TemBERTure

1 简介

蛋白质的热稳定性（thermostability）在生物技术领域具有重要意义，尤其是在制药、食品和生物燃料生产等行业中，热稳定性蛋白质能够加速化学反应，降低生产成本。然而，传统的实验方法用于评估蛋白质热稳定性不仅耗时、昂贵，且难以大规模扩展，导致可用的蛋白质热稳定性数据有限。

研究者开发了TemBERTureDB¹，数据来自于Meltome Atlas实验²、UniProtKB³、BacDive⁴、NCBI⁵，一个包含超过48,000个蛋白质序列的数据库，涵盖13种物种的热稳定和非热稳定蛋白质， 共600,000条数据。

图1. A图是TemBERTure_DB的构成，B图为TemBERTure的架构示意图，其中TemBERTure_CLS使用分类预测头，TemBERTure_Tm使用回归预测头

基于现有的protBERT-BFD⁶语言模型，通过adapter-base^7,8的方法进行微调，开发出TemBERTure_CLS和TemBERTure_Tm分别用于预测蛋白质的热稳定性类别和熔点温度。TemBERTure_CLS在预测热稳定性类别方面表现出色，准确率为0.89，F1分数为0.9。TemBERTure_Tm在预测熔点温度方面存在偏差，呈现出双峰分布，但其在预测蛋白质的热稳定性类别方面具有较高的准确性。

图2. TemBERTure_CLS性能表现

图2. TemBERTure_Tm性能表现

2 参数说明

输入待预测的序列/fasta文件

3 结果说明

列名	说明
Sequence	输入的序列
Tm	模型预测的蛋熔点温度值(Melting Temperature)
Thermostability Classification	模型预测的蛋白热稳定性类别：Thermophilic与Non-thermophilic
Thermophilicity Prediction Score	模型预测的蛋白嗜热性概率评分，数值在0-1.0之间，越大表示蛋白嗜热的概率越高

4 参考文献

[1] Chiara Rodella, Symela Lazaridi, Thomas Lemmin, TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae103. https://doi.org/10.1093/bioadv/vbae103
[2] Jarzab A, Kurzawa N, Hopf T et al. Meltome atlas—thermal proteome stability across the tree of life. Nat Methods 2020;17:495–503. https://doi.org/10.1038/s41592-020-0801-4
[3] The UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res 2023;51:D523–31. https://doi.org/10.1093/nar/gkac1052
[4] Reimer LC, Sardà Carbasse J, Koblitz J et al. BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res 2022;50:D741–6. https://doi.org/10.1093/nar/gkab961
[5] Sayers EW, Bolton EE, Brister JR et al. Database resources of the national center for biotechnology information. Nucleic Acids Res 2022;50:D20–6. https://doi.org/10.1093/nar/gkab1112
[6] Elnaggar A, Heinzinger M, Dallago C et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans Pattern Anal Mach Intell 2022;44:7112–27. https://doi.org/10.1109/TPAMI.2021.3095381
[7] Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M. & Gelly, S.. (2019). Parameter-Efficient Transfer Learning for NLP. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:2790-2799 Available from https://proceedings.mlr.press/v97/houlsby19a.html
[8] Poth et al., Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning. EMNLP 2023. https://aclanthology.org/2023.emnlp-demo.13/