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

Abstract

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.

Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

“… The development of computational models for the prediction of T m values of collagen triple helices is currently an active area of research. [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ] Several of these models succeeded in capturing the influence of inner residues on triple helix stability. These algorithms, however, do not take frameshifts, terminal capping groups and their charge into account.…”

Predicting Collagen Triple Helix Stability through Additive Effects of Terminal Residues and Caps

“…Cross‐comparisons between different works have therefore been virtually impossible. Our set of parameters allows for recalculating reported T m data to a common reference peptide and, thus, enables the comparison of results from diverse sources. The development of computational models for the prediction of T m values of collagen triple helices is currently an active area of research [65–71] . Several of these models succeeded in capturing the influence of inner residues on triple helix stability.…”

“…The development of computational models for the prediction of T m values of collagen triple helices is currently an active area of research [65–71] . Several of these models succeeded in capturing the influence of inner residues on triple helix stability.…”

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

“…“Attention” mechanisms, which direct neural networks to focus on the most relevant components of input data based on context rather than sequential order, improve training efficiency and performance in RNNs. Transformer models extend attention mechanisms to “self-attention” by removing sequential recurrent processing steps altogether and processing input tokens in parallel, thus shifting training toward semisupervised and unsupervised methods. , Transformer neural networks are the current state-of-the-art in natural language processing (NLP) tasks and have recently been extended to predict chemical and physical properties of materials. − All supervised learning models are generally trained by assigning a loss function based on model performance and modifying trainable parameters through multiple iterations to minimize this loss. Artificial neural networks utilize back-propagation and gradient descent algorithms to optimally modify model parameters in each iteration.…”