Plastic waste is made from petroleum, but researchers are now engineering new proteins—like those found in silk or elastin—to build biodegradable replacements from scratch. The problem is twofold: petroleum-based plastics generate enormous waste (500 million tons could enter the ocean by 2040), and natural proteins harvested from animals are inconsistent, contaminated, or culturally problematic. Recombinant DNA technology can now produce animal-free structural proteins, but scientists cannot predict how fusing different protein building blocks will affect the final material’s strength or flexibility. This forces slow, trial-and-error experimentation. This project will combine molecular dynamics simulations with experimental biomanufacturing to create a predictive toolbox. It will link a protein’s genetic sequence directly to its mechanical and structural properties, accelerating the design of new sustainable polymers. If successful, the work could replace fossil-derived plastics in packaging, textiles, or medical implants with materials that are lightweight, biodegradable, and tunable—without relying on animal sources. The research sits at the intersection of computation, biotechnology, and materials engineering, and is primarily fundamental science aimed at building predictive models rather than delivering a finished product.
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Most everyday plastic materials are made of polymers produced from petroleum. The growing global market for polymers (sales turnover of over £25 billion in the UK in 2022) also generates a vast amount of waste: predictions are that 500 million tons of plastic residues will be dumped in the ocean between 2016 and 2040 if no mitigation strategies are implemented. This challenges our ability to meet the demands for polymeric materials while minimising waste production. There is therefore an urgent unmet need for new circular and sustainable polymers that fulfil the current role of plastics, while allowing the UK to meet the targets set in the Net Zero strategy. To that end, Nature is a great source of inspiration: it uses biopolymers to self-assemble structural materials for a wide range of mechanical and structural requirements. Specifically, structural proteins (e.g., elastin, collagen, keratin, or silk) are a very appealing class of biopolymers due to their sustainability, lightweight, stimuli responsiveness, easy processability, degradability, and tuneable structural or mechanical properties. Structural proteins are normally harvested from animal sources, but these sources suffer from batch-to-batch variability, presence of contaminants, and cultural or religious concerns that limit their commercial viability. Fortunately, recent developments in engineering biology (e.g., in recombinant DNA technology or bioprocess engineering) allow us to overcome these issues and biomanufacture non-animal-derived structural proteins. Using recombinant DNA technology also means that we are no longer constrained to working only with structural proteins selected for by evolution. We can instead engineer new recombinant structural proteins that merge building blocks from multiple natural proteins into a single biopolymer chain. This fusion approach is very powerful, because it allows us to design new smart materials that simultaneously display functions from dissimilar natural proteins. However, the collective behaviour of different building blocks cannot be inferred from the properties of individual ordered/disordered, hydrophobic/hydrophilic, charged/uncharged, or structural/functional blocks. This makes it difficult to predict how new protein sequences will define the properties of the materials produced from them. Thus, to date protein-based materials are mainly researched using low-throughput trial-and-error experimentation, which impedes rapid development and prototyping. It would be desirable to have predictive tools that speed up the exploration of the vast design space of new recombinant proteins, by connecting new protein sequences with material mechanical or structural properties. Building on own feasibility results, we will apply a combined experimental-computational approach to develop such predictive tools. We will focus on biopolymers inspired by natural structural proteins that fuse various structural and functional building blocks to achieve a range of mechanical/structural properties. These proteins will contain elastin-like or resilin-like building blocks because their thermoresponsiveness enables a highly scalable and simple purification process for these proteins, compared to standard purification techniques such as affinity chromatography. At the intersection between computation, materials, biotechnology, and bioprocess engineering, the objective of this project is to deliver a predictive toolbox that uses computational modelling to accelerate design-build-test-learn cycles for protein-based biopolymers that replace fossil-derived polymers. To develop such predictive tools, we will (i) generate computational datasets by performing molecular dynamics (MD) simulations of new structural proteins; (ii) obtain experimental data by biomanufacturing those proteins and analysing the mechanical/structural properties of materials produced from them; and (iii) developing predictive models by exploring the mathematical links between the datasets obtained from MD simulations and the experimental material characterisation.
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