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A machine learning approach to identify carbon dioxide-binding proteins for sustainability and health

   Department of Biosciences

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  Prof M Cann, Prof A Jones, Dr Matteo Degiacomi  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The role of carbon dioxide in the history of life on Earth is unequivocal. Global carbon dioxide fixation by ancient bacteria, approximately 2½ billion years ago, generated the atmospheric oxygen that permitted the evolution of the Earth’s dizzying array of multicellular organisms. Despite the fundamental role for carbon dioxide in the development of life on Earth little is known of the molecular mechanisms by which organisms respond to fluctuating carbon dioxide.

Cells are exposed to fluctuating carbon dioxide through altered environmental conditions, changes in cell metabolism, and the effects of lifestyle and pathology. Identifying carbon dioxide-binding proteins is crucial to global strategic research challenges, including crop responses to climate change, public health, and vector-borne disease. Therefore, this proposal will illuminate the biology of this crucial yet relatively under explored molecule of central importance to all life on Earth.

Experimental techniques capable of identifying carbon dioxide-binding proteins, though effective, are expensive and laborious. In this context, computational approaches can provide crucial insights to guide experiments, and help understanding the mechanisms underpinning the interactions between proteins and carbon dioxide. This project will use machine learning and molecular modelling to reach the following goals:

1.      develop and deploy a machine learning model capable of identifying all carbon dioxide-binding sites in large protein structure ensembles,

2.      study the effect of carbon-dioxide binding to the structure of proteins of student’s choice using molecular simulation.

The project, leveraging among others on ground-breaking results recently obtained by deep learning models such as AlphaFold, will yield the first comprehensive analyses of the extent of carbon dioxide-binding sites in biology. This knowledge will transform our understanding of protein-carbon dioxide interactions and create tools that can be exploited community-wide to manage the impact of carbon dioxide on biological systems.

The ideal candidate will have a background in computational sciences. A background in biosciences, though advantageous, is not required. The student will receive training in machine learning, computational biophysics, and bioinformatics. Being embedded in an experimental group, they will also be directly exposed to cell biology, and physical chemistry techniques, and will directly interact with scientists capable of experimentally validating the predictions produced by their software. Training will be provided within the laboratories of the project supervisors at Durham and Liverpool Universities, and within key national and international workshops. The successful student will complete the Ph.D. with a broad interdisciplinary skill set valuable to both academia and industry.


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 IELTS or TOEFL English language certificate (where required);

·      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.

Informal enquiries may be made to [Email Address Removed]

The deadline for all applications is 12noon on Monday 9th January 2023. 

Funding Notes

Studentships are funded by the Biotechnology and Biological Sciences Research Council (BBSRC) for 4 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend. We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.


(2022) Allophycocyanin A is a carbon dioxide receptor in the cyanobacterial phycobilisome. Nature Communications 15.
(2021) Ubiquitin is a carbon dioxide-binding protein. Science Advances. 7: eabi5507.
(2021) MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction. Bioinformatics, 2021, btab479.
(2021) Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation. J. Proteome Res. 2022, 21, 6, 1510–1524.
(2018) The identification of carbon dioxide mediated protein post-translational modifications. Nature Communications 9:3092 | DOI: 10.1038/s41467-018-05475-z.
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