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EASTBIO Understanding prebiotic RNA self-replication using quantum chemistry and machine learning


School of Chemistry

About the Project

The supervisory team of this project merges expertise in Molecular Dynamics and Machine learning (AM), quantum chemistry simulations (RS) and experimental biochemistry (JS), which, in addition to training in scientific research and analytical methods, will ensure the acquisition of skills in several areas, machine learning, computational modelling at a classical and quantum mechanical level as well as working with experimental crystallography and beyond.

Project Details:
DNA and RNA are the two most fundamental polymers of life, which carry genetic information about the composition of proteins and promote biochemical processes through catalytic activity. A fundamental challenge is to understand how DNA and RNA originated on the Archean Earth and is one of the greatest challenges of chemistry and biology. Despite our achievements in proposing plausible reaction routes towards RNA and DNA building blocks,1 it is still unclear how the first functional and self-replicating polymers emerged on our planet. Recent advancements published by the Szostak lab, demonstrated that rate and fidelity of enzyme-free RNA self-replication can be enhanced by two noncanonical RNA monomers, namely inosine and 2-thiouridine.2 However, the exact molecular mechanism of this process remains obscure. Crystallography can provide key insights into the structural and mechanistic properties of this self-replication process, but has multiple shortcomings. (1) Any dynamical aspects of RNA self-replication cannot be resolved and (2) not all nucleic acid sequences can be readily crystallised. For instance, in contrast to RNA, arabinose and threose nucleic acids do not undergo self-replication and experiments cannot resolve the mechanistic workings behind this. Molecular simulations are a state-of-the-art method that allows atomistic resolution and can capture dynamical information, which is necessary to scrutinize the missing aspects of non-enzymatic RNA self-replication. New advances in machine learning will allow us to model the copied and templating strands accurately for various activated RNA-like monomers. You will use a mixture of simulation techniques such as molecular dynamics (MD), Density Functional Theory and hybrid machine learning and MD approaches to study the dynamics and mechanistic properties of the template copying. You will also apply cutting edge analysis methods such as, Free energy methods3 and Markov state models. The main goal of this project is to apply these mechanistic insights to guide further experimental work in the field of origins of RNA on Earth.
The project will be performed in close collaboration with the experimental group of Professor Jack Szostak (Harvard MGH), who will provide recent and relevant crystallographic data of the investigated systems. This will also allow us to directly relate the simulations to the experimental work and you will be encouraged to visit the Szostak lab at least once per year. By merging methods derived from physics and chemistry, working with structural biology data, and building a close collaboration with experimental partners we offer a highly interdisciplinary project and excellent scientific environment for the successful applicant.

Application Process:
To apply for an EASTBIO PhD studentship http://www.eastscotbiodtp.ac.uk/, follow the instructions below:
Check FindaPhD https://www.findaphd.com/phds/program/bbsrc-eastbio-doctoral-training-partnership-call-for-applications-for-2021/?p1048 for our available projects and contact potential supervisors before you apply.

After you have discussed the projects of interest to you with the project supervisors, download and complete our Equality, Diversity and Inclusion survey https://edinburgh.onlinesurveys.ac.uk/eastbio-dtp-equality-diversity-inclusion-form-2021 and then fill in the EASTBIO Application Form and submit the application form plus your academic transcripts to Dr Antonia Mey () (Links to the forms can be found here: http://www.eastscotbiodtp.ac.uk/how-apply-0)

Send the EASTBIO Reference Form to your two academic/professional referees, and ask them to submit these directly to Dr Antonia Mey () (Link to the form can be found here: http://www.eastscotbiodtp.ac.uk/how-apply-0)

If you are nominated by the supervisor(s) of the EASTBIO PhD project you wish to apply for, they will provide a Supervisor Support Statement.

All EASTBIO (online) interviews will be in the week 8-12 February 2021 with awards made the following week.

The School of Chemistry holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. The University is a member of the Race Equality Charter and is a Stonewall Scotland Diversity Champion, actively promoting LGBT equality. The University has a range of initiatives to support a family friendly working environment. See our University Initiatives website for further information. University Initiatives website: https://www.ed.ac.uk/equality-diversity/help-advice/family-friendly

Funding Notes

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership View Website. This opportunity is open to UK and International students and provides funding to cover stipend and tuition fees. Please refer to UKRI website View Website and Annex B View Website of the UKRI Training Grant Terms and Conditions for full eligibility criteria. Applicants must hold a first or upper second class UK honours degree or equivalent.

References

1) Xu, J., Chmela, V., Green, N.J., Russel, D.A., Janicki, M.J., Góra, R.W., Szabla, R., Bond, A.D., Sutherland, J.D.*, Selective prebiotic formation of RNA pyrimidine and DNA purine nucleosides. Nature 2020, 582, 60–66.
2) Kim, S. C.; O’Flaherty, D. K.; Zhou, L.; Lelyveld, V. S.; Szostak, J. W. Inosine, but None of the 8-Oxo-Purines, Is a Plausible Component of a Primordial Version of RNA. Proc. Natl. Acad. Sci. 2018, 115 (52), 13318–13323.
3) Mey et al., Best practices in Alchemical Free Energy Calculations arXiv:2008.03067


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