ArticleACS biomaterials science & engineering2017
Predicting Silk Fiber Mechanical Properties through Multiscale Simulation and Protein Design.
Article in ACS biomaterials science & engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions.Materials (Basel, Switzerland) · 2025Review
- Charting the envelope of mechanical properties of synthetic silk fibers through predictive modeling of the drawing process.Science advances · 2025Article
- Recombinant fibrous protein biomaterials meet skin tissue engineering.Frontiers in bioengineering and biotechnology · 2024Review
- Development and Characterization of Pickering Emulsion Stabilized by Walnut Protein Isolate Nanoparticles.Molecules (Basel, Switzerland) · 2023Article
- An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength.Materials (Basel, Switzerland) · 2022Article
- Engineering Natural and Recombinant Silks for Sustainable Biodevices.Frontiers in chemistry · 2022Review
- A supertough electro-tendon based on spider silk composites.Nature communications · 2020Article
- Towards engineering and production of artificial spider silk using tools of synthetic biology.Engineering biology · 2020Review
- Biomolecular Assemblies: Moving from Observation to Predictive Design.Chemical reviews · 2018Review
- Fabrication and Characterization of Recombinant Silk-Elastin-Like-Protein (SELP) Fiber.Macromolecular bioscience · 2018Article
- High-Strength, Durable All-Silk Fibroin Hydrogels with Versatile Processability toward Multifunctional Applications.Advanced functional materials · 2018Article
- Rationally designed synthetic protein hydrogels with predictable mechanical properties.Nature communications · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.