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AffiliationTelephone
Associate Professor in the Department of Physics+44 (0) 191 33 43642
Associate Professor in the Centre for Materials Physics 

Biography

Matteo T. Degiacomi, born in Lugano (Switzerland), 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 developed of POW, a flexible parallel optimization environment. POW was applied to the prediction of pore-forming toxin Aerolysin heptameric conformation and of 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. In 2020 he was promoted to Associate Professor.

Research Interests

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 near-atomistic resolution. In this context, our current research focusses on combining machine learning and molecular dynamics simulations to sample protein conformational spaces.

Research interests

  • Computational Biophysics
  • Machine Learning
  • Molecular Dynamics

Publications

Chapter in book

Conference Paper

Journal Article

Supervision students