Utilizing machine learning to decipher the mechanism of protein-DNA binding

Details

Transcriptional gene regulation, or the ability to control the timing and levels of gene expression through binding of transcription factors (TFs) to DNA, is a defining feature of life. It allows organisms to coordinate function internally and to respond to external changes in the environment. The main mechanism through which gene regulation occurs, especially in bacteria, relies on binding between a protein (transcription factor, or TF) and DNA. TFs bind DNA in a sequence-specific manner, preferring some residues over others and in doing so enabling regulation to be specific and efficient. In spite of the importance and the central role that the specificity of transcription factor-DNA binding plays in gene regulation, we know little about how 3D protein structure determines this specificity.

The aim of this interdisciplinary project is to utilize cutting-edge machine learning tools and techniques to decipher how the 3D structure of the TF determines its sequence-binding specificity. We will do this for TetR, a bacterial transcriptional regulator that is critical in the regulation of antibiotic resistance to an entire category of antibiotics, tetracyclines.

To achieve this aim, the student will address the following objectives:

1. develop a model to predict sequence-binding specificity of wildtype TetR: utilize Alpha Fold and Rosetta to simulate how the wildtype TetR protein binds to a range of different DNA sequences and use those predictions to reconstruct the biophysical sequence-binding specificities of TetR. Binding specificities for TFs are currently determined experimentally and Lagator group did so for TetR, providing a unique experimental reference dataset to validate and fine-tune the model on. Achieving this objective will enable and demonstrate how to determine sequence specificity of TFs computationally.

2. characterize sequence-binding specificity of TetR variants: currently, almost nothing is known about how binding specificity changes as the protein sequence changes. Here, we will rely on the novel aspect of Alpha Fold, namely, its ability to predict structure of protein-DNA complexes, to simulate a large number of TetR variants with mutations in the DNA-binding domain. Then, the student will utilize the model from Objective 1 to determine sequence-binding specificities of all these variants. This will mark the first study to characterize how binding specificity changes as a consequence of changes to protein sequence.

3. decipher how structure shapes binding specificity: armed with a large number of TetR variants with characterized binding specificities (from Objective 2), the student will interrogate the relationship between the differences in their structure and in their binding specificities. Doing so will allow us to identify, for the first time, how changes to protein structure alter its binding.

Achieving the aim and objectives of the study will: (i) provide key novel insights into the relationship between protein sequence, structure and function (i.e., its binding specificity). As such, the project will be the first to unravel this critical relationship, and to do so for a key regulator of antibiotic resistance. (ii) develop new techniques at the interface between machine learning, structural biology and biophysics, and demonstrate how they can be applied to tackle key outstanding questions of relevance to the study of gene regulation, evolution, molecular and synthetic biology.

To achieve the aim and objectives will require an interdisciplinary team, with expertise in the biology and biophysics of gene regulation (Dr. Lagator), structural biology (Prof. Lovell) and machine learning/AI (Prof. Rattray). The student will therefore have all the critical support in place to tackle this project, which can be found with the supervisors and the members of their research groups. Each of the supervisors will provide bespoke support for the student, depending on their background and development needs. The student will have a weekly meeting with the supervisors, and will also be assigned an advisor to help identify, signpost and develop their skillset to tackle the project aim. They will also be invited to join respective group meetings, providing an opportunity to regularly present their work and benefit from the experience of other PhD students and postdocs. The student will have access to various workshops and courses offered within the Faculty, aimed at developing research and soft skills of PhD students. The student will also be integrated into the Microbial Evolution Research Manchester (MERMan) group, one of Europe’s biggest clusters of researchers working on various aspects of microbial ecology and evolution, where they will be invited to give seminars and expand their research network.

Eligibility 

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in computer science, physics, mathematics, bioinformatics or other related disciplines. Alternatively, the candidates would exhibit strong experience in computational biology and coding.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How to Apply

To be considered for this project you MUST submit a formal online application form – on the application form select PhD Bioinformatics Programme. Full details on how to apply can be found on the Website: How to apply for postgraduate research at The University of Manchester

If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website: Equality, diversity and inclusion (EDI | Postgraduate Research | Biology, Medicine and Health | University of Manchester

Funding Notes

Applications are invited from self-funded students. This project has a Band 2(med) fee. Details of our different fee bands can be found on our website https://www.bmh.manchester.ac.uk/study/research/fees/