TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning

Hao Jiang; Gong-Bo Zhang; Yu-Xiang Wang; Feng-Yi Jiang; Hong-Yu Zhang; Zhi-Wei Nie; Li Yuan; Jie Chen; Wen-Bin Zhang

doi:10.1007/s10118-025-3447-y

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TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning

RESEARCH ARTICLE | Updated：2025-12-03

- TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning
- TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning
- Chinese Journal of Polymer Science 2025年43卷第12期页码：2191-2200
- Affiliations：
  
  a.Beijing National Laboratory for Molecular Sciences, Key Laboratory of Polymer Chemistry and Physics of Ministry of Education, Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
  b.AI for Science (AI4S)-Preferred Program, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
  c.School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
  d.Pengcheng Laboratory, Shenzhen 518055, China
- Author bio：
  
  wenbin@pku.edu.cn
- Funds：
  
  This work was financially supported by the National Key R&D Program of China (Nos. 2020YFA0908100 and 2023YFF1204401), Shenzhen Medical Research Fund (No. B2302037), the National Natural Science Foundation of China (Nos. 22331003 and 21925102), and Beijing National Laboratory for Molecular Sciences (No. BNLMS-CXXM-202006).;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-025-3447-y.
- DOI：10.1007/s10118-025-3447-y
  中图分类号：
- 收稿：2025-06-29，
  
  录用：2025-09-01，
  
  网络首发：2025-11-19，
  
  纸质出版：2025-12-15
- Accepted：
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Jiang, H.; Zhang, G. B.; Wang, Y. X.; Jiang, F. Y.; Zhang, H. Y.; Nie, Z. W.; Yuan, L.; Chen, J.; Zhang, W. B. TmPred: enhancing thermophilic protein melting point prediction with protein language models and deep learning. Chinese J. Polym. Sci. 2025, 43, 2191–2200

Hao Jiang, Gong-Bo Zhang, Yu-Xiang Wang, et al. TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning[J]. Chinese Journal of Polymer Science, 2025, 43(12): 2191-2200.
Jiang, H.; Zhang, G. B.; Wang, Y. X.; Jiang, F. Y.; Zhang, H. Y.; Nie, Z. W.; Yuan, L.; Chen, J.; Zhang, W. B. TmPred: enhancing thermophilic protein melting point prediction with protein language models and deep learning. Chinese J. Polym. Sci. 2025, 43, 2191–2200 DOI： 10.1007/s10118-025-3447-y.

Hao Jiang, Gong-Bo Zhang, Yu-Xiang Wang, et al. TmPred: Enhancing Thermophilic Protein Melting Point Prediction with Protein Language Models and Deep Learning[J]. Chinese Journal of Polymer Science, 2025, 43(12): 2191-2200. DOI： 10.1007/s10118-025-3447-y.

摘要

TmPred is a deep learning model that combines language models

GCNs

and Graphormer to predict melting temperatures of thermophilic proteins. It outperforms existing methods with improved RMSE

Pearson

and R² and shows strong generalization

offering an effective tool for thermophilic protein mining and engineering.

Abstract

Thermophilic proteins maintain their structure and function at high temperatures

making them widely useful in industrial applications. Due to the complexity of experimental measurements

predicting the melting temperature (

) of proteins has become a research hotspot. Previous methods rely on amino acid composition

physicochemical properties of proteins

and the optimal growth temperature (OGT) of hosts for

prediction. However

their performance in predicting

values for thermophilic proteins (

60 °C) are generally unsatisfactory due to data scarcity. Herein

we introduce TmPred

prediction model for thermophilic proteins

that combines protein language model

graph convolutional network and Gr

aphormer module. For performance evaluation

TmPred achieves a root mean square error (RMSE) of 5.48 °C

a pearson correlation coefficient (

) of 0.784

and a coefficient of determination (

) of 0.613

representing improvements of 19%

15%

and 32%

respectively

compared to the state-of-the-art predictive models like DeepTM. Furthermore

TmPred demonstrated strong generalization capability on independent blind test datasets. Overall

TmPred provides an effective tool for the mining and modification of thermophilic proteins by leveraging deep learning.

关键词

Keywords

references

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