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DMS_name
stringclasses
68 values
PDB_file
stringclasses
68 values
chain
stringclasses
8 values
site
stringclasses
695 values
wildtype
stringclasses
20 values
mutation
stringclasses
20 values
mut_name
stringlengths
4
7
DMS_score
float64
-7.77
70
MinMax_normalized_DMS_score
float64
0
1
Rank_quartile_normalized_DMS_score
float64
-2.28
3.78
closest_interface_atom_distance
float64
1.4
103
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
A
HA8A
0.53991
0.353
0.176
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
A
HB8A
0.53991
0.353
0.176
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
C
HA8C
-0.841397
0.242
-0.549
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
C
HB8C
-0.841397
0.242
-0.549
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
D
HA8D
-1.796409
0.165
-1.065
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
D
HB8D
-1.796409
0.165
-1.065
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
E
HA8E
-0.226328
0.291
-0.165
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
E
HB8E
-0.226328
0.291
-0.165
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
F
HA8F
0.463458
0.347
0.078
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
F
HB8F
0.463458
0.347
0.078
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
G
HA8G
-0.542226
0.266
-0.322
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
G
HB8G
-0.542226
0.266
-0.322
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
L
HA8L
-0.924273
0.235
-0.58
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
L
HB8L
-0.924273
0.235
-0.58
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
M
HA8M
-0.581397
0.263
-0.353
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
M
HB8M
-0.581397
0.263
-0.353
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
N
HA8N
-0.321529
0.284
-0.227
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
N
HB8N
-0.321529
0.284
-0.227
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
P
HA8P
-1.896438
0.157
-1.115
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
P
HB8P
-1.896438
0.157
-1.115
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
Q
HA8Q
-1.035516
0.226
-0.628
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
Q
HB8Q
-1.035516
0.226
-0.628
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
R
HA8R
-1.300125
0.205
-0.769
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
R
HB8R
-1.300125
0.205
-0.769
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
V
HA8V
-0.231065
0.291
-0.165
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
V
HB8V
-0.231065
0.291
-0.165
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
W
HA8W
-0.507396
0.269
-0.321
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
W
HB8W
-0.507396
0.269
-0.321
16
NGF_2018_tanezumab
tanezumab_4edw
A
8
H
Y
HA8Y
-1.057593
0.224
-0.643
2.4
NGF_2018_tanezumab
tanezumab_4edw
B
8
H
Y
HB8Y
-1.057593
0.224
-0.643
16
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
A
RA9A
-1.598161
0.181
-0.957
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
A
RB9A
-1.598161
0.181
-0.957
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
C
RA9C
3.562993
0.597
1.451
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
C
RB9C
3.562993
0.597
1.451
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
D
RA9D
-1.411472
0.196
-0.8
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
D
RB9D
-1.411472
0.196
-0.8
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
E
RA9E
-1.461012
0.192
-0.863
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
E
RB9E
-1.461012
0.192
-0.863
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
F
RA9F
-0.845091
0.241
-0.549
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
F
RB9F
-0.845091
0.241
-0.549
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
G
RA9G
-1.17768
0.215
-0.729
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
G
RB9G
-1.17768
0.215
-0.729
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
H
RA9H
-2.259008
0.127
-1.357
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
H
RB9H
-2.259008
0.127
-1.357
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
I
RA9I
0.768577
0.372
0.216
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
I
RB9I
0.768577
0.372
0.216
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
K
RA9K
-1.12842
0.219
-0.682
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
K
RB9K
-1.12842
0.219
-0.682
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
L
RA9L
-0.564386
0.264
-0.353
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
L
RB9L
-0.564386
0.264
-0.353
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
M
RA9M
-2.636679
0.097
-1.571
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
M
RB9M
-2.636679
0.097
-1.571
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
N
RA9N
-1.540779
0.185
-0.886
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
N
RB9N
-1.540779
0.185
-0.886
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
P
RA9P
-0.490014
0.27
-0.282
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
P
RB9P
-0.490014
0.27
-0.282
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
Q
RA9Q
-0.865568
0.24
-0.549
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
Q
RB9Q
-0.865568
0.24
-0.549
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
S
RA9S
-1.27639
0.207
-0.769
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
S
RB9S
-1.27639
0.207
-0.769
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
T
RA9T
-2.290004
0.125
-1.408
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
T
RB9T
-2.290004
0.125
-1.408
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
V
RA9V
-1.164816
0.216
-0.724
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
V
RB9V
-1.164816
0.216
-0.724
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
W
RA9W
-0.125063
0.3
-0.071
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
W
RB9W
-0.125063
0.3
-0.071
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
9
R
Y
RA9Y
0.287715
0.333
0.078
1.5
NGF_2018_tanezumab
tanezumab_4edw
B
9
R
Y
RB9Y
0.287715
0.333
0.078
16.8
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
A
GA10A
-1.698927
0.172
-1.027
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
A
GB10A
-1.698927
0.172
-1.027
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
C
GA10C
0.317661
0.335
0.078
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
C
GB10C
0.317661
0.335
0.078
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
D
GA10D
-1.124539
0.219
-0.675
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
D
GB10D
-1.124539
0.219
-0.675
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
E
GA10E
-0.820335
0.243
-0.502
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
E
GB10E
-0.820335
0.243
-0.502
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
F
GA10F
0.755638
0.371
0.216
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
F
GB10F
0.755638
0.371
0.216
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
H
GA10H
-0.618613
0.26
-0.353
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
H
GB10H
-0.618613
0.26
-0.353
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
I
GA10I
5.129283
0.724
2.373
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
I
GB10I
5.129283
0.724
2.373
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
K
GA10K
0.799694
0.374
0.216
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
K
GB10K
0.799694
0.374
0.216
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
L
GA10L
2.562993
0.517
0.902
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
L
GB10L
2.562993
0.517
0.902
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
M
GA10M
-3.829324
0
-1.831
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
M
GB10M
-3.829324
0
-1.831
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
N
GA10N
-0.021969
0.308
-0.005
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
N
GB10N
-0.021969
0.308
-0.005
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
P
GA10P
0.013876
0.311
0
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
P
GB10P
0.013876
0.311
0
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
Q
GA10Q
-0.954855
0.233
-0.58
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
Q
GB10Q
-0.954855
0.233
-0.58
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
R
GA10R
-1.286242
0.206
-0.769
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
R
GB10R
-1.286242
0.206
-0.769
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
S
GA10S
-0.485916
0.27
-0.282
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
S
GB10S
-0.485916
0.27
-0.282
16.3
NGF_2018_tanezumab
tanezumab_4edw
A
10
G
T
GA10T
-3.021969
0.066
-1.717
1.8
NGF_2018_tanezumab
tanezumab_4edw
B
10
G
T
GB10T
-3.021969
0.066
-1.717
16.3
End of preview. Expand in Data Studio

AbAgym

AbAgym is a curated dataset of deep mutational scanning (DMS) measurements for antibody-antigen complexes. This Hugging Face version reorganizes the original AbAgym files into loadable dataset configurations using Apache Parquet, while preserving the original structure archive.

The original AbAgym repository describes the dataset as containing 68 DMS datasets on antibody-antigen complexes, approximately 324,000 non-redundant mutations, 36,541 non-redundant interface mutations, and 3D structures for the antibody-antigen complexes.

Quickstart Usage

Install the Hugging Face datasets package:

pip install datasets

Each subset can be loaded using the Hugging Face datasets library. For example, to load the non-redundant AbAgym dataset:

import datasets

ds = datasets.load_dataset(
    "RosettaCommons/AbAgym",
    name="non_redundant",
    data_dir="non_redundant"
)["train"]

print(ds)
print(ds[0])

Citation

Please cite the original AbAgym publication when using this dataset:

@article{cia2025abagym,
  title   = {AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes},
  author  = {Cia, G. and Li, D. and Poblete, S. and Rooman, M. and Pucci, F.},
  journal = {mAbs},
  volume  = {17},
  number  = {1},
  year    = {2025}
}

Dataset Card Authors

Jeongbin Park (jeongbp@umich.edu) contributed as a project during BIDS-TP2026, Universiy of Michigan, Ann Arbor

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