Computational design of novel proteins to bind unnatural cofactors

Project Description for Advert (max 500 words). This will be the text that is advertised to prospective students:

De novo protein design is quickly becoming a viable strategy for creating novel protein structures, although adding functionality to these structures remains challenging [1]. While there has been some success in designing novel enzymes, their enzymatic activity falls short of most natural systems, making it necessary to perform directed evolution to improve activity [2]. In nature, cofactors are often incorporated into proteins to add a vast array of functionality, and so they offer an attractive route to creating highly active enzymes. There are > 70,000 experimentally determined structures of enzymes, and of these around 12,000 contain cofactors [3]. This dataset contains a huge amount of information that could be utilised to design proteins that bind novel cofactors.

The aim of this project is to apply deep learning-based tools, which are being developed in the Wood lab, to design novel proteins that can bind small-molecule catalysts as unnatural cofactors. The molecules that we are targeting are highly active, biocompatible catalysts, and by binding them to a protein, we aim to tune substrate specificity, as well as enantioselectivity of the products. The design strategy deploys deep learning to identify regions of the small molecule that are chemically similar to natural cofactors, and exploits known structural information for proteins that bind cofactors to guide the design process. Large numbers of designs will be generated and screened computationally, before the most promising candidates are taken forward for experimental validation at scale, taking advantage of the robotics facilities available through the Edinburgh Genome Foundry. Designs will be screened for cofactor binding before a subset are taken forward for detailed catalytic, biophysical and structural analysis. Information generated during this process will be incorporated into subsequent rounds of design, creating an engineering "design, build, test" cycle.

Throughout this project, the student will receive training in experimental automation, molecular biology, biophysical techniques, and structural characterisation. Furthermore, the Wallace lab will support training in biochemical characterisation of the designed enzymes. While the major focus of this project is experimental, there will be ample opportunity for the student to develop computational skills in machine learning and atomistic simulation of proteins. No prior knowledge of computational techniques is required, as the student will be well supported in developing these skills, but experience of basic programming with the Python programming language would be advantageous.