Oxford Protein Informatics Group (OPIG)

What we do

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

Konrad Krawczyk
Saulo de Oliveira
Claire Marks
Jinwoo Leem
Mihai Cucuringu

Visitor

Eoin Malins

Students

Cristian Regep
Jaroslaw Nowak
Eleanor Law
Hannah Patel
Florian Klimm
Elliot Nelson
Lyuba Bozhilova
Joseph Buck
Clare West
Aleksandr Kovaltsuk
Susan Leung
Laura Depner
Fergus Boyles
Anne Neirobisch
Fergus Imrie
James Wilsenach
Javier Pardo Diaz
Lucian Chan
Mark Chonofsky
Matthew Raybould
Wing Ki (Catherine) Wong

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