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

Wan-Chen Zhao; Hai-Yang Huo; Zhong-Yuan Lu; Zhao-Yan Sun

doi:10.1007/s10118-025-3451-2

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

RESEARCH ARTICLE | Updated：2025-11-14

- Analyzing Conformational Transition Pathways in Semi-flexible Polymer Chains with Deep Learning
- Chinese Journal of Polymer Science Vol. 43, Pages: 2201-2212(2025)
- Affiliations：
  
  a.State Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  b.State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, China
  c.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
- Author bio：
  
  luzhu@jlu.edu.cn (Z.Y.L.)
  zysun@ciac.ac.cn (Z.Y.S.)
- Funds：
- DOI：10.1007/s10118-025-3451-2
  CLC：
- Received：01 August 2025，
  
  Accepted：09 September 2025，
  
  Published Online：12 November 2025，
  
  Published：15 December 2025
- Accepted：
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Zhao, W. C.; Huo, H. Y.; Lu, Z. Y.; Sun, Z. Y. Analyzing conformational transition pathways in semi-flexible polymer chains with deep learning. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-025-3451-2

Wan-Chen Zhao, Hai-Yang Huo, Zhong-Yuan Lu, et al. Analyzing Conformational Transition Pathways in Semi-flexible Polymer Chains with Deep Learning[J/OL]. Chinese journal of polymer science, 2025, 432201-2212.
Zhao, W. C.; Huo, H. Y.; Lu, Z. Y.; Sun, Z. Y. Analyzing conformational transition pathways in semi-flexible polymer chains with deep learning. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-025-3451-2 DOI：

Wan-Chen Zhao, Hai-Yang Huo, Zhong-Yuan Lu, et al. Analyzing Conformational Transition Pathways in Semi-flexible Polymer Chains with Deep Learning[J/OL]. Chinese journal of polymer science, 2025, 432201-2212. DOI： 10.1007/s10118-025-3451-2.

摘要

Abstract

Polymers often exhibit multi-state conformational transitions with multiple pathways as temperature varies. However

characterizing the inherent features of these pathways is hindered by the lack of physical characterizations that can distinguish various transition pathways between complex and disordered states. In this work

we introduced a machine-learning framework based on spatiotemporal point-cloud neural networks to identify and analyze conformational transition pathways in polymer chains. As a case study

we applied this framework to the temperature-induced unfolding of a single semi-flexible polymer chain

simulated

via

coarse-grained molecular dynamics. We first combined spatiotemporal point cloud neural networks and contrastive

learning to extract features of conformational evolution

and then we employed unsupervised learning methods to cluster distinct transition pathways and unfolding trajectories. Our results reveal that

with increasing temperature

semi-flexible polymer chains exhibit five distinct unfolding pathways: rigid rod

$$ \to $$

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random coil; small toroid

$$ \to $$

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large toroid

$$ \to $$

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hairpin

$$ \to $$

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random coil; rod bundle

$$ \to $$

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hairpin

$$ \to $$

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random coil; hairpin

$$ \to $$

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random coil; and tailed structure

$$ \to $$

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random coil. We further calculated the structural order parameters of those typical conformations with distinct transition pathways

we distincted five transition mechanisms

including the straightening of rigid rods

tightening of small rings

expansion of hairpin ends

symmetrization of rod bundles

and retraction of tailed structures. These findings demonstrate that our framework presents a promising data-driven approach for analyzing complex conformational transitions in disordered polymers

which might be potentially extendable to other heterogeneous systems like intrinsically disordered proteins.

关键词

Keywords

references

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