Biodesign and Engineering of Functionalized Spider Silk Variants

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Spider silk is a high-performance biomaterial with exciting applications ranging from personal protective clothing to surface coatings for medical implants and guides for neuronal regeneration. Harvesting silk from spiders is tedious and economically not viable on a large scale. Consequently, the heterologous production of silk in a wide range of host organisms (from bacteria to goats) has been the focus of intense synthetic biology research. In this project, we intend to build on these advances to create libraries of functionalised spider silk derivatives for various applications.The work will include four closely interlinked work packages, which will interact through an iterative design - build - test - learn cycle in several rounds during the lifetime of the project.Design: The first part of the project will exploit recently available spider genome and transcriptome sequences to characterize the full spectrum of natural spider silk sequences. The aim is to identify design patterns of silk sequences that correlate with biophysical properties of the resulting silk.Build: The second part of the project will test the inferred design patterns, using DNA synthesis and automated DNA assembly pipelines to create diverse libraries of silk proteins, for expression and characterization. We will use Escherichia coli, as well as biotechnologically promising alternative bacterial host species, to produce and isolate the engineered silk proteins.Test: To facilitate the high-throughput materials testing of these libraries, a third part of the project will focus on the development of suitable tags for the expressed silks, which will allow rapid determination of expression levels and correlation with key biophysical properties of the resulting spun material. Spinning will be based on a range of dry- and wet-spinning approaches, and the resulting fibres will be tested using a collection of biophysical and materials science assays.Learn: The fourth part of the project uses machine learning methods to predict improved sequence designs, which will be tested in several iterations of the design cycle.This project provides comprehensive interdisciplinary training in methods pioneered by the supervisory team, across a range of key disciplines at the interface of biology and engineering, essential for the next generation of biotechnology scientists.