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---
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}
}
``` |