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

Identification of a human type XVII collagen fragment with high capacity for maintaining skin health

Collagen XVII (COL17) is a transmembrane protein that mediates skin homeostasis. Due to expression of full length collagen was hard to achieve in microorganisms, arising the needs for selection of collagen fragments with desired functions for microbial biosynthesis. Here, COL17 fragments (27-33 amino acids) were extracted and replicated 16 times for recombinant expression in Escherichia coli. Five variants were soluble expressed, with the highest yield of 223 mg/L. The fusion tag was removed for biochemical and biophysical characterization. Circular dichroism results suggested one variant (sample-1707) with a triple-helix structure at >37 °C. Sample-1707 can assemble into nanofiber (width, 5.6 nm) and form hydrogel at 3 mg/mL. Sample-1707 was shown to induce blood clotting and promote osteoblast differentiation. Furthermore, sample-1707 exhibited high capacity to induce mouse hair follicle stem cells differentiation and osteoblast migration, demonstrating a high capacity to induce skin cell regeneration and promote wound healing. A strong hydrogel was prepared from a chitosan and sample-1707 complex with a swelling rate of >30 % higher than simply using chitosan. Fed-batch fermentation of sample-1707 with a 5-L bioreactor obtained a yield of 600 mg/L. These results support the large-scale production of sample-1707 as a biomaterial for use in the skin care industry.

Elucidating the role of water in collagen self-assembly by isotopically modulating collagen hydration

Water is known to play an important role in collagen self-assembly, but it is still largely unclear how water-collagen interactions influence the assembly process and determine the fibril network properties. Here, we use the H[Formula: see text]O/D[Formula: see text]O isotope effect on the hydrogen-bond strength in water to investigate the role of hydration in collagen self-assembly. We dissolve collagen in H[Formula: see text]O and D[Formula: see text]O and compare the growth kinetics and the structure of the collagen assemblies formed in these water isotopomers. Surprisingly, collagen assembly occurs ten times faster in D[Formula: see text]O than in H[Formula: see text]O, and collagen in D[Formula: see text]O self-assembles into much thinner fibrils, that form a more inhomogeneous and softer network, with a fourfold reduction in elastic modulus when compared to H[Formula: see text]O. Combining spectroscopic measurements with atomistic simulations, we show that collagen in D[Formula: see text]O is less hydrated than in H[Formula: see text]O. This partial dehydration lowers the enthalpic penalty for water removal and reorganization at the collagen-water interface, increasing the self-assembly rate and the number of nucleation centers, leading to thinner fibrils and a softer network. Coarse-grained simulations show that the acceleration in the initial nucleation rate can be reproduced by the enhancement of electrostatic interactions. These results show that water acts as a mediator between collagen monomers, by modulating their interactions so as to optimize the assembly process and, thus, the final network properties. We believe that isotopically modulating the hydration of proteins can be a valuable method to investigate the role of water in protein structural dynamics and protein self-assembly.

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

Harnessing the power of a photoinitiated thiol-ene "click" reaction for the efficient synthesis of S-lipidated collagen model peptide amphiphiles

A photoinitiated thiol-ene "click" reaction was used to synthesize S-lipidated collagen model peptide amphiphiles. Use of 2-iminothiolane provided an epimerization-free thiol handle required for thiol-ene based incorporation of lipid moieties onto collagen-based peptide sequences. This approach not only led to improvements in the triple helical characteristics of the resulting collagen model peptides but also increased the aqueous solubility of the peptide amphiphiles. As a result, this methodology holds significant potential for the design and advancement of functional peptide amphiphiles, offering enhanced capabilities across a wide range of applications.

Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.

Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Generative design of de novo proteins based on secondary structure constraints using an attention-based diffusion model

We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity as they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in conditional generative design of various biological or engineering systems. Future work may include additional conditioning, and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.

Stability of collagen heterotrimer with same charge pattern and different charged residue identities

Salt bridges are important factors in maintaining the stability of proteins, and their contribution to protein folding has received much attention. Although the interaction energies, or stabilizing contributions, of individual salt bridges have been measured in various proteins, a systematic assessment of various types of salt bridges in a relatively uniform environment is still a valuable analysis. Here, we used a collagen heterotrimer as a host-guest platform to construct 48 heterotrimers with the same charge pattern. A variety of salt bridges were formed between the oppositely charged residues Lys, Arg, Asp, and Glu. The melting temperature (Tm) of the heterotrimers was measured with circular dichroism. The atomic structures of 10 salt bridges were shown in three x-ray crystals of heterotrimer. Molecular dynamics simulation based on the crystal structures indicated that strong, intermediate, and weak salt bridges have distinctive N-O distances. A linear regression model was used to predict the stability of heterotrimers with high accuracy (R2 = 0.93). We developed an online database to help readers understand how a salt bridge stabilizes collagen. This work will help us better understand the stabilizing mechanism of salt bridges in collagen folding and provide a new strategy to design collagen heterotrimers.

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

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

Unraveling the molecular mechanism of collagen flexibility during physiological warmup using molecular dynamics simulation and machine learning

Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unveil the molecular mechanism of collagen flexibility upon slight heating and to develop a model to predict the strain of collagen sequences. We used molecular dynamics approaches to simulate the molecular structures and mechanical behavior of the gap and overlap regions in type I collagen at 307 K, 310 K, and 313 K. The results showed that the molecular model in the overlap region is more sensitive to temperature increases. Upon increasing the temperature by 3 degrees Celsius, the end-to-end distance and Young's modulus of the overlap region decreased by 5% and 29.4%, respectively. The overlap region became more flexible than the gap region at higher temperatures. GAP-GPA and GNK-GSK triplets are critical for providing molecular flexibility upon heating. A machine learning model developed from the molecular dynamics simulation results showed good performance in predicting the strain of collagen sequences at a physiological warmup temperature. The strain-predictive model could be applied to future collagen designs to obtain desirable temperature-dependent mechanical properties.

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

Collagen model peptides (CMPs) consisting of proline-(2S,4R)-hydroxyproline-glycine (POG) repeats have provided a breadth of knowledge of the triple helical structure of collagen, the most abundant protein in mammals. Predictive tools for triple helix stability have, however, lagged behind since the effect of CMPs with different frames ([POG]_n , [OGP]_n , or [GPO]_n ) and capped or uncapped termini have so far been underestimated. Here, we elucidated the impact of the frame, terminal functional group and its charge on the stability of collagen triple helices. Combined experimental and theoretical studies with frame-shifted, capped and uncapped CMPs revealed that electrostatic interactions, strand preorganization, interstrand H-bonding, and steric repulsion at the termini contribute to triple helix stability. We show that these individual contributions are additive and allow for the prediction of the melting temperatures of CMP trimers.