Biodesign and engineering of functionalized spider silk variants

蜘蛛丝是一种高性能生物材料，其应用范围从个人防护服到医疗植入物的表面涂层和神经元再生指南。从蜘蛛身上收获丝是乏味的，并且在经济上不可行。因此，在广泛的宿主生物体（从细菌到山羊）中异源生产蚕丝一直是合成生物学研究的焦点。在这个项目中，我们打算建立在这些进展，以创建库的功能化蜘蛛丝衍生物的各种application.Design：该项目的第一部分将利用最近可用的蜘蛛基因组和转录组序列来表征天然蜘蛛丝序列图案的全谱。构建：该项目的第二部分将测试推断出的设计模式，使用DNA合成和自动DNA组装管道来创建用于表达和表征的多种丝蛋白文库。我们将使用大肠杆菌，以及生物技术上有前途的替代宿主物种，生产和分离工程丝蛋白。测试：为了促进这些文库的高通量测试，该项目的第三部分将集中于为表达的丝开发合适的标签，这将允许使用生物物理和生物化学的集合快速测定表达水平和与所得纺制材料的关键性质的相关性，了解：该项目的第四部分使用机器学习方法来预测改进的序列设计，这些设计将在设计周期的几个迭代中进行测试。该项目提供了全面的跨学科培训的方法开创了监督团队，在生物学和工程的接口，为下一代生物技术科学家必不可少的一系列关键学科。

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.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 sequence motifs. 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 host species, to produce and isolate the engineered silk proteins.Test: To facilitate the high-throughput 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 properties of the resulting spun material using a collection of biophysical and materials sciences 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.