74 28.3378 1 0.3829 5 37.5502 1 0.5074 4
2 022 Yang 74 27.1508 2 0.3669 6 36.1864 2 0.4890 8
3 051 MULTICOM 74 22.1636 8 0.2995 17 35.6897 3 0.4823 9
4 208 falcon2 73 23.9507 5 0.3555 8 34.8799 4 0.4778 10
5 456 Yang-Multimer 74 22.9414 7 0.3100 15 34.2037 5 0.4622 12
6 304 AF3-server 73 24.1926 4 0.3588 7 34.0091 6 0.4659 11
7 319 MULTICOM_LLM 74 18.8219 12 0.2543 28 32.7903 7 0.4431 14
8 241 elofsson 74 25.0292 3 0.3382 11 32.6127 8 0.4407 16
9 110 MIEnsembles-Server 74 23.2528 6 0.3142 14 32.4228 9 0.4381 17
10 294 KiharaLab 74 9.6124 24 0.1299 45 31.9147 10 0.4313 20
11 287 plmfold 74 20.8965 9 0.2824 20 31.7789 11 0.4294 21
12 331 MULTICOM_AI 74 16.6108 18 0.2245 35 31.5161 12 0.4259 22
13 301 GHZ-MAN 73 18.2264 15 0.2771 22 30.9174 13 0.4235 23
14 075 GHZ-ISM 70 12.6929 22 0.2956 18 30.9089 14 0.4416 15
15 284 Unicorn 70 13.4034 21 0.3058 16 30.5502 15 0.4364 19
16 019 Zheng-Server 74 20.1157 10 0.2718 24 30.5254 16 0.4125 29
17 028 NKRNA-s 60 -3.5975 36 0.4067 4 30.3035 17 0.5051 6
18 425 MULTICOM_GATE 74 16.3561 19 0.2210 36 30.2614 18 0.4089 30
19 345 MULTICOM_human 74 14.0665 20 0.1901 40 30.1441 19 0.4074 32
20 465 Wallner 74 2.8191 31 0.0381 56 29.7265 20 0.4017 34
21 264 GuijunLab-Human 73 19.1357 11 0.2895 19 29.4886 21 0.4040 33
22 147 Zheng-Multimer 74 18.3589 13 0.2481 30 29.2369 22 0.3951 37
23 462 Zheng 74 17.7111 16 0.2393 33 29.2175 23 0.3948 38
24 312 GuijunLab-Assembly 73 16.8527 17 0.2583 26 28.2435 24 0.3869 39
25 148 Guijunlab-Complex 74 18.2706 14 0.2469 31 28.2076 25 0.3812 41
26 475 ptq 67 3.2432 30 0.2574 27 27.9487 26 0.4171 27
27 015 PEZYFoldings 74 -1.9203 33 -0.0260 64 27.0090 27 0.3650 46
28 122 MQA_server 64 -4.6316 37 0.2401 32 26.8910 28 0.4202 26
29 164 McGuffin 74 10.7396 23 0.1451 43 26.6399 29 0.3600 47
30 267 kiharalab_server 74 7.1639 26 0.0968 48 25.8207 30 0.3489 51
31 314 GuijunLab-PAthreader 71 4.6225 28 0.1496 41 25.3507 31 0.3571 48
32 163 MultiFOLD2 74 5.3639 27 0.0725 50 24.8883 32 0.3363 57
33 293 MRAH 74 8.5560 25 0.1156 46 24.7723 33 0.3348 58
34 369 Bhattacharya 66 -6.3451 39 0.1463 42 24.6630 34 0.3737 44
35 272 GromihaLab 70 -24.1768 46 -0.2311 79 23.9483 35 0.3421 55
36 419 CSSB-Human 74 4.0761 29 0.0551 52 23.8270 36 0.3220 63
37 079 MRAFold 74 2.5160 32 0.0340 57 23.7574 37 0.3210 66
38 375 milliseconds 60 -11.7731 41 0.2704 25 22.9258 38 0.3821 40
39 031 MassiveFold 66 -14.3317 42 0.0253 58 22.8313 39 0.3459 53
40 196 HYU_MLLAB 74 -2.7314 34 -0.0369 65 21.6879 40 0.2931 71
41 298 ShanghaiTech-human 62 -21.5557 44 0.0394 55 21.6252 41 0.3488 52
42 269 CSSB_server 60 -24.6393 47 0.0560 51 21.1504 42 0.3525 50
43 145 colabfold_baseline 59 -27.6680 48 0.0395 54 21.0367 43 0.3566 49
44 235 isyslab-hust 72 -4.7785 38 -0.0108 62 20.7775 44 0.2886 72
45 221 CSSB_FAKER 74 -3.1546 35 -0.0426 66 20.7742 45 0.2807 77
46 286 CSSB_experimental 72 -7.1792 40 -0.0442 67 20.6118 46 0.2863 74
47 262 CoDock 57 -38.9471 53 -0.0868 74 18.8361 47 0.3305 60
48 388 DeepFold-server 74 -22.7653 45 -0.3076 82 18.2605 48 0.2468 85
49 198 colabfold 59 -33.7540 50 -0.0636 70 17.8325 49 0.3022 69
50 059 DeepFold 74 -28.5099 49 -0.3853 86 16.2620 50 0.2198 87
51 091 Huang-HUST 56 -48.3435 54 -0.2204 78 15.8594 51 0.2832 76
52 014 Cool-PSP 74 -19.5948 43 -0.2648 80 15.1089 52 0.2042 88
53 311 RAGfold_Prot1 57 -35.3606 51 -0.0239 63 15.0853 53 0.2647 80
54 112 Seder2024easy 57 -54.9630 55 -0.3678 85 15.0821 54 0.2646 81
55 423 ShanghaiTech-server 59 -35.8289 52 -0.0988 75 14.8453 55 0.2516 84
56 322 XGroup 37 -68.8989 57 0.1379 44 13.5336 56 0.3658 45
57 323 Yan 31 -77.3999 62 0.2774 21 13.1001 57 0.4226 24
58 023 FTBiot0119 39 -72.2390 59 -0.0574 68 12.9354 58 0.3317 59
59 017 Seder2024hard 56 -59.8363 56 -0.4256 88 12.4411 59 0.2222 86
60 489 Fernandez-Recio 36 -74.1127 60 0.0524 53 11.8591 60 0.3294 61
61 204 Zou 36 -81.4161 65 -0.1504 76 11.4782 61 0.3188 67
62 171 ChaePred 23 -94.1100 74 0.3430 9 11.2618 62 0.4896 7
63 290 Pierce 27 -87.1699 71 0.2530 29 11.2359 63 0.4161 28
64 393 GuijunLab-QA 32 -77.3324 61 0.2084 38 10.8040 64 0.3376 56
65 450 OpenComplex_Server 73 -82.5872 67 -1.1039 99 10.3363 65 0.1416 93
66 219 XGroup-server 31 -83.6648 68 0.0753 49 10.2051 66 0.3292 62
67 139 DeepFold-refine 74 -69.7802 58 -0.9430 95 10.1718 67 0.1375 94
68 274 kozakovvajda 36 -78.5430 63 -0.0706 71 9.8207 68 0.2728 79
69 494 ClusPro 36 -78.9562 64 -0.0821 73 9.4358 69 0.2621 83
70 167 OpenComplex 74 -86.1897 70 -1.1647 101 9.3861 70 0.1268 95
71 358 PerezLab_Gators 34 -86.1895 69 -0.1820 77 9.3628 71 0.2754 78
72 218 HIT-LinYang 21 -101.0492 77 0.2358 34 8.8291 72 0.4204 25
73 397 smg_ulaval 15 -110.6301 79 0.4913 2 8.1220 73 0.5415 2
74 191 Schneidman 24 -97.6101 75 0.0996 47 7.7117 74 0.3213 65
75 481 Vfold 20 -107.5672 78 0.0216 59 7.6218 75 0.3811 42
76 085 Bates 25 -98.0834 76 -0.0033 61 7.4571 76 0.2983 70
77 261 UNRES 53 -90.7900 72 -0.9206 94 5.2776 77 0.0996 97
78 040 DELCLAB 67 -92.5985 73 -1.1731 102 4.8277 78 0.0721 102
79 212 PIEFold_human 74 -82.1307 66 -1.1099 100 4.5216 79 0.0611 103
80 033 Diff 10 -131.3576 89 -0.3358 84 4.3700 80 0.4370 18
81 380 mialab_prediction 15 -118.9058 80 -0.0604 69 3.9616 81 0.2641 82
82 325 405 9 -128.1125 83 0.2097 37 3.6779 82 0.4087 31
83 159 406 9 -128.2675 84 0.1925 39 3.5974 83 0.3997 36
84 189 LCBio 11 -130.5387 87 -0.4126 87 3.5400 84 0.3218 64
85 187 Ayush 24 -119.5786 81 -0.8158 92 3.4773 85 0.1449 92
86 338 GeneSilico 12 -124.8768 82 -0.0731 72 3.4470 86 0.2873 73
87 376 OFsingleseq 11 -133.6544 93 -0.6959 91 3.1449 87 0.2859 75
88 231 B-LAB 10 -132.3617 90 -0.4362 89 3.1438 88 0.3144 68
89 117 Vakser 25 -134.0005 94 -1.4400 105 1.8145 89 0.0726 101
90 337 APOLLO 15 -132.5610 91 -0.9707 96 1.7168 90 0.1145 96
91 276 FrederickFolding 3 -141.1825 97 0.2725 23 1.6728 91 0.5576 1
92 361 Cerebra_server 71 -129.6595 86 -1.7417 108 1.6169 92 0.0228 105
93 468 MIALAB_gong 10 -130.9361 88 -0.2936 81 1.5200 93 0.1520 91
94 120 Cerebra 67 -129.2185 85 -1.7197 107 1.4647 94 0.0219 106
95 008 HADDOCK 9 -132.9743 92 -0.3305 83 1.4117 95 0.1569 90
96 114 COAST 15 -134.5021 96 -1.1001 98 1.1938 96 0.0796 99
97 300 ARC 15 -134.0676 95 -1.0712 97 1.1763 97 0.0784 100
98 400 OmniFold 2 -143.3696 98 0.3152 13 1.0244 98 0.5122 3
99 174 colabfold_foldseek 1 -145.4930 101 0.5070 1 0.5070 99 0.5070 5
100 049 UTMB 1 -145.5598 102 0.4402 3 0.4566 100 0.4566 13
101 197 D3D 1 -145.9805 105 0.0195 60 0.4004 101 0.4004 35
102 355 CMOD 1 -145.6727 104 0.3273 12 0.3781 102 0.3781 43
103 271 mialab_prediction2 1 -145.6586 103 0.3414 10 0.3449 103 0.3449 54
104 143 dMNAfold 1 -146.4818 107 -0.4818 90 0.1987 104 0.1987 89
105 357 UTAustin 2 -146.7055 108 -1.3527 104 0.1713 105 0.0856 98
106 132 profold2 6 -145.1441 100 -1.5240 106 0.1278 106 0.0213 107
107 351 digiwiser-ensemble 4 -145.1230 99 -1.2808 103 0.0728 107 0.0182 108
108 281 T2DUCC 1 -146.8308 109 -0.8308 93 0.0339 108 0.0339 104
109 138 Shengyi 2 -147.7383 110 -1.8692 109 0.0000 109 0.0000 109
110 105 PFSC-PFVM 41 -146.1917 106 -1.9559 110 0.0000 109 0.0000 109
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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