Dr. Matteo Degiacomi - Generative Neural Networks vs Protein Conformational Spaces

9 November, 2023 15:00 (local Swedish time)
Generative Neural Networks vs Protein Conformational Spaces
Matteo Degiacomi (Durham University, United Kingdom)
Abstract:
Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. I will discuss how a generative neural network (GNN) can learn a continuous conformational space representation from example structures produced by molecular dynamics simulations or experiments. I will then show how such representation, obtained via our freely available software molearn [1], can be leveraged on to predict putative protein transition states [2], or to generate conformations useful in the context of flexible protein-protein docking [3]. Finally, I will demonstrate that transfer learning is possible, i.e., a GNN can learn features common to any protein.
[1] S.C. Musson and M. T. Degiacomi (2023). Molearn: a Python package streamlining the design of generative models of biomolecular dynamics. Journal of Open Source Software, 8(89), 5523 [2] V.K. Ramaswamy, S.C. Musson, C. Willcocks, M.T. Degiacomi (2021). Learning Protein Conformational Space with Convolutions and Latent Interpolations. Physical Review X, 11(1), 011052 [3] M.T. Degiacomi (2019). Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space. Structure, 27(6), 1034-1040.
Matteo obtained an MSc in Computer Science and a PhD in computational biophysics (2012) in the Swiss Federal Institute of Technology of Lausanne (EPFL). During his PhD studies he combined molecular dynamics simulations and global optimization algorithms to predict the assembly of large protein complexes. 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 computational methods for the prediction of protein 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 Fellowship, allowing him to establish his independent research in Durham University, and in 2020 he was appointed Associate Professor in soft condensed matter physics. His current research revolves around the development of machine learning methods to sample protein conformational spaces.
follow Matteo on twitter: @MatteoDegiacomi
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