Dr Matteo Degiacomi

Reader in Biomedical Artificial Intelligence

Contact details

Address

Street

room IF 1.31
University of Edinburgh
Informatics Forum
10 Crichton Street

City
Edinburgh
Post code
EH8 9AB
Street

University of Edinburgh
Joseph Black Building
David Brewster Road

City
Edinburgh
Post code
EH9 3FJ

Biography

Background

Matteo Degiacomi, obtained an MSc in Computer Science (2008) and a PhD in computational biophysics (2012) in Ecole Polytechnique Fédérale de Lausanne (EPFL). During his PhD supervised by Prof Matteo Dal Peraro he combined molecular dynamics simulations and particle swarm optimization to predict the assembly into complexes of the pore-forming toxin Aerolysin and the type-III secretion system’s basal body. In 2013 he joined the research groups of Prof Justin Benesch and Prof Dame Carol Robinson FRS in the University of Oxford. His research, funded by a Swiss National Science Foundation Early Postdoc Mobility Fellowship, focused on the development of new computational methods for the prediction of protein molecular assembly guided by ion mobility, cross-linking, SAXS and electron microscopy data, as well as their application to the study of small Heat Shock Proteins and protein-lipid interactions. In 2017 he obtained an EPSRC Junior Research Fellowship, allowing him to establish his independent research in Durham University, and in 2020 he was promoted to Associate Professor. In 2024 he moved to the University of Edinburgh, taking joint Reader position between the School of Informatics and the School of Chemistry. 

Open to PhD supervision enquiries?

Yes

Research summary

Specific interactions of simple molecules produce phenomena of increasing complexity, culminating with the finely tuned biological mechanisms that ultimately make life possible. Understanding the structure and dynamics of these molecules is an important step to shed light on their function in an organism. The overarching goal of my work is the development and application of computational methods to interpret and exploit multiple sources of experimental data for the modelling of biomolecular systems at atomistic resolution. A particular area of current focus is the combination generative  modelling and molecular dynamics simulations to sample protein conformational spaces.

Please use my ‪Google Scholar profile to view my most up to date publications.

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