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Details of Grant 

EPSRC Reference: EP/S003002/1
Title: Development and experimental validation of a deep-learning based pipeline for user-centric protein design.
Principal Investigator: Wood, Dr CW
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Sch of Biological Sciences
Organisation: University of Edinburgh
Scheme: EPSRC Fellowship
Starts: 01 November 2018 Ends: 31 October 2021 Value (£): 304,056
EPSRC Research Topic Classifications:
Synthetic biology
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Sep 2018 Eng Fellowship Interviews Sept 2018 Announced
13 Jun 2018 Engineering Prioritisation Panel Meeting 13 and 14 June 2018 Announced
Summary on Grant Application Form
Proteins are the molecules that provide most of the complex functionality in all living things. They are made of 20 different building-block types called amino acids, which are combined in different sequences to make long chains. The varying shapes and chemistries of the amino acids cause the chains to fold into a distinct 3D structure. It is this structure that enables proteins to perform the different roles they have in nature, whether it's digesting your food, moving you around or simply keeping the top of your head warm.

Even though life emerged over 4 billion years ago, only a small number of possible protein structures have been explored by evolution due to their inherent complexity. As protein structure is directly related to function, this means that there is a huge pool of unexplored proteins with functions that could be applied to solve problems in medicine, biotechnology, energy and agriculture. If we can design new proteins from scratch, we can address some of these problems with the new proteins that we create.

As mentioned previously, proteins are complex, and so it is difficult to design new proteins, but to make it easier we can write programs that can create and test huge numbers of designs in computer simulations. This improves the chance of designing a sequence of amino acids that will adopt our desired structure when we create it in the laboratory. However, even with state-of-the-art methods for designing proteins on a computer, only a small number of sequences adopt the structures we intend them to, making protein design costly and unreliable.

I intend to create a new method for designing proteins that uses a type of artificial intelligence called a deep-neural network (see http://playground.tensorflow.org for an interactive example). This technique will be used to learn the complex rules for generating stable proteins that are hidden inside the amino-acid sequences of protein structures we have already observed. Once the rules have been learned, we can use them to create new sequences of amino acids that are good candidates for adopting the structure we require. This method will form part of an automated pipeline that will create and test protein structures in computer simulations, before recommending the best designs for our intended application. This will make the process of protein design much more reliable.

To get an understanding of how effective this method is, I will test it by creating hundreds of the protein designs recommended by the pipeline in the laboratory, using robotics to accelerate this process. Once tested, I plan to showcase the method by designing new proteins that can perform chemical reactions that are useful industrially. This will make performing these chemical reactions much cheaper and more environmentally friendly, paving the way for the design of many more proteins with useful functions that address the challenges that the human race currently faces.

Key Findings
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