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Computational design of superstable proteins through maximized hydrogen bonding

Nature Chemistry volume 18, pages 364–373 (2026)Cite this article

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Abstract

Hydrogen bonds are fundamental chemical interactions that stabilize protein structures, particularly in β sheets, enabling resistance to mechanical stress and environmental extremes. Here, inspired by natural mechanostable proteins with shearing hydrogen bonds, such as titin and silk fibroin, we de novo designed superstable proteins by maximizing hydrogen-bond networks within force-bearing β strands. Using a computational framework combining artificial intelligence-guided structure and sequence design with all-atom molecular dynamics MD simulations, we systematically expanded protein architecture, increasing the number of backbone hydrogen bonds from 4 to 33. The resulting proteins exhibited unfolding forces exceeding 1,000 pN, about 400% stronger than the natural titin immunoglobulin domain, and retained structural integrity after exposure to 150 °C. This molecular-level stability translated directly to macroscopic properties, as demonstrated by the formation of thermally stable hydrogels. Our work introduces a scalable and efficient computational strategy for engineering robust proteins, offering a generalizable approach for the rational design of resilient protein systems for extreme environments.

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Fig. 1: Design of SuperMyo protein with elongated force-bearing β strands combining AI tools and MD simulations.
Fig. 2: Experimental validation of designed SuperMyos with higher mechanostability.
Fig. 3: Mechanical analysis of SuperMyo protein series.
Fig. 4: Excellent mechanical performance of SuperMyo after extreme thermal treatment.
Fig. 5: SuperMyo constructed as protein hydrogels demonstrating superior macroscopic properties.

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Data availability

All data are available in the main text or in the Supplementary Information. The AlphaFold2-predicted structure of the designed SuperMyo protein is provided in Supplementary Data 1, and the screening metric data generated during the design process are available in Supplementary Data 2. The solution NMR structure for A339 in Fig. 2g has been deposited in the PDB (9UKW). Source data are provided with this paper.

Code availability

The Python script for automated execution of annealing simulations and SMD is available via GitHub at https://github.com/Andachten/AutoMD.

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Acknowledgements

The authors acknowledge the High-Performance Computing Center (HPCC) of Nanjing University for computational resources and thank Y. Cao and B. Xue for their insightful discussions. Support for the protein design computations, protein expression and purification, and the AMF–SMFS and material characterizations was provided by grants awarded to P.Z. from the National Natural Science Foundation of China (22222703, 22477058, 22588302), the Natural Science Foundation of Jiangsu Province (BK20202004) and the Fundamental Research Funds for the Central Universities (020514380335, KG202503). Support for the protein sample expression and data acquisition for solution NMR was provided by grants awarded to Y.J. from the National Natural Science Foundation of China (32271316) and the Jiangsu Provincial Science and Technology Major Plan Special Fund (BG2024026). Credit: muscle icon in the graphical abstract, Bioicons.com under a Creative Commons license CC-BY 3.0.

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Author notes

  1. These authors contributed equally: Bin Zheng, Zhuojian Lu, Shangchen Wang, Lichao Liu, Mingjun Ao.

Authors and Affiliations

  1. Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, State Key Laboratory of Coordination Chemistry, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Frontier Interdisciplinary Science Research Center, Nanjing University, Nanjing, China

    Bin Zheng, Zhuojian Lu, Shangchen Wang, Lichao Liu, Mingjun Ao, Guojing Tang, Ruishi Wang, Yuanhao Liu, Hantian Zhang, Yinying Meng, Jun Qiu, Tianfu Feng, Ziyi Wang, Renming Liu, Yuelong Xiao, Yutong Liu, Yifen Huang & Peng Zheng

  2. State Key Laboratory of Pharmaceutical Biotechnology, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Life Sciences, Nanjing University, Nanjing, China

    Yurui Zhou, Ziling Wang & Yajun Jiang

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  1. Bin Zheng
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Contributions

P.Z. and B.Z. conceived and designed the project. B.Z., Y.Z. and Ziling Wang developed the method. B.Z., Z.L., S.W., L.L., M.A., Y.Z., G.T., R.W., Yuanhao Liu, H.Z., Y.M., J.Q., T.F., Ziyi Wang, R.L., Y.X., Yutong Liu, Ziling Wang, Y.J. and Y.H. performed the experiments. P.Z. supervised the project. The manuscript was written by P.Z. and B.Z., with contributions and approval from all authors.

Corresponding author

Correspondence to Peng Zheng.

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The authors declare no competing interests.

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Nature Chemistry thanks Rafael Bernardi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Text, Figs. 1–14 and Tables 1–3.

Supplementary Video 1 (download MP4 )

Video of SMD simulation of A339.

Supplementary Video 2 (download MP4 )

Video of SMD simulation of B42.

Supplementary Video 3 (download MP4 )

Video of SMD simulation of C352.

Supplementary Video 4 (download MP4 )

Video of SMD simulation of D399.

Supplementary Video 5 (download MP4 )

Video of SMD simulation of E389.

Supplementary Video 6 (download MP4 )

Video of SMD simulation of F553.

Supplementary Video 7 (download MP4 )

Video of annealing simulation of A339.

Supplementary Video 8 (download MP4 )

Video of annealing simulation of B42.

Supplementary Video 9 (download MP4 )

Video of annealing simulation of C352.

Supplementary Video 10 (download MP4 )

Video of annealing simulation of D399.

Supplementary Video 11 (download MP4 )

Video of annealing simulation of E389.

Supplementary Video 12 (download MP4 )

Video of annealing simulation of F553.

Supplementary Data 1 (download ZIP )

Compressed ZIP file Supplementary Data 1 contains the AF2 predicted structure files of the top three designs in the SuperMyo A-F series.

Supplementary Data 2 (download ZIP )

Compressed ZIP file Supplementary Data 2 contains the CSV tables in the SI file include the pLDDT scores from AF2 outputs, pLDDT scores from ESMFold outputs, the average unfolding forces from three 1 nm/ns SMD simulations, the average unfolding forces from three 0.1 nm/ns SMD simulations, and the maximum RMSD values of the proteins observed during the annealing simulation in the SuperMyo design process.

Source data

Source Data Fig. 2 (download XLSX )

SMD data for Fig. 2b; AFM–SMFS force curves data for Fig. 2d; Statistical source data of unfolding force for Fig. 2e; CD data for Fig. 2f.

Source Data Fig. 3 (download XLSX )

SMD data for Fig. 3b; AFM–SMFS force curves data for Fig. 3c; Statistical source data of unfolding force for Fig. 3d; H-bonds number vs. unfolding force for Fig. 3e; Refolding force curves for Fig. 3g.

Source Data Fig. 4 (download XLSX )

Annealing simulation data for Fig. 4a; CD data for Fig. 4b; UV–vis data for Fig. 4e; AFM–SMFS force curves data for Fig. 4g; Statistical source data of unfolding force for Fig. 4h.

Source Data Fig. 5 (download XLSX )

Rheology data for Fig. 5b; Real-time heating rheology data for Fig. 5d.

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Zheng, B., Lu, Z., Wang, S. et al. Computational design of superstable proteins through maximized hydrogen bonding. Nat. Chem. 18, 364–373 (2026). https://doi.org/10.1038/s41557-025-01998-3

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