期刊
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
卷 125, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jmbbm.2021.104921
关键词
Collagen; Machine learning; Melting temperature; Deep learning; Long short-term memory artificial recurrent; neural network
资金
- IBM-MIT Watson AI Lab, ARO [W911NF-17-1-0384]
- NOR [NIH P41EB027062, U01 EB014976]
- ONR [N000141612333, N000142012189, N000141912375]
- Ministry of Science and Technology in Taiwan [MOST 109-2222-E-006-005-MY2, MOST 110-2224-E-007-003]
- NSF GRFP
- U.S. Department of Defense (DOD) [N000142012189] Funding Source: U.S. Department of Defense (DOD)
Collagen is a crucial structural protein in human tissues, commonly used for repairs and regeneration, but designing specific collagen sequences remains a challenge. Research shows that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths have the greatest impact on the stability of collagen structures.
Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical properties with high strength and toughness. Due to this ubiquitous role in human tissues, collagen-based biomaterials are commonly used for tissue repairs and regeneration, requiring chemical and thermal stability over a range of temperatures during materials preparation ex vivo and subsequent utility in vivo. Collagen unfolds from a triple helix to a random coil structure during a temperature interval in which the midpoint or Tm is used as a measure to evaluate the thermal stability of the molecules. However, finding a robust framework to facilitate the design of a specific collagen sequence to yield a specific Tm remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the Tm values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding Tm values. By using this framework, we are able to quickly evaluate how mutations and order in the primary sequence affect the stability of collagen triple helices. Specifically, we confirm that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths cause the greatest drop in Tm values.
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