AI4Protein/GO_CC_AlphaFold2

---
license: apache-2.0
task_categories:
- text-classification
tags:
- protein
- downstream task
---

# GO-CC Dataset with AlphaFold2 Structural Sequence

- Description: Cellular Component of Gene Ontology (GO) project.
- Number of labels: 320
- Problem Type: multi_label_classification
- Columns:
- aa_seq: protein amino acid sequence
- foldseek_seq: foldseek 20 3di structural sequence
- ss8_seq: DSSP 8 secondary structure sequence

# Github

Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models

https://github.com/tyang816/SES-Adapter

# Citation
Please cite our work if you use our dataset.
```
@article{tan2024ses-adapter,
title={Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models},
author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
journal={Journal of Chemical Information and Modeling},
year={2024},
publisher={ACS Publications}
}
```

许可证：Apache-2.0
任务类别：
- 文本分类
标签：
- 蛋白质
- 下游任务

# 基于AlphaFold2（阿尔法折叠2）结构序列的GO-CC数据集

- 数据集描述：基因本体（Gene Ontology，GO）项目的细胞组分数据
- 标签总数：320个
- 任务类型：多标签分类
- 字段列表：
- aa_seq：蛋白质氨基酸序列
- foldseek_seq：Foldseek生成的20类3DI结构序列
- ss8_seq：DSSP生成的8状态二级结构序列

# 开源项目
简单、高效且可扩展的结构感知适配器助力蛋白质大语言模型性能提升
https://github.com/tyang816/SES-Adapter

# 引用声明
若您使用本数据集，请引用我们的研究成果。

@article{tan2024ses-adapter,
title={简单、高效且可扩展的结构感知适配器助力蛋白质大语言模型性能提升},
author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
journal={《化学信息与建模杂志》(Journal of Chemical Information and Modeling)},
year={2024},
publisher={ACS出版社}
}