Dr Matteo T. Degiacomi
(email at email@example.com)
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.
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. Particular attention is dedicated to the characterization of protein conformational spaces, and their exploitation in a molecular modelling framework. To reach this objective I combine software development with molecular dynamics simulations, and interact closely with experimental teams.
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. While often a single experimental technique cannot fully describe a molecular system under study diverse sources of information can be integrated into consistent models by means of specialized software. This enables rationalizing existing data, and generating new testable hypotheses.
Molecular ensemble representations as means to rationalize multiple experimental data
Several experimental techniques are aimed at studying protein structure, which in turn can provide precious insights about molecular functions. Data are often rationalized against available high resolution structures obtained by X-ray crystallography or NMR. This can be problematic because (1) the protein conformation in an experiment might differ from its reference atomistic structure, (2) the data might only be explained by multiple conformations, and (3) the data obtained through different experiments might be ambiguous. In order to tackle these issues, I rely on sampling of proteins’ conformational space by means of molecular simulations and deep learning methods. Particular attention is dedicated to the analysis and comparison of ion mobility, SAXS, and chemical cross-linking. My aims are to:
- develop a framework to assess experimental data against a full protein conformational ensemble and determine a conformational subset consistent with it
- analyse whether, and to which degree, combinations of different techniques can enhance structure discrimination within an ensemble of candidates
Flexible protein assembly prediction restrained by low-resolution experimental data
Proteins often assemble into complexes to achieve a specific biological function. Obtaining high resolution atomistic structures of such complexes, however, is challenging. Software that can predict possible protein arrangements using available experimental data as guide can be exploited. The prediction of the structure of a protein assembly is however often difficult, because when interacting two binding partners may change conformation. These changes may be limited to local side chain rearrangements in the interacting surface, or may involve large scale rearrangements, which are difficult to predict. Further challenges arise from difficulties in properly combining diverse (and possibly inconsistent) analytical data. A final hurdle is represented by the difficulty of properly treating electrostatic contributions during the docking process. My aims are to:
- integrate low resolution ensemble-based experimental data into the docking process handled by cutting-edge optimization techniques
- develop methods to predict the assembly of multiple flexible protein copies according to arbitrary, deformable topologies
- develop methods to appropriately score protein-protein electrostatic interactions
Methods application: the case of integrins
Integrins play a central role in processes as diverse as cell adhesion, migration and apoptosis. Their malfunction leads to a range of diseases including autoimmunity and cancer. Integrins can form a range of different heterodimers featuring a large extracellular domain, a helical transmembrane region and a small intracellular domain. Binding of protein ligands to the intracellular or extracellular regions leads to large conformational changes allowing the cell to bind the extracellular matrix and sense its environment. My goal is to provide an atomistic view of integrin structure and dynamics upon binding, and new leads for the development of drugs targeting integrin-related diseases. My aims are to:
- generate structural models of integrin dimeric variants consistent with available data
- characterise integrin dynamics upon binding, and how this is affected by known mutations
- study how glycosylation and metal ion binding affect integrin ligand binding
- predict the arrangement of integrin oligomeric assemblies
- Degiacomi, Matteo T., Schmidt, Carla, Baldwin, Andrew J. & Benesch, Justin L.P. (2017). Accommodating protein dynamics in the modeling of chemical crosslinks. Structure 25(11): 1751-1757.e5.
- Pritišanac, I., Degiacomi, M.T., Alderson, T.R., Carneiro, M.G., AB, E., Siegal, Gregg & Baldwin, Andrew J. (2017). Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory. Journal of the American Chemical Society 139(28): 9523-9533.
- Tamò, G., Maesani, A., Träger, S., Degiacomi, M.T., Floreano, D. & Dal Peraro, Matteo (2017). Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies. Scientific reports 7(1): 235.
- Landreh, M., Marklund, E.G., Uzdavinys, P., Degiacomi, M.T., Coincon, M., Gault, J., Gupta, K., Liko, I., Benesch, J.L.P., Drew, D. & Robinson, C.V. (2017). Integrating mass spectrometry with MD simulations reveals the role of lipids in Na+/H+ antiporters. Nature Communications 8: 13993.
- Erastova, Valentina, Degiacomi, Matteo T., Fraser, Donald & Greenwell, H. Chris (2017). Mineral Surface Chemistry Control for Origin of Prebiotic Peptides. Nature Communications 8: 2033.
- Hopper, J.T.S., Ambrose, S., Grant, O.C., Krumm, S.A., Allison, T.M., Degiacomi, M.T., Tully, M.D., Pritchard, L.K., Ozorowski, G., Ward, A.B., Crispin, M., Doores, K.J., Woods, R.J., Benesch, J.L.P., Robinson, C.V. & Struwe, W.B. (2017). The Tetrameric Plant Lectin BanLec Neutralizes HIV through Bidentate Binding to Specific Viral Glycans. Structure 25(5): 773-782.e5.
- Liko, I., Degiacomi, M.T., Mohammed, S., Yoshikawa, S., Schmidt, C. & Robinson, C.V. (2016). Dimer interface of Bovine cytochrome c oxidase is influenced by local posttranslational modifications and lipid binding. Proceedings of the National Academy of Sciences of the United States of America 113(29): 8230-8235.
- Degiacomi, M.T. & Benesch, J.L.P. (2016). EM∩IM: Software for relating ion mobility mass spectrometry and electron microscopy data. Analyst 141(1): 70-75.
- Laganowsky, A., Reading, E., Allison, T.M., Ulmschneider, M.B., Degiacomi, M.T., Baldwin, A.J. & Robinson, C.V. (2014). Membrane proteins bind lipids selectively to modulate their structure and function. Nature 510(7503): 172-175.
- Degiacomi, M.T. & Dal Peraro, M. (2013). Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling. Structure 21(7): 1097-1106.
- Degiacomi, M.T., Iacovache, I., Pernot, L., Chami, M., Kudryashev, M., Stahlberg, H., Van Der Goot, F.G. & Dal Peraro, M. (2013). Molecular assembly of the aerolysin pore reveals a swirling membrane-insertion mechanism. Nature Chemical Biology 9(10): 623-629.