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Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning
Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unve...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969283/ https://www.ncbi.nlm.nih.gov/pubmed/36860343 http://dx.doi.org/10.1016/j.csbj.2023.02.017 |
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author | Hui, Wei-Han Chiu, Pei-Hsin Ng, Ian-Ian Chang, Shu-Wei Chou, Chia-Ching Chen, Hsiang-Ho |
author_facet | Hui, Wei-Han Chiu, Pei-Hsin Ng, Ian-Ian Chang, Shu-Wei Chou, Chia-Ching Chen, Hsiang-Ho |
author_sort | Hui, Wei-Han |
collection | PubMed |
description | Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unveil the molecular mechanism of collagen flexibility upon slight heating and to develop a model to predict the strain of collagen sequences. We used molecular dynamics approaches to simulate the molecular structures and mechanical behavior of the gap and overlap regions in type I collagen at 307 K, 310 K, and 313 K. The results showed that the molecular model in the overlap region is more sensitive to temperature increases. Upon increasing the temperature by 3 degrees Celsius, the end-to-end distance and Young’s modulus of the overlap region decreased by 5% and 29.4%, respectively. The overlap region became more flexible than the gap region at higher temperatures. GAP-GPA and GNK-GSK triplets are critical for providing molecular flexibility upon heating. A machine learning model developed from the molecular dynamics simulation results showed good performance in predicting the strain of collagen sequences at a physiological warmup temperature. The strain-predictive model could be applied to future collagen designs to obtain desirable temperature-dependent mechanical properties. |
format | Online Article Text |
id | pubmed-9969283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-99692832023-02-28 Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning Hui, Wei-Han Chiu, Pei-Hsin Ng, Ian-Ian Chang, Shu-Wei Chou, Chia-Ching Chen, Hsiang-Ho Comput Struct Biotechnol J Research Article Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unveil the molecular mechanism of collagen flexibility upon slight heating and to develop a model to predict the strain of collagen sequences. We used molecular dynamics approaches to simulate the molecular structures and mechanical behavior of the gap and overlap regions in type I collagen at 307 K, 310 K, and 313 K. The results showed that the molecular model in the overlap region is more sensitive to temperature increases. Upon increasing the temperature by 3 degrees Celsius, the end-to-end distance and Young’s modulus of the overlap region decreased by 5% and 29.4%, respectively. The overlap region became more flexible than the gap region at higher temperatures. GAP-GPA and GNK-GSK triplets are critical for providing molecular flexibility upon heating. A machine learning model developed from the molecular dynamics simulation results showed good performance in predicting the strain of collagen sequences at a physiological warmup temperature. The strain-predictive model could be applied to future collagen designs to obtain desirable temperature-dependent mechanical properties. Research Network of Computational and Structural Biotechnology 2023-02-10 /pmc/articles/PMC9969283/ /pubmed/36860343 http://dx.doi.org/10.1016/j.csbj.2023.02.017 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Hui, Wei-Han Chiu, Pei-Hsin Ng, Ian-Ian Chang, Shu-Wei Chou, Chia-Ching Chen, Hsiang-Ho Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title | Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title_full | Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title_fullStr | Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title_full_unstemmed | Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title_short | Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
title_sort | unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969283/ https://www.ncbi.nlm.nih.gov/pubmed/36860343 http://dx.doi.org/10.1016/j.csbj.2023.02.017 |
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