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 |
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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