metadata

dataset_info:
  features:
    - name: seq
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 1875591
      num_examples: 6000
    - name: test
      num_bytes: 480997
      num_examples: 1332
  download_size: 2310262
  dataset_size: 2356588
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: apache-2.0
task_categories:
  - text-classification
tags:
  - chemistry
  - biology
  - medical

Dataset Card for Metal Ion Binding Dataset

Dataset Summary

Metal ion binding sites within proteins play a crucial role across a spectrum of processes, spanning from physiological to pathological, toxicological, pharmaceutical, and diagnostic. Consequently, the development of precise and efficient methods to identify and characterize these metal ion binding sites in proteins has become an imperative and intricate task for bioinformatics and structural biology.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating the existence of metal-ion binding site(s) on a given protein sequence. See the metal ion binding dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':1}

The average for the seq and the label are provided below:

Feature	Mean Count
seq	309
label (0)	0.5
label (1)	0.5

Data Fields

seq: a string containing the protein sequence
label: an integer label indicating the existence of metal-ion binding site(s) on a given protein sequence

Data Splits

The metal ion binding dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	6,000
Test	1,332

Source Data

Initial Data Collection and Normalization

We employ data collected from Cheng et al curated from the Protein Data Bank (PDB).

Licensing Information

The dataset is released under the Apache-2.0 License.

Citation

If you find our work useful, please consider citing the following paper:

@misc{chen2024xtrimopglm,
  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
  year={2024},
  eprint={2401.06199},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  note={arXiv preprint arXiv:2401.06199}
}