Skip to main content
Log in

An atomistic model of silk protein network for studying the effect of pre-stretching on the mechanical performances of silks

预拉伸对丝蛋白力学性能影响的全原子网络模型

  • Research Paper
  • Published:
Acta Mechanica Sinica Aims and scope Submit manuscript

Abstract

Silk protein builds one of the strongest natural fibers based on its complex nanocomposite structures. However, the mechanical performance of silk protein, related to its molecular structure and packing is still elusive. In this study, we constructed an atomistic silk protein network model, which reproduces the extensive connection topology of silk protein with structure details of the β-sheet crystallites and amorphous domains. With the silk protein network model, we investigated the structure evolution and stress distribution of silk protein under external loading. We found a pre-stretching treatment during the spinning process can improve the strength of silk protein. This treatment improves the properties of silk protein network, i.e., increases the number of nodes and bridges, makes the nodes distributed homogeneously, and induces the bridges in the network well aligned to the loading direction, which is of great benefit to the mechanical performances of silk protein. Our study not only provides a realized atomistic model for silk protein network that well represents the structures and deformations of silk proteins under loading, but also gains deep insights into the mechanism how the pre-loading on silk proteins during spinning improves the mechanical properties of silk fibers.

摘要

丝蛋白因其复杂的纳米复合结构成为自然界中最强的纤维之一, 但是丝蛋白的纳米结构和其力学性能之间的关系仍不明确. 在本研究中, 我们构建的丝蛋白全原子网络模型充分描述了β-微晶与无定形域交联的拓扑结构. 通过该模型我们研究了在外部载荷作用下, 丝蛋白的微观结构演化及其应力分布. 我们发现纺丝过程中的预拉伸处理可以提升丝蛋白的拉伸强度. 研究发现预拉伸处理增强了丝蛋白的网络结构性能, 包括预拉伸使得网络内“节点”和“桥”的数量的增加, “节点”的分布更加均匀, “桥”沿加载方向排列等. 我们的工作构建了丝蛋白全原子的网络结构模型, 该模型能够描述在外载作用下丝蛋白结构演化和变形之间的关系, 同时也揭示了预拉伸增强丝蛋白力学性能的分子机制.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. L. D. Koh, Y. Cheng, C. P. Teng, Y. W. Khin, X. J. Loh, S. Y. Tee, M. Low, E. Ye, H. D. Yu, Y. W. Zhang, and M. Y. Han, Structures, mechanical properties and applications of silk fibroin materials, Prog. Polym. Sci. 46, 86 (2015).

    Article  Google Scholar 

  2. K. Li, P. Li, and Y. Fan, The assembly of silk fibroin and graphene-based nanomaterials with enhanced mechanical/conductive properties and their biomedical applications, J. Mater. Chem. B 7, 6890 (2019).

    Article  Google Scholar 

  3. N. Du, X. Y. Liu, J. Narayanan, L. Li, M. L. M. Lim, and D. Li, Design of superior spider silk: From nanostructure to mechanical properties, Biophys.l J. 91, 4528 (2006).

    Article  Google Scholar 

  4. G. Xu, L. Gong, Z. Yang, and X. Y. Liu, What makes spider silk fibers so strong? From molecular-crystallite network to hierarchical network structures, Soft Matter 10, 2116 (2014).

    Article  Google Scholar 

  5. M. M. R. Khan, H. Morikawa, Y. Gotoh, M. Miura, Z. Ming, Y. Sato, and M. Iwasa, Structural characteristics and properties of Bombyx mori silk fiber obtained by different artificial forcibly silking speeds, Int. J. Biol. Macromolecules 42, 264 (2008).

    Article  Google Scholar 

  6. Z. Shao, and F. Vollrath, Surprising strength of silkworm silk, Nature 418, 741 (2002).

    Article  Google Scholar 

  7. B. Mortimer, C. Holland, and F. Vollrath, Forced reeling of Bombyx mori silk: Separating behavior and processing conditions, Biomacromolecules 14, 3653 (2013).

    Article  Google Scholar 

  8. X. Zhou, D. Li, S. Wan, Q. Cheng, and B. Ji, In silicon testing of the mechanical properties of graphene oxide-silk nanocomposites, Acta Mech. 230, 1413 (2017).

    Article  Google Scholar 

  9. Y. Cheng, L. D. Koh, F. Wang, D. Li, B. Ji, J. Yeo, G. Guan, M. Y. Han, and Y. W. Zhang, Carbon nanoscroll-silk crystallite hybrid structures with controllable hydration and mechanical properties, Nanoscale 9, 9181 (2017).

    Article  Google Scholar 

  10. Y. Cheng, L. D. Koh, D. Li, B. Ji, Y. Zhang, J. Yeo, G. Guan, M. Y. Han, and Y. W. Zhang, Peptide-graphene interactions enhance the mechanical properties of silk fibroin, ACS Appl. Mater. Interfaces 7, 21787 (2015).

    Article  Google Scholar 

  11. C. Xu, D. Li, Y. Cheng, M. Liu, Y. Zhang, and B. Ji, Pulling out a peptide chain from β-sheet crystallite: Propagation of instability of H-bonds under shear force, Acta Mech. Sin. 31, 416 (2015).

    Article  Google Scholar 

  12. S. Keten, Z. Xu, B. Ihle, and M. J. Buehler, Nanoconfinement controls stiffness, strength and mechanical toughness of β-sheet crystals in silk, Nat. Mater. 9, 359 (2010).

    Article  Google Scholar 

  13. S. Keten, and M. J. Buehler, Geometric confinement governs the rupture strength of H-bond assemblies at a critical length scale, Nano Lett. 8, 743 (2008).

    Article  Google Scholar 

  14. G. Bratzel, and M. J. Buehler, Molecular mechanics of silk nanostructures under varied mechanical loading, Biopolymers 97, 408 (2012).

    Article  Google Scholar 

  15. D. Li, Q. Wang, C. Xu, Y. Cheng, Y. W. Zhang, and B. Ji, How does nature evade the “larger is weaker” fate of ultralong silk β-sheet nanocrystallites, Nano Lett. 20, 8516 (2020).

    Article  Google Scholar 

  16. S. Keten, and M. J. Buehler, Atomistic model of the spider silk nanostructure, Appl. Phys. Lett. 96, 153701 (2010).

    Article  Google Scholar 

  17. S. Keten, and M. J. Buehler, Nanostructure and molecular mechanics of spider dragline silk protein assemblies, J. R. Soc. Interface. 7, 1709 (2010).

    Article  Google Scholar 

  18. Y. Kim, M. Lee, I. Baek, T. Yoon, and S. Na, Mechanically inferior constituents in spider silk result in mechanically superior fibres by adaptation to harsh hydration conditions: a molecular dynamics study, J. R. Soc. Interface. 15, 20180305 (2018).

    Article  Google Scholar 

  19. M. Patel, D. K. Dubey, and S. P. Singh, Phenomenological models of Bombyx mori silk fibroin and their mechanical behavior using molecular dynamics simulations, Mater. Sci. Eng.-C 108, 110414 (2020).

    Article  Google Scholar 

  20. M. Patel, D. K. Dubey, and S. P. Singh, Investigations into the role of water concentration on mechanical behavior and nanomechanics of Bombyx mori silk fibroin using molecular dynamics simulations, J Mater Sci 55, 17019 (2020).

    Article  Google Scholar 

  21. M. Patel, D. K. Dubey, and S. P. Singh, Insights into nanomechanical behavior and molecular mechanisms in Bombyx Mori silk fibroin in saline environment using molecular dynamics analysis, Macromol. Res. 29, 694 (2021).

    Article  Google Scholar 

  22. S. Lin, S. Ryu, O. Tokareva, G. Gronau, M. M. Jacobsen, W. Huang, D. J. Rizzo, D. Li, C. Staii, N. M. Pugno, J. Y. Wong, D. L. Kaplan, and M. J. Buehler, Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres, Nat. Commun. 6, 6892 (2015).

    Article  Google Scholar 

  23. L. Pan, F. Wang, Y. Cheng, W. R. Leow, Y. W. Zhang, M. Wang, P. Cai, B. Ji, D. Li, and X. Chen, A supertough electro-tendon based on spider silk composites, Nat. Commun. 11, 1332 (2020).

    Article  Google Scholar 

  24. N. G. Rim, E. G. Roberts, D. Ebrahimi, N. Dinjaski, M. M. Jacobsen, Z. Martín-Moldes, M. J. Buehler, D. L. Kaplan, and J. Y. Wong, Predicting silk fiber mechanical properties through multiscale simulation and protein design, ACS Biomater. Sci. Eng. 3, 1542 (2017).

    Article  Google Scholar 

  25. S. A. Fossey, G. Némethy, K. D. Gibson, and H. A. Scheraga, Conformational energy studies of β-sheets of model silk fibroin peptides. I. Sheets of poly(Ala-Gly) chains, Biopolymers 31, 1529 (1991).

    Article  Google Scholar 

  26. C. Z. Zhou, F. Confalonieri, M. Jacquet, R. Perasso, Z. G. Li, and J. Janin, Silk fibroin: Structural implications of a remarkable amino acid sequence, Proteins 44, 119 (2001).

    Article  Google Scholar 

  27. D. Ouyang, Investigating the molecular structures of solid dispersions by the simulated annealing method, Chem. Phys. Lett. 554, 177 (2012).

    Article  Google Scholar 

  28. R. C. Bernardi, M. C. R. Melo, and K. Schulten, Enhanced sampling techniques in molecular dynamics simulations of biological systems, Biochim. Biophys. Acta (BBA)-Gen. Subj. 1850, 872 (2015).

    Article  Google Scholar 

  29. X. Daura, B. Jaun, D. Seebach, W. F. van Gunsteren, and A. E. Mark, Reversible peptide folding in solution by molecular dynamics simulation, J. Mol. Biol. 280, 925 (1998).

    Article  Google Scholar 

  30. B. Hess, P-LINCS: A parallel linear constraint solver for molecular simulation, J. Chem. Theor. Comput. 4, 116 (2008).

    Article  Google Scholar 

  31. H. J. C. Berendsen, J. P. M. Postma, W. F. van Gunsteren, A. DiNola, and J. R. Haak, Molecular dynamics with coupling to an external bath, J. Chem. Phys. 81, 3684 (1984).

    Article  Google Scholar 

  32. G. Lang, B. R. Neugirg, D. Kluge, A. Fery, and T. Scheibel, Mechanical testing of engineered spider silk filaments provides insights into molecular features on a mesoscale, ACS Appl. Mater. Interfaces 9, 892 (2017).

    Article  Google Scholar 

  33. J. Zhong, Y. Liu, J. Ren, Y. Tang, Z. Qi, X. Zhou, X. Chen, Z. Shao, M. Chen, D. L. Kaplan, and S. Ling, Understanding secondary structures of silk materials via micro- and nano-infrared spectroscopies, ACS Biomater. Sci. Eng. 5, 3161 (2019).

    Article  Google Scholar 

  34. J. Yin, E. Chen, D. Porter, and Z. Shao, Enhancing the toughness of regenerated silk fibroin film through uniaxial extension, Biomacromolecules 11, 2890 (2010).

    Article  Google Scholar 

  35. D. Liu, A. Tarakanova, C. C. Hsu, M. Yu, S. Zheng, L. Yu, J. Liu, Y. He, D. J. Dunstan, and M. J. Buehler, Spider dragline silk as torsional actuator driven by humidity, Sci. Adv. 5, eaau9183 (2019).

    Article  Google Scholar 

  36. J. Zidek, J. Jancar, A. Milchev, and T. A. Vilgis, Mechanical response of hybrid cross-linked networks to uniaxial deformation: A molecular dynamics model, Macromolecules 47, 8795 (2014).

    Article  Google Scholar 

  37. S. U. I. Nosé, A molecular dynamics method for simulations in the canonical ensemble, Mol. Phys. 100, 191 (2002).

    Article  Google Scholar 

  38. W. G. Hoover, Canonical dynamics: Equilibrium phase-space distributions, Phys. Rev. A 31, 1695 (1985).

    Article  Google Scholar 

  39. D. H. Tsai, The virial theorem and stress calculation in molecular dynamics, J. Chem. Phys. 70, 1375 (1979).

    Article  Google Scholar 

  40. M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, and E. Lindahl, GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX 1–2, 19 (2015).

    Article  Google Scholar 

  41. Y. Duan, C. Wu, S. Chowdhury, M. C. Lee, G. Xiong, W. Zhang, R. Yang, P. Cieplak, R. Luo, T. Lee, J. Caldwell, J. Wang, and P. Kollman, A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations, J. Comput. Chem. 24, 1999 (2003).

    Article  Google Scholar 

  42. W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79, 926 (1983).

    Article  Google Scholar 

  43. T. Darden, D. York, and L. Pedersen, Particle mesh Ewald: An N-log(N) method for Ewald sums in large systems, J. Chem. Phys. 98, 10089 (1993).

    Article  Google Scholar 

  44. U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee, and L. G. Pedersen, A smooth particle mesh Ewald method, J. Chem. Phys. 103, 8577 (1995).

    Article  Google Scholar 

  45. The PyMOL Molecular Graphics System, Version 1.8 Schrodinger, LLC, 2015.

  46. W. Kabsch, and C. Sander, Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features, Biopolymers 22, 2577 (1983).

    Article  Google Scholar 

  47. W. G. Touw, C. Baakman, J. Black, T. A. H. te Beek, E. Krieger, R. P. Joosten, and G. Vriend, A series of PDB-related databanks for everyday needs, Nucleic Acids Res. 43, D364 (2015).

    Article  Google Scholar 

  48. F. Eisenhaber, P. Lijnzaad, P. Argos, C. Sander, and M. Scharf, The double cubic lattice method: efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies, J. Comput. Chem. 16, 273 (1995).

    Article  Google Scholar 

  49. N. Du, Z. Yang, X. Y. Liu, Y. Li, and H. Y. Xu, Structural origin of the strain-hardening of spider silk, Adv. Funct. Mater. 21, 772 (2011).

    Article  Google Scholar 

  50. Q. Wang, and H. C. Schniepp, Nanofibrils as building blocks of silk fibers: Critical review of the experimental evidence, JOM 71, 1248 (2019).

    Article  Google Scholar 

  51. Q. Wang, S. Ling, Q. Yao, Q. Li, D. Hu, Q. Dai, D. A. Weitz, D. L. Kaplan, M. J. Buehler, and Y. Zhang, Observations of 3 nm silk nanofibrils exfoliated from natural silkworm silk fibers, ACS Mater. Lett. 2, 153 (2020).

    Article  Google Scholar 

  52. C. Vepari, and D. L. Kaplan, Silk as a biomaterial, Prog. Polym. Sci. 32, 991 (2007).

    Article  Google Scholar 

  53. J. Sirichaisit, V. L. Brookes, R. J. Young, and F. Vollrath, Analysis of structure/property relationships in silkworm (Bombyx mori) and spider dragline (Nephila edulis) silks using raman spectroscopy, Biomacromolecules 4, 387 (2003).

    Article  Google Scholar 

  54. J. M. Gosline, P. A. Guerette, C. S. Ortlepp, and K. N. Savage, The mechanical design of spider silks: From fibroin sequence to mechanical function, J. Exp. Biol. 202, 3295 (1999).

    Article  Google Scholar 

  55. A. Torres-Sánchez, J. M. Vanegas, and M. Arroyo, Examining the mechanical equilibrium of microscopic stresses in molecular simulations, Phys. Rev. Lett. 114, 258102 (2015).

    Article  Google Scholar 

  56. J. M. Vanegas, A. Torres-Sánchez, and M. Arroyo, Importance of force decomposition for local stress calculations in biomembrane molecular simulations, J. Chem. Theor. Comput. 10, 691 (2014).

    Article  Google Scholar 

  57. O. H. S. Ollila, H. J. Risselada, M. Louhivuori, E. Lindahl, I. Vattulainen, and S. J. Marrink, 3D pressure field in lipid membranes and membrane-protein complexes, Phys. Rev. Lett. 102, 078101 (2009).

    Article  Google Scholar 

  58. A. P. Thompson, S. J. Plimpton, and W. Mattson, General formulation of pressure and stress tensor for arbitrary many-body interaction potentials under periodic boundary conditions, J. Chem. Phys. 131, 154107 (2009).

    Article  Google Scholar 

  59. J. Magoshi, Y. Magoshi, and S. Nakamura, Mechanism of fiber formation of silkworm, In: D. Kaplan, W W Adams, B. Farmer, C. Viney, eds. Silk Polymers (American Chemical Society, Washington DC, 1994), pp. 292–310.

    Google Scholar 

  60. E. Iizuka, The physico-chemical properties of silk fibers and the fiber spinning process, Experientia 39, 449 (1983).

    Article  Google Scholar 

  61. F. Vollrath, and D. P. Knight, Liquid crystalline spinning of spider silk, Nature 410, 541 (2001).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dechang Li  (李德昌).

Additional information

This work was supported by the National Natural Science Foundation of China (Grants Nos. 12122212, 11932017, 11772054, and 11772055).

Supporting Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, W., Tang, Z., Wu, X. et al. An atomistic model of silk protein network for studying the effect of pre-stretching on the mechanical performances of silks. Acta Mech. Sin. 38, 222013 (2022). https://doi.org/10.1007/s10409-022-22013-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10409-022-22013-x

Keywords

Navigation