---
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
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    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}
}
```