Periodic Reporting for period 1 - SUPERB (StructUral ProtEins foR Biomedical materials)
Reporting period: 2020-05-01 to 2022-04-30
Aging of an increasing global population (with life expectancy predicted to reach 106 years for those born in the EU after 2030) is a serious challenge for public health systems, bringing along a rise in diseases caused by compromised tissues. To that end, the healthcare community is eagerly searching for new, smarter and more sustainable materials to treat those diseases. Developing such materials would contribute to a better health condition for citizens, while reducing the costs of healthcare (expected to rise to double-digit figures in the EU by 2050). In that regard, protein polymers are an interesting source of advanced medical materials, due to their natural abundance, sustainability, easy processability, cytocompatibility, tuneable degradation, bioresorbability, and controllable mechanical properties. These are all interesting features for materials for medical applications. The main hypothesis of the SUPERB (StructUral ProtEins foR Biomedical materials) project is that the discovery of new protein-based materials can be greatly accelerated by the use of computational modelling. The objective is to simulate and manufacture de novo fusion proteins for the synthesis of medical materials that incorporate, heal and regenerate in the body faster and better than current materials, like meshes based on polypropylene, polytetrafluoroethylene and polyethylene tereoxphthalate. These are aspects of great social and commercial relevance.
The research performed in this project sits at a crossover between computational modelling, bioprocess engineering and materials science. We first designed fusion proteins computationally through the modular assembly of simplified building block motifs from structural proteins like elastin or silk. These proteins were studied via molecular dynamics simulation to uncover how polymer building blocks (i.e. hydrophilic vs hydrophobic, charged vs uncharged, ordered vs disordered) and their distribution along the polymer chain affected the ability of these proteins to form macroscopic viscoelastic materials (Objective 1). Optimal protein sequences identified via simulations were then biosynthesised through fermentative processes (Objective 2), investigating processing routes to maximise their recovery from the fermentation broth. Finally, the purified proteins will be used to manufacture macroscopic materials (e.g. hydrogels) for biomedical applications (Objective 3). These materials were tested and characterised to validate the predictive power of the computational models.
The research performed in this project sits at a crossover between computational modelling, bioprocess engineering and materials science. We first designed fusion proteins computationally through the modular assembly of simplified building block motifs from structural proteins like elastin or silk. These proteins were studied via molecular dynamics simulation to uncover how polymer building blocks (i.e. hydrophilic vs hydrophobic, charged vs uncharged, ordered vs disordered) and their distribution along the polymer chain affected the ability of these proteins to form macroscopic viscoelastic materials (Objective 1). Optimal protein sequences identified via simulations were then biosynthesised through fermentative processes (Objective 2), investigating processing routes to maximise their recovery from the fermentation broth. Finally, the purified proteins will be used to manufacture macroscopic materials (e.g. hydrogels) for biomedical applications (Objective 3). These materials were tested and characterised to validate the predictive power of the computational models.
In order to meet the objectives, the project was structured into four work packages (WPs):
WP 1: Computational modelling
WP 2: Fermentative production of fusion proteins
WP 3: Materials manufacture, characterisation, and testing
WP 4: Project management and communication
The project was successfully completed, and the results exceeded the initial expectations. Computational and experimental data showed an excellent agreement and confirmed the principal hypothesis of the project: that the discovery of new protein-based materials for biomedical applications can be greatly accelerated by computational modelling. Computational and experimental tools were leveraged to investigate the influence of block selection and sequence organisation on the temperature-responsive, mechanical, and microstructural properties of protein-based biomedical materials, specifically hydrogels. New protein biopolymers were simulated via molecular dynamic (MD) simulations, biosynthesized using microbial fermentations, and macroscopic materials were produced. The main outcome of this research was a computational framework and a set of heuristic rules to predict the properties of new protein-based healthcare materials before synthesizing them in the lab. To understand the impact of this work, the framework was able to predict in 2 weeks of computational work the same amount of data that required more than 8 months to be generated in the lab.
The SUPERB project developed sequence heuristics and computational models for a fast and reliable a priori prediction of the phase behaviour of new protein biopolymers. The computational model specifically focused on predicting the temperature-responsiveness of silk-like and elastin-like proteins. The method takes as input the dataset of molecular properties obtained via MD simulations of protein biopolymers. Experimentally, these materials had mechanical properties in the range of 1-55 kPa. The hydrogels displayed a uniform network structure with pores in the 10-nm range, with little dependence on the biopolymer sequence. The outcome of this project connected genetically encoded information (protein molecular weight, sequence, or hydrophobicity/hydrophilicity) with the stimuli-responsive, viscoelastic, and microstructural properties of biomedical materials. This will allow us to explore a larger sequence space at a faster pace. Furthermore, carrying this project at an industrial company allow for testing of industrial-relevant pilot scale conditions. The targets of titre (>1 g/L) and purity (>99%) were achieved for multiple protein biopolymers developed in this project.
WP 1: Computational modelling
WP 2: Fermentative production of fusion proteins
WP 3: Materials manufacture, characterisation, and testing
WP 4: Project management and communication
The project was successfully completed, and the results exceeded the initial expectations. Computational and experimental data showed an excellent agreement and confirmed the principal hypothesis of the project: that the discovery of new protein-based materials for biomedical applications can be greatly accelerated by computational modelling. Computational and experimental tools were leveraged to investigate the influence of block selection and sequence organisation on the temperature-responsive, mechanical, and microstructural properties of protein-based biomedical materials, specifically hydrogels. New protein biopolymers were simulated via molecular dynamic (MD) simulations, biosynthesized using microbial fermentations, and macroscopic materials were produced. The main outcome of this research was a computational framework and a set of heuristic rules to predict the properties of new protein-based healthcare materials before synthesizing them in the lab. To understand the impact of this work, the framework was able to predict in 2 weeks of computational work the same amount of data that required more than 8 months to be generated in the lab.
The SUPERB project developed sequence heuristics and computational models for a fast and reliable a priori prediction of the phase behaviour of new protein biopolymers. The computational model specifically focused on predicting the temperature-responsiveness of silk-like and elastin-like proteins. The method takes as input the dataset of molecular properties obtained via MD simulations of protein biopolymers. Experimentally, these materials had mechanical properties in the range of 1-55 kPa. The hydrogels displayed a uniform network structure with pores in the 10-nm range, with little dependence on the biopolymer sequence. The outcome of this project connected genetically encoded information (protein molecular weight, sequence, or hydrophobicity/hydrophilicity) with the stimuli-responsive, viscoelastic, and microstructural properties of biomedical materials. This will allow us to explore a larger sequence space at a faster pace. Furthermore, carrying this project at an industrial company allow for testing of industrial-relevant pilot scale conditions. The targets of titre (>1 g/L) and purity (>99%) were achieved for multiple protein biopolymers developed in this project.
There is great interest in uncovering the heuristics and design rules that link biosynthesised protein biopolymer sequence with the mechanical properties of the materials derived thereof. This project investigated these questions. Molecular dynamics simulations were used as predictive tool to investigate a priori the ability of new protein biopolymer to form viscoelastic materials. The model linking computational simulations with experimentally observed phase behavior of these proteins is now under review, and can be found in an open repository. This will result not only in a better understanding at the fundamental (atomistic) level of the properties of this biosynthesized protein biopolymers, but also allow to accelerate the development of new protein-based materials for medical applications (e.g. tissue regeneration scaffold, or drug delivery carriers). From the biosynthesis and materials science standpoints, the activities performed in the SUPERB project allowed to develop new purification routes and synthesis protocols to develop protein-based hydrogels with tuneable mechanical properties and with controlled microenvironments for tissue regeneration applications. The healthcare research community is eagerly investigating protein-based materials that incorporate, heal and regenerate in the body faster and better than current materials, in order to treat diseases caused by compromised tissues. The results of the SUPERB project can accelerate the development of such materials.
During the fellowship and while under Covid-19 restrictions, the researcher engaged in multiple outreach and dissemination activities. He presented his work at two international scientific conferences. He also gave seminars at six research institutions. He also engaged in school talks, participated in two science festivals, prepared several outreach articles, and prepared a webpage with a tutorial video and codes for computational simulations, as well as updates on the progress of the project.
During the fellowship and while under Covid-19 restrictions, the researcher engaged in multiple outreach and dissemination activities. He presented his work at two international scientific conferences. He also gave seminars at six research institutions. He also engaged in school talks, participated in two science festivals, prepared several outreach articles, and prepared a webpage with a tutorial video and codes for computational simulations, as well as updates on the progress of the project.