4.6 Article

An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength

期刊

MATERIALS
卷 15, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/ma15030708

关键词

spider silk; protein fibers; image analysis; structure-function relationship; prediction; mechanical properties

资金

  1. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [815357]
  2. Center for Innovative Medicine (CIMED) at Karolinska Institutet [FOR 4-1364/2019]
  3. Stockholm City Council, Karolinska Institutet SFO Regen [FOR 4-1364/2019]
  4. Olle Engkvists stiftelse [207-0375]
  5. Swedish Research Council [2019-01257]
  6. Formas [201900427]
  7. European Commission under the FET Proactive (Neurofibers) grant [732344]
  8. Italian Ministry of Education, University and Research (MIUR) under the Departments of Excellence grant [L. 232/2016]
  9. Caritro Foundation [U1277.2020/SG.1130]
  10. Olle Engkvist stiftelse [207-0375]
  11. [ARS01-01384-PROSCAN]
  12. [PRIN-20177TTP3S]

向作者/读者索取更多资源

This study introduces a new method for categorizing protein fibers, allowing for highly accurate prediction of tensile strength through the use of a light microscope and image analysis. This non-destructive quality control process provides further insights into the relationship between supramolecular organization and mechanical functionality of protein fibers.
Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers.

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