From nucleic acid sequence to structure: Developing Deep Learning model for structure prediction of G-quadruplexes

Apply and key information

Summary

Motivation

Biomolecular self-assembly is a critical area of research with applications in biological function, therapeutics, biosensors, and biotechnological materials. Understanding the self-assembly of nucleic acids containing repetitive guanine segments, specifically G-quadruplexes (GQs), remains a significant frontier. These structures exhibit diverse architectures with specialized roles in living systems and potential applications in soft materials.

Our laboratory focuses on understanding and controlling the self-assembly of GQs. We investigate the interdependence of architectural motifs, the factors influencing self-assembly, and the mechanisms underpinning this process. To achieve this, we employ advanced biophysical characterization methods, including biomolecular NMR spectroscopy and Circular Dichroism, alongside Molecular Dynamics (MD) simulations. These studies, combined with structural diversity data from the Protein Data Bank (PDB), provide a foundation for developing predictive models linking nucleic acid sequences to atomic-detail structures.

Underlying aim

The main objective of this study is to develop an atomic-detail structure predictive model for unimolecular G-quadruplexes from DNA sequence.

Methods to be used

Machine Learning approaches.

MD simulations.

Impact

These studies advance understanding of G-quadruplex dynamics, enabling accurate structure prediction from sequences. This can accelerate drug discovery targeting GQs, guide nucleic acid material design, and reveal their biological roles. Integrating machine learning and simulations establishes a framework for nucleic acid self-assembly, driving innovation in science and technology.

Candidates with a background in 'Data Science', Chemistry, Physics, Engineering or simply with an interest in Machine Learning preferred.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Sound understanding of subject area as evidenced by a comprehensive research proposal
A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

First Class Honours (1st) Degree
Completion of Masters at a level equivalent to commendation or distinction at Ulster
Practice-based research experience and/or dissemination
Experience using research methods or other approaches relevant to the subject domain
Publications record appropriate to career stage
Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.