This research cluster develops and applies AI, machine learning, and computational structural biology to understand how protein sequence, structure, dynamics, and evolution determine biological function. By combining deep learning, molecular simulation, bioinformatics, NMR spectroscopy, and large-scale structural databases, the groups predict protein function, interpret disease-associated mutations, and reveal the molecular mechanisms underlying health and disease.
People
| Name | AI-informed protein structure and antibody engineering research |
|---|---|
| Franca Fraternali | Using molecular simulations and bioinformatics to characterise protein-protein interaction networks and the impact of disease mutations on structural stability. |
| Flemming Hansen | Combining deep learning with NMR spectroscopy and computational tools to investigate the dynamics and regulation of disordered viral proteins and enzymes. |
| David Jones | Developing AI-driven computational methods to predict and design protein structure and function, enabling new insights into biology, evolution, disease mechanisms and biotechnology applications. |
| Christine Orengo | Developing the CATH database and computational algorithms to classify protein structures, predict functions and analyse evolutionary relationships. |
| Joseph Ng | Using computational structural biology to model the functional effects of genetic variants and the structural integrity of protein complexes. |
| Ian Sillitoe | Developing deep-learning and hidden Markov model-based tools for the structural classification and functional annotation of protein domains. |