metadata

dataset_info:
  features:
    - name: input_ids
      sequence: int16
    - name: coords
      sequence:
        sequence: float64
    - name: labels
      dtype: int64
  splits:
    - name: train
      num_bytes: 60578601712
      num_examples: 3820837
    - name: val
      num_bytes: 3036676376
      num_examples: 192371
    - name: test
      num_bytes: 10230362892
      num_examples: 648372
  download_size: 12182948798
  dataset_size: 73845640980
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*

Residue identity prediction

Overview

Understanding the structural role of individual amino acids is important for engineering new proteins. We can understand this role by predicting the substitutabilities of different amino acids at a given protein site based on the surrounding structural environment. We generate a novel dataset consisting of atomic environments extracted from nonredundant structures in the PDB. We formulate this as a classification task where we predict the identity of the amino acid in the center of the environment based on all other atoms.

Datasets

splits:
- split-by-cath-topology: split by CATH 4.2 topology class at the domain level (NOTE: only indices available for download currently due to size of dataset)

Citation Information

@article{townshend2020atom3d,
  title={Atom3d: Tasks on molecules in three dimensions},
  author={Townshend, Raphael JL and V{\"o}gele, Martin and Suriana, Patricia and Derry, Alexander and Powers, Alexander and Laloudakis, Yianni and Balachandar, Sidhika and Jing, Bowen and Anderson, Brandon and Eismann, Stephan and others},
  journal={arXiv preprint arXiv:2012.04035},
  year={2020}
}