Oxford Protein Informatics Group (OPIG)

What we do

In OPIG, our research interests cover several areas in the field of protein structure and interaction networks, combining both theoretical work and empirical analyses. Of particular interest are the concepts of protein structure evolution, protein folding and structure prediction, as well as understanding the structure and evolution of protein interaction networks.

People

Academic Staff

Research Staff

Florian Klimm
Claire Marks

Visitor

Eoin Malins

Students

Joseph Bluck
Fergus Boyles
Lyuba Bozhilova
Lucian Chan
Mark Chonofsky
Laura Depner
Fergus Imrie
Aleksandr Kovaltsuk
Susan Leung
Elliot Nelson
Anne Nierobisch
Carlos Outeiral Rubiera
Javier Pardo Diaz
Hannah Patel
Eve Richardson
Matthew Raybould
Constantin Schneider
Dominik Schwartz
Clare West
Conor Wild
James Wilsenach
Wing Ki (Catherine) Wong
Anna Carbery

Publications

Pearce, N.M., Krojer, T., Bradley, A.R., Collins, P., Nowak, R.P., Talon, R., Marsden, B.D., Kelm, S., Shi, J., Deane, C.M. & von Delft, F. (2017), A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density, Nature Communications, 8:15123.
Regep, C., Georges, G., Shi, J., Popovic, B. & Deane, C.M. (2017), The H3 loop of antibodies shows unique structural characteristics, Proteins: Structure, Function, and Bioinformatics, 85(7):1311-1318.
Knapp, B., Dunbar, J., Alcala, M. & Deane, C.M. (2017,) Variable Regions of Antibodies and T-Cell Receptors May Not Be Sufficient in Molecular Simulations Investigating Binding, Journal of Chemical Theory and Computation, 13(7):3097-3105.
Pearce, N.M., Bradley, A.R., Krojer, T., Marsden, B.D., Deane, C.M. & von Delft, F. (2017). Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites, Struct. Dyn., 4(3):32104.
de Oliveira, S. & Deane, C. (2017), Co-evolution techniques are reshaping the way we do structural bioinformatics, F1000Research, 6:1224.
Marks, C., Nowak, J., Klostermann, S., Georges, G., Dunbar, J., Shi, J., Kelm, S. & Deane, C.M. (2017), Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction, Bioinformatics, pages btw823.
Marks, C. & Deane, C.M. (2017), Antibody H3 Structure Prediction, Comput. Struct. Biotechnol. J., 15:222-231.
Chen, J.W.C., Chen, Z.A., Rogala, K.B., Metz, J., Deane, C.M., Rappsilber, J. & Wakefield, J.G. (2017). Cross-linking mass spectrometry identifies new interfaces of Augmin required to localise the \gamma-tubulin ring complex to the mitotic spindle, Biology open, 6(5):654-663.
Deane, C.M. & Vásquez, M. (2017,) Developability of Biotherapeutics: Computational Approaches, Edited by Sandeep Kumar and Satish K. Singh MAbs, 9(1):12-14.
Demharter, S., Pearce, N., Beattie, K., Frost, I., Leem, J., Martin, A., Oppenheimer, R., Regep, C., Rukat, T., Skates, A., Trendel, N., Gavaghan, D.J., Deane, C.M. & Knapp, B. (2017), Ten simple rules for surviving an interdisciplinary PhD, PLOS Computational Biology, 13(5):e1005512