2025-26 Project (Furnham & Wall)

Understanding drug resistance in key Mycobacterium tuberculosis drug targets using multiplex saturation mutagenesis and AI protein large language models

SUPERVISORY TEAM

Supervisor

Dr Nicholas Furnham at LSHTM
Email: nick.furnham@lshtm.ac.uk

Co-Supervisor

Dr Richard Wall at LSHTM
Email: richard.wall@lshtm.ac.uk

PROJECT SUMMARY

Project Summary

Developing new antibiotics that are less likely to generate clinical resistance is a holy grail in infectious disease drug development. The advent of AI technologies that can capture the multi-dimensional nature of the evolution of antibiotic resistance offers a potential route to addressing this challenge. This project aims to address this problem in the context of the development of a new generation of drugs targeting Tuberculosis (TB). TB remains a significant global health burden exacerbated by the reduced effectiveness of available therapies driven by resistance-conferring mutations. This project will focus on developing methodologies to predict emergence of drug resistance in TB as well as generating a platform that could be used to design inhibitors that avoid the most severe resistance-conferring mutations.

Project Key Words

AI, mutagenesis, tuberculosis, modelling, resistance

MRC LID Themes

  1. Infectious Disease
  2. Health Data Science

Skills

MRC Core Skills

  1. Quantitative skills
  2. Interdisciplinary skills

Skills we expect a student to develop/acquire whilst pursuing this project

Experimental molecular bacteriology (including M. tuberculosis) techniques including in vitro culture, and molecular biology, such as plasmid cloning, site-directed mutagenesis, reverse genetics and library construction. Advanced skills in sequencing analysis, structural bioinformatics and computational biology including genome analysis and machine learning / deep learning approaches including using protein large language models.

Routes

Which route/s are available with this project?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • LSHTM – MSc Health Data Science
  • LSHTM – MSc Medical Microbiology

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution, meeting – at the minimum – the institutional research degree regulations and expectations. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training (for research degree study). Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Primary location for duration of this research degree: LSHTM, London

Travel requirements for this project: No significant travel is envisaged for this project. There may be an opportunity to visit the headquarters of Janssen Pharmaceutica in Belgium as part of this PhD. Additionally, there is a potential to spend time at our collaborators labs at University of Queensland, Australia.

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum LSHTM institutional eligibility criteria for doctoral study.
  • As a significant proportion of the project is computational, a strong interest in bioinformatics techniques and some programming experience would be helpful. A background in the lab based molecular biosciences would be beneficial, especially if that included molecular or cellular bacteriology, though not essential. Appropriate training will be made available to fill gaps in knowledge/experience.

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = Yes

PROJECT IN MORE DETAIL

Scientific description of this research project

Project Objectives: 
The urgent need for new treatments for tuberculosis (TB) is driven by the increasing prevalence of antimicrobial resistance (AMR), which has rendered many existing treatments less effective. This threatens global TB control efforts and highlights the urgent need for new drugs with novel modes of action that can overcome drug-resistant strains. This project aims to develop a robust and rapid method to identify resistance-conferring mutations for new single-target antitubercular compounds. By constructing gene-wide mutation libraries created through saturation mutagenesis for several novel TB drug targets, the project will facilitate the identification of biologically-validated mutations that could inform drug design and future resistance surveillance in clinical settings. Initial experiments will support existing drug discovery work, together with our industrial partner, on an essential enzyme of the purine biosynthesis pathway as a potential drug target. While creating resistance-proof compounds may be challenging, the goal is to design compounds that minimise the most critical forms of resistance, extending the effectiveness and longevity of these drugs. Although focused on Mycobacterium tuberculosis, the causative agent of TB, the methods developed will be broadly applicable to other pathogens.

Key objectives:   
· Construct and validate gene-wide mutation libraries, using saturation mutagenesis, for novel M. tuberculosis drug targets   
· Generate resistance maps defining potential chemical space within drug binding sites   
· Develop computational strategies for designing compounds capable of bypassing the most serious resistance-conferring mutations in silico using protein large language models    

Techniques to be used:  
A range of computational techniques and tools will be used including: data collection, curation and management, structural bioinformatics, genomic sequence analysis, and machine learning and AI methods including the use of large language models built on protein data. Experimental methods include construction of gene-wide mutation libraries created via saturation mutagenesis using an extensive molecular biology techniques, culture and genetic modification of M. tuberculosis, susceptibility testing, and plasmid/bacterial library generation   

Data and resources availability: 
A diverse range of structurally different compounds for mutation selection are available from current on-going work and active collaborations with freedom to use agreements already in place. LSHTM houses a containment level 3 laboratory for work with infectious bacteria, including Mtb, which the student will have opportunity to access with training and skill development in containment bacteriology.  The Furnham group has access to a range of publicly available sequence and structural data relating to Mtb.    

Project risks and their mitigation:
The computational components are considered relatively low risk. However, there are some inherent experimental risks most notably the potential inability to accurately identify resistance mutations. This can be mitigated by using alternative library construction strategies, or traditional resistance selection experiments that will be able identify key resistance mutations for further downstream computational analysis.

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Furnham-Wall recording

MRC LID LINKS

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