ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences

Abstract

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 T_m 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 T_m remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the T_m values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding T_m 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 T_m values.

Keywords: Collagen; Deep learning; Long short-term memory artificial recurrent neural network; Machine learning; Melting temperature.

Fig. 1.. Distribution of data from literature, based on experimental results.

a) Overview of the problem studied here, to predict the melting point Tm from the sequence of collagen molecules. b) Experimental melting temperatures collected. c) Normalized T_m value distribution. Thermal stability data sets for observed Tm values for Gly-X-Y tripeptide units in triple helical collagen-like peptides are integrated here to produce an algorithm for predicting global melting temperatures. Data from ^,,-,,-,-.

Fig. 2.. Overview of machine learning model.

a) We design a deep learning network to discover hidden features of collagen sequences by introducing embedding layer. b) The structure of our deep learning model starts at an embedding layer, followed by two 1D convolution layers, then we flatten all the features and send them into a fully connected layer for regression to determine T_m value. Figures S1 and S2 provide details of the neural network model.

Fig. 3.. Predictive accuracy of ColGen, and training performance.

a) Data comparing training with test set demonstrates a 95% confidence interval. Plotting R² of training / testing / generation. b) Training and validation error over epochs demonstrate a well fit model. The validation and training errors reach a plateau around 150 epochs.

Fig. 4.. Characterization of the effects of various types of mutations, predicted by ColGen.

a) T_m values of mutations in G, P, or O position demonstrates that mutations in the middle of the sequence are the most destabilizing for Tm values. Mutations in the G position are the most destabilizing to the peptide. Error bars indicate standard deviation of all amino acids that were mutated. b) Thermal stability as a function of collagen sequence length, where length is number of repeat units (GOP) demonstrates that there is a critical length at which the Tm can no longer be increased significantly. This critical length is consistent with other studies.

Figure 5.. Characterization of effect of disorder on T_m, as predicted by the model.

A) T_m values of disorder arranged by G, P, or O position confirm that increasing mutations along the chain decreases thermal stability of the triple helix. Error bars indicate standard deviation of all amino acids that were mutated. b) T_m values of disorder in the O position demonstrates that initial mutations to polar, positive charged, and negative charged amino acids confer the same degree of stability in the molecule. However, upon increasing mutations, polar amino acids are the least destabilizing to the triple helix, suggesting that they should be used for bacterial expression of collagen where expression of O is not possible.