Predicting mechanical properties of silk from its amino acid sequences via machine learning.

Yoonjung Kim, Taeyoung Yoon, Woo B Park, Sungsoo Na

Journal of the mechanical behavior of biomedical materials 2023 Apr

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The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences. Copyright © 2023 Elsevier Ltd. All rights reserved.

Citation

Yoonjung Kim, Taeyoung Yoon, Woo B Park, Sungsoo Na. Predicting mechanical properties of silk from its amino acid sequences via machine learning. Journal of the mechanical behavior of biomedical materials. 2023 Apr;140:105739