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Can I read Discovering Design Principles of Collagen Molecular Stability Using a Genetic Algorithm, Deep Learning, and Experimental Validation on EtoBox?
Discovering Design Principles of Collagen Molecular Stability Using a Genetic Algorithm, Deep Learning, and Experimental Validation by Eesha Khare; Chi-Hua Yu; Constancio Gonzalez Obeso; Mario Milazzo; David L. Kaplan; Markus J. Buehler is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Discovering Design Principles of Collagen Molecular Stability Using a Genetic Algorithm, Deep Learning, and Experimental Validation about?
## Significance Collagen is the most abundant structural protein in humans and as such, is often used in biomedical applications for tissue repair and regeneration. Designing de novo collagen to maintain its structural integrity in vivo, important for its mechanical performance and subsequent utility, remains a challenge today. In this work, we develop a deep learning framework to generate collagen sequences with desired thermal stability and validate our deep learning framework using both simulation and experiment. Given this validation, we discover key insights into the prevalence of amino acids in collagen triple helices and find a mechanistic relationship between our simulations and experiment. This framework enables researchers to develop collagen sequences with desired thermal stability for biomedical applications.
Who reads Discovering Design Principles of Collagen Molecular Stability Using a Genetic Algorithm, Deep Learning, and Experimental Validation?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Eesha Khare; Chi-Hua Yu; Constancio Gonzalez Obeso; Mario Milazzo; David L. Kaplan; Markus J. Buehler
- Publisher
- Proceedings of the National Academy of Sciences
- Published
- 2022
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)