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Binding memory of liquid molecules

Author

Listed:
  • Shiyi Qin

    (Shenzhen Bay Laboratory)

  • Zhi Yang

    (Shenzhen Bay Laboratory)

  • Huimin Liu

    (Shenzhen Bay Laboratory)

  • Xiaoli Wang

    (Shenzhen Bay Laboratory
    Northeastern University)

  • Bing Miao

    (University of Chinese Academy of Sciences (UCAS))

  • Shangguo Hou

    (Shenzhen Bay Laboratory)

  • Kai Huang

    (Shenzhen Bay Laboratory)

Abstract

Understanding the binding dynamics of liquid molecules is of fundamental importance in physical and life sciences. However, nanoscale fast dynamics pose great challenges for experimental characterization. Conventionally, the binding dynamics have been assumed to be memoryless. Here, we integrate large scale computer simulation, scaling theory, and real-time single particle tracking microscopy with high spatiotemporal precision to unveil a universal memory effect in the binding dynamics of liquid molecules. This binding memory can be quantified by a binding time autocorrelation function, whose power-law decay depends on binding affinity, the topological and materials properties of the surrounding environment and the heterogeneity of the binding landscape. Context-dependent biomolecular binding memory is likely exploited by biological systems to regulate biochemical reactions and biophysical processes. Deciphering this binding memory offers a novel strategy to probe complex biological systems and advanced soft materials.

Suggested Citation

  • Shiyi Qin & Zhi Yang & Huimin Liu & Xiaoli Wang & Bing Miao & Shangguo Hou & Kai Huang, 2025. "Binding memory of liquid molecules," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61630-3
    DOI: 10.1038/s41467-025-61630-3
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    References listed on IDEAS

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    1. Kazem Asadollahi & Sunnia Rajput & Lazarus Andrew Zhang & Ching-Seng Ang & Shuai Nie & Nicholas A. Williamson & Michael D. W. Griffin & Ross A. D. Bathgate & Daniel J. Scott & Thomas R. Weikl & Guy N., 2023. "Unravelling the mechanism of neurotensin recognition by neurotensin receptor 1," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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