Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation

Collagen is the most abundant structural protein in humans, providing crucial mechan-ical properties, including high strength and toughness, in tissues. Collagen-based bio-materials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, mea-sured as melting temperature (T-m). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to Tm values, a robust framework to facilitate the design of collagen sequences with specific Tm remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific Tm values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their T-m values using both experimen-tal and computational methods. We find that the model accurately predicts T-m values within a few degrees centigrade. Further, using this model, we conduct a high -throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific Tm values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

Automated summary

A general model using deep learning and genetic algorithm was developed to design collagen sequences with specific melting temperatures (Tm). Experimental and computational methods were used to verify the accuracy of the model in predicting Tm values. The study also identified the most frequently occurring collagen triplets and their correlation with triple-helical quality. This research is critical for the development of collagen sequences with specific Tm values for materials manufacturing and biomedical applications.