PTSP-BERT: Predict the thermal stability of proteins using sequence-based bidirectional representations from transformer-embedded features

Zhibin Lv; Mingxuan Wei; Hongdi Pei; Shiyu Peng; Mingxin Li; Liangzhen Jiang

doi:10.1016/j.compbiomed.2024.109598

PTSP-BERT: Predict the thermal stability of proteins using sequence-based bidirectional representations from transformer-embedded features

Comput Biol Med. 2024 Dec 20:185:109598. doi: 10.1016/j.compbiomed.2024.109598. Online ahead of print.

Authors

Zhibin Lv¹, Mingxuan Wei², Hongdi Pei³, Shiyu Peng², Mingxin Li², Liangzhen Jiang⁴

Affiliations

¹ College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China. Electronic address: lvzhibin@pku.edu.cn.
² College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China.
³ Department of Biomedical Engineering, Johns Hopkins University, MD, 21218, USA.
⁴ College of Food and Biological Engineering, Chengdu University, Chengdu, 610106, China; Country Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu, 610106, China.

PMID: 39708499
DOI: 10.1016/j.compbiomed.2024.109598

Abstract

Thermophilic proteins, mesophiles proteins and psychrophilic proteins have wide industrial applications, as enzymes with different optimal temperatures are often needed for different purposes. Convenient methods are needed to determine the optimal temperatures for proteins; however, laboratory methods for this purpose are time-consuming and laborious, and existing machine learning methods can only perform binary classification of thermophilic and non-thermophilic proteins, or psychrophilic and non-psychrophilic proteins. Here, we developed a deep learning model, PSTP-BERT, based on protein sequences that can directly perform Three classes identification of thermophilic, mesophilic, and psychrophilic proteins. By comparing BERT-bfd with other deep learning models using five-fold cross-validation, we found that BERT-bfd-extracted features achieved the highest accuracy under six classifiers. Furthermore, to improve the model's accuracy, we used SMOTE (synthetic minority oversampling technique) to balance the dataset and light gradient-boosting machine to rank BERT-bfd-extracted features according to their weights. We obtained the best-performing model with five-fold cross-validation accuracy of 89.59 % and independent test accuracy of 85.42 %. The performance of the PSTP-BERT is significantly better than that of existing models in Three classes identification task. In order to compare with previous binary classification models, we used PSTP-BERT to perform binary classification tasks of thermophilic and non-thermophilic protein, and psychrophilic and non-psychrophilic protein on an independent test set. PSTP-BERT achieved the highest accuracy on both binary classification tasks, with an accuracy of 93.33 % for thermophilic protein binary classification and 88.33 % for psychrophilic protein binary classification. The accuracy of the independent test of the model can reach between 89.8 % and 92.9 % after training and optimization of the training set with different sequence similarities, and the prediction accuracy of the new data can exceed 97 %. For the convenience of future researchers to use and reference, we have uploaded source code of PSTP-BERT to GitHub.

Keywords: BERT; Deep learning; Psychrophilic proteins; Thermophilic proteins; Three classes identification.