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]
https://github.com/Degiacomi-Lab/molearn
[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.
Speaker bio:
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 new 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.
