PhD Studentship: Unravelling the structural diversity of ultra-long CDR loops with antimicrobial potential

Supervisors :

Prof Christine Orengo – The Orengo Group website

Dr Gorka Lasso - The Lasso Lab Website

Collaborator: Prof. Kartik Chandran – Chandran Lab, Albert Einstein College of Medicine

Abstract:

Antibodies are a rapidly expanding class of therapeutics with applications in infectious disease, cancer, and immunotherapy. Among them, bovine antibodies with ultralong complementarity-determining regions (UL-CDRs) represent a unique structural class with exceptional potential for targeting otherwise inaccessible epitopes. These UL-CDRs feature a distinctive architecture comprising a β-ribbon stalk and a disulfide-rich knob mini-domain, enabling high stability and independent folding. Despite their promise, the structural and functional diversity of UL-CDRs remains largely unexplored. This project aims to characterise the sequence, structural, and functional properties of UL-CDRs using deep learning and structural bioinformatics, with the goal of identifying novel antimicrobial peptide candidates.

Approach and Methods:

• Apply deep learning-based modelling and clustering to analyse a curated dataset of hundreds of thousands of UL-CDR sequences
• Characterise sequence–structure relationships and structural diversity of UL-CDRs
• Identify structural similarities between UL-CDRs and proteins from viruses, prokaryotes, and eukaryotes
• Predict potential antimicrobial and therapeutic functions of UL-CDRs
• Collaborate with experimental virologists to validate computational predictions

Impact and Outlook:

This project will uncover the untapped structural and functional potential of bovine UL-CDRs, laying the groundwork for engineering novel antimicrobial peptides. The findings could lead to the development of new therapeutic scaffolds with applications in infectious disease, biotechnology, and immunotherapy. The project also contributes to our understanding of antibody evolution and diversity across species.

Training and Student Development:

The student will gain expertise in:

• Structural bioinformatics and protein modelling
• Deep learning and machine learning for biological data
• Sequence and structure analysis of large-scale datasets
• Functional annotation and evolutionary analysis
• Collaborative research with experimental virology labs

The project offers access to GPU-enabled HPC clusters, high-end workstations, and a collaborative, interdisciplinary environment. The student will develop a versatile skill set applicable to careers in academia, biotech, and pharmaceutical R&D.

Research Environment:

The Orengo and Lasso labs provide a dynamic, inclusive, and multidisciplinary research environment at the intersection of computational biology and infectious disease. The student will benefit from joint supervision, regular group meetings, and collaboration with the Chandran Lab in New York. The project is supported by cutting-edge computational infrastructure and a strong culture of mentorship and innovation.

Desirable Prior Experience:

• Background in bioinformatics, computational biology, structural biology, or related fields
• Experience with protein modelling, machine learning, or sequence analysis is advantageous

Interest in antimicrobial research and therapeutic development

How to apply

This project is offered as part of the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste  

Applications should be submitted by 12^th January 2026.

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