The rapid growth of human population commands to increase crop yields by 50-70 % by 2050 in order to feed the predicted 9-10 billion people. Extra food and biofuel production has to be achieved using the shrinking supply of arable land making it a key global challenge that requires ground-breaking innovations and "thinking outside the box". One of the innovative solutions for future prosperity of humankind is improved photosynthesis. All food production is based on photosynthesis either directly when growing crops or indirectly when plants used to feed livestock. However, despite being the most important biological process on the planet, photosynthesis is surprisingly inefficient with only 5% of sun energy received by plants converted into biochemical energy of sugars.
One of the bottle necks of photosynthesis which curbs crop productivity is the CO2 fixation catalysed by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). Recently our lab showed how crop performance might be improved if crop enzymes would be substituted by Rubiscos from other species [1]. Possibility of chaperone assisted Rubisco transplantation between plant species was demonstrated by us earlier [2].
Machine learning algorithms such as neural networks became a part of our daily lives and are integral part of the success of the leading IT companies from Alphabet and Amazon, Alibaba and Baidu, to Tencent and Yandex. The AI program AlphaFold developed by Google's DeepMind made it into the world news in November 2020 with its predictions of protein structures. This project will use algorithms developed by AlphaFold and in academia to predict Rubisco properties from sequences.
The specific objectives of the project are:
1) Use machine learning trained on available Rubisco sequence and catalytic data to (i) predict Rubisco kinetics from amino acid sequences; (ii) find superior Rubiscos already existing in nature; (iii) find amino acid substitutions that might improve Rubisco catalytic properties in a range of key crops.
2) Assemble potential superior Rubiscos and modified crop Rubiscos with improved catalysis in E. coli using recently developed system [3].
3) Perform catalytic assays of recombinant Rubiscos. The knowledge obtained can then be used to engineer crops with improved photosynthesis/yield.
The student will be trained in both coding and wet lab skills. The prospective candidates could be both molecular biologists willing to learn machine learning or computer scientists willing to learn synthetic biology.
[1] Journal Experimental Botany (2021) 72:6066–6075; [2] PNAS (2015) 112:3564-3569; [3] Science (2017) 358:1272-1278.
Informal enquiries may be made to [Email Address Removed].
HOW TO APPLY
Applications should be made by emailing [Email Address Removed] with:
· a CV (including contact details of at least two academic (or other relevant) referees);
· a covering letter – clearly stating your first choice project, and optionally 2nd ranked project, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University;
· copies of your relevant undergraduate degree transcripts and certificates;
· a copy of your passport (photo page).
A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE AT https://www.nld-dtp.org.uk/how-apply. Applications not meeting these criteria may be rejected.
In addition to the above items, please email a completed copy of the Additional Details Form (as a Word document) to [Email Address Removed]. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.
The closing date for applications is 10th January 2022 at 5.00pm (UK time).
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