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

Your Location：

Home >

Browse articles >

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
- Chinese Journal of Polymer Science Vol. 43, Issue 12, Pages: 2191-2200(2025)
- 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：
- DOI：10.1007/s10118-025-3447-y
  CLC：
- Received：29 June 2025，
  
  Accepted：01 September 2025，
  
  Online First：19 November 2025，
  
  Published：15 December 2025
- Accepted：
Scan QR Code
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

Atalah, J.; Cáceres-Moreno, P.; Espina, G.; Blamey, J. M. Thermophiles and the applications of their enzymes as new biocatalysts. Bioresour. Technol. 2019 , 280 , 478−488..

Bloom, J. D.; Labthavikul, S. T.; Otey, C. R.; Arnold, F. H. Protein stability promotes evolvability. Proc. Natl. Acad. Sci. 2006 , 103 , 5869−5874..

Rigoldi, F.; Donini, S.; Redaelli, A.; Parisini, E.; Gautieri, A. Review: engineering of thermostable enzymes for industrial applications. APL Bioeng. 2018 , 2 , 011501..

Chu, L.; Zhang, X.; Li, J.; Den g, X.; Wu, M.; Cheng, Y.; Zhu, W.; Qian, X.; Bai, Y. Continuous-flow synthesis of polysubstituted γ -butyrolactones via enzymatic cascade catalysis. Chin. Chem. Lett. 2024 , 35 , 108896..

Zhao, S.; Li, B.; Song, Y.; Wu, S.; Hu, H.; Wang, J.; Shi, L.; Huang, F. Temperature-responsive self-assembly nano- chaperone protects green fluorescent proteins from thermal denaturation. Supramol. Mater. 2024 , 3 , 100067..

Zhang, C.; Bai, X.; Chen, S.; Dedkova, L. M.; Hecht, S. M. Local conformational constraint of firefly luciferase can affect the energy of bioluminescence and enzyme stability. CCS Chem. 2022 , 4 , 1695−1707..

Bai, X.; Liu, Y.; Lee, J.; Fang, J.; Wu, W. H.; Seo, J.; Zhang, W. B. Cellular synthesis of protein pretzelanes. Giant 2022 , 10 , 100092..

Zhang, F.; Liu, Y.; Shao, Y.; Zhang, W. B. Active template synthesis of protein [ n ] catenanes using engineered peptide-peptide ligation tools. CCS Chem. 2024 , 6 , 377−389..

Qu, Z.; Fang, J.; Wa ng, Y. X.; Sun, Y.; Liu, Y.; Wu, W. H.; Zhang, W. B. A single-domain green fluorescent protein catenane. Nat. Commun. 2023 , 14 , 3480..

Fang, J.; Li, T.; Lee, J.; Lm, D.; Xu, L.; Liu, Y.; Seo, J.; Zhang, W. B. A single-domain protein catenane of dihydrofolate reductase. Natl. Sci. Rev. 2023 , 10 , nwad304..

Wang, X. W.; Zhang, W. B. Protein catenation enhances both the stability and activity of folded structural domains. Angew. Chem. Int. Ed. 2017 , 56 , 13985−13989..

Zhou, C. Y.; Yu, M. D.; Li, W. Q. Comparation of three measuring methods for thermodynamic stability of protein. Anal. Test. Technol. Instrum. 2021 , 27 , 252−259..

Teufl, M.; Zajc, C. U.; Traxlmayr, M. W. Engineering strategies to overcome the stability–function trade-off in proteins. ACS Synth. Biol. 2022 , 11 , 1030−1039..

Lehmann, M.; Pasamontes, L.; Lassen, S. F.; Wyss, M. The consensus concept for thermostability engineering of proteins. Biochim Biophys Acta. 2000 , 1543 , 408−415..

Rahban,M.; Zolghadri, S.; Salehi, N.; Ahmad, F.; Haertlé, T.; Rezaei-Ghaleh, N.; Sawyer, L.; Saboury, A. A. Thermal stability enhancement: fundamental concepts of protein engineering strategies to manipulate the flexible structure. Int. J. Biol. Macromol. 2022 , 214 , 642−654..

Kumar, M. D.; Bava, K. A.; Gromiha, M. M.; Prabakaran, P.; Kitajima, K.; Uedaira, H.; Sarai, A. ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res. 2006 , 34 , D204−D206..

Jarzab, A.; Kurzawa, N.; Hopf, T.; Moerch, M.; Zecha, J.; Leijten, N.; Bian, Y.; Musiol, E.; Maschberger, M.; Stoehr, G.; Becher, I.; Daly, C.; Samaras, P.; Mergner, J.; Spanier, B.; Angelov, A.; Werner, T.; Bantscheff, M.; Wilhelm, M.; Klingenspor, M.; Lemeer, S.; Liebl, W.; Hahne, H.; Savitski, M. M.; Kuster, B. Meltome atlas—thermal proteome stability across the tree of life. Nat. Methods 2020 , 17 , 495−503..

Nikam, R.; Kulandaisamy, A.; Harini, K.; Sharma, D.; Gromiha, M. M. ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Res. 2021 , 49 , 240−244..

Kulandaisamy, A.; Sakthivel, R.; Gromiha, M. M. MPTherm: database for membrane protein thermodynamics for understanding folding and stability. Briefings Bioinf. 2021 , 22 , 2119−2125..

Zhang, G. Y.; Liu, G. L.; Fang, B. S. A study on the recognition of thermophilic and mesophilic proteins based on support vector machine. Comput. Appl. Chem. 2006 , 23 , 707−710..

Ku, T.; Lu, P.; Chan, C.; Wang, T.; Lai, S.; Lyu, P.; Hsiao, N. Predicting melting temperature directly from protein sequences. Comput. Biol. Chem. 2009 , 33 , 445−450..

Dehouck, Y.; Folch, B.; Rooman, M. Revisiting the correlation between proteins’ thermoresistance and organisms’ thermophilicity. Protein Eng. Des. Sel. 2008 , 21 , 275−278..

Gromiha, M. M.; Oobatake, M.; Sarai, A. Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins. Biophys. Chem. 1999 , 82 , 51−67..

Sun, L. W.; Li, H.; Zhang, X. Q.; Gao, H. B.; Luo, M. B. Identifying conformation states of polymer through unsupervised machine learning. Chinese J. Polym. Sci. 2020 , 38 , 1403−1408..

Xu, C.; Lu, Y.; Deng, X.; Yu, P. Prediction of molecular conformation using deep generative neural networks. Chin. J. Chem. 2023 , 41 , 3684−3688..

Banerjee, A.; Hsu, H. P.; Kremer, K.; Kukharenko,O. Data-driven identification and analysis of the glass transition in polymer melts. ACS Macro Lett. 2023 , 12 , 679−684..

Hong, X.; Yang, Q.; Liao, K.; Pei, J.; Chen, M.; Mo, F.; Lu, H.; Zhang, W. B.; Zhou, H.; Chen, J.; Su, L.; Zhang, S. Q.; Liu, S.; Huang, X.; Sun, Y. Z.; Wang, Y.; Zhang, Z.; Yu, Z.; Luo, S.; Fu, X. F.; You, S. L. AI for organic and polymer synthesis. Sci. China Chem. 2024 , 67 , 2461−2496..

Wang, T. Y.; Li, J. F. Application of deep learning in protein structure prediction and its inspirations. Acta Polymerica Sinica (in Chinese) 2022 , 53 , 581−591..

[Ferguson, A. L.; Ranganathan, R. 100th Anniversary of macromolecular science viewpoint: data-driven protein design. ACS Macro Lett . 2021 , 10 , 327–340..

Zhao, Y.; Liu, Q.; Du, J.; Meng, Q.; Zhang, L. Machine learning methods for developments of binding kinetic models in predicting protein-ligand dissociation rate constants. Smart Mol. 2023 , 1 , e20230012..

Pucci, F.; Kwasigroch, J. M.; Rooman, M. SCooP: an accurate and fast predictor of protein stability curves as a function of temperature. Bioinformatics 2017 , 33 , 3415−3422..

[Gorania, M.; Seker, H.; Haris, P. I. Predicting a protein’s melting temperature from its amino acid sequence. IEEE EMBS 2010 , 1820–1823..

Yang, Y.; Zhao, J.; Zeng, L.; Vihinen, M. ProTstab2 for prediction of protein thermal stabilities. Int. J. Mol. Sci. 2022 , 23 , 10798..

[Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q. W.; Liu, T. Y. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst . 2017 , 31, 3149–3157..

Jung, F.; Frey, K.; Zimmer, D.; Mühlhaus, T. DeepSTABp: a deep learning approach for the prediction of thermal protein stability. Int. J. Mol. Sci. 2023 , 24 , 7444..

Elnaggar, A.; Heinzinger, M.; Dallago, C.; Rehawi, G.; Wang, Y.; Jones, L.; Gibbs, T.; Feher, T.; Angerer, C.; Steinegger, M.; Bhowmik, D.; Rost, B. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 2022 , 44 , 7112−7127..

Li, M.; Wang, H.; Yang, Z.; Zhang, L.; Zhu, Y. DeepTM: a deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences. Comput. Struct. Biotechnol. J. 2023 , 21 , 5544−5560..

Durowoju, I. B.; Bhandal, K. S.; Hu, J.; Carpick, B.; Kirkitadze, M. Differential scanning calorimetry—a method for assessing the thermal stability and conformation of protein antigen. J. Vis. Exp. 2017 , 121 , e55262..

[Kipf, T. N.; Welling, M. Semi-supervised classification with graph convolutional networks. Int. Conf. Learn. Represent . 2017 ..

[Ying, C.; Cai, T.; Luo, S.; Zheng, S.; Ke, G.; He, D.; Shen, Y.; Liu, T. Y. Do transformers really perform badly for graph representation Adv. Neural Inf. Process. Syst . 2020 , 34 , 28877–28888..

Mu, J.; Li, Z.; Zhang, B.; Zhang, Q.; Iqbal, J.; Wadood, A.; Wei, T.; Feng, Y.; Chen, H. F. Graphormer supervised de novo protein design method and function validation. Briefings Bioinf. 2023 , 25 , bbae135..

UniProt Consortium. UniProt: the universal protein knowledgebase in 2025. Nucleic Acids Res. 2025 , 53 , 609−617..

Li, W.; Jaroszewski, L.; Godzik, A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 2001 , 17 , 282−283..

Erickson, E.; Gado, J. E.; Avilán, L.; Bratti, F.; Brizendine, R. K.; Cox, P. A.; Gill, R.; Graham, R.; Kim, D. J.; König, G.; Michener, W. E.; Poudel, S.; Ramirez, K. J.; Shakespeare, T. J.; Zahn, M.; Boyd, E. S.; Payne, C. M.; DuBois, J. L.; Pickford, A. R.; Beckham, G. T.; McGeehan, J. E. Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity. Nat. Commun. 2022 , 13 , 7850..

Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zinlinski, M.; Steinegger, M.; Pacholask, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 , 596 , 583−589..

Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2020 , 31 , 6000−6010..

Hanson, J.; Paliwal, K.; Litfin, T.; Yang, Y.; Zhou, Y. Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. Bioinformatics 2018 , 34 , 4039−4045..

Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, J.; Killeen, T.; Lin, Z.; Gemelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S. PyTorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 2019 , 33 , 8026−8037..

[Kingma, D. P.; Ba, J. Adam: A method for stochastic optimization. Int. Conf. Learn. Represent . 2015 ..

Heinzinger, M.; Weissenow, K.; Sanchez, J. G.; Henkel, A.; Mirdita, M.; Steinegger, M.; Rost, B. ProstT5: Bilingual Language Model for Protein Sequence and Structure. NAR Genom. Bioinform. 2024 , 6 , lqae150..

Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; Dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023 , 379 , 1123−1130..

Heinzinger, M.; Elnaggar, A.; Wang, Y.; Dallago, C.; Nechaev, D.; Matthes, F.; Rost, B. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics 2019 , 20 , 723..

van Kempen, M.; Kim, S. S.; Tumescheit, C.; Mirdita, M.; Lee, J.; Gilchrist, C. L. M.; Söding, J.; Steinegger, M. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 2024 , 42 , 243−246..

Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997 , 9 , 1735−1780..

[Zeyer, A.; Bahar, P.; Irie, K.; Schlüter, R.; Ney, H. A Comparison of transformer and LSTM encoder decoder models for ASR. IEEE ASRU 2019 , 8–15..

Views

463

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Analyzing Conformational Transition Pathways in Semi-flexible Polymer Chains with Deep Learning

Experimental Investigation of Graphene and TiO2 Reinforced Poly(vinyl alcohol)-based Fiber Metal Laminates

On-demand Peelable UV-curable Adhesives with Superior Bonding Strength: Rational Molecular Engineering of Cohesion-adhesion Synergy

Enhancing Processability of Phenylethynyl-terminated Imide Oligomers and Properties of Cured Polyimides via ―CF3 Incorporation

Two-way Shape Memory Effect of Polycaprolactone-based Polyurethane via Synergistic Role of Hydrogen Bonding and Crystallization Tuned by Malic Acid

Related Author

Zhao-Yan Sun

Zhong-Yuan Lu

Hai-Yang Huo

Wan-Chen Zhao

Related Institution

State Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences

State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University

School of Applied Chemistry and Engineering, University of Science and Technology of China

⁰