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 mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials 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, measured as melting temperature (T_m). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to T_m values, a robust framework to facilitate the design of collagen sequences with specific T_m remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific T_m 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 experimental 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 T_m values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.