Imperial College London


Faculty of Natural SciencesDepartment of Life Sciences

Director Centre for Bioinformatics



+44 (0)20 7594 5212m.sternberg Website




306Sir Ernst Chain BuildingSouth Kensington Campus





Professor Michael J. E. Sternberg is the Director of the Centre for Integrative Systems Biology and Bioinformatics (CISBIO) and holds the Chair of Structural Bioinformatics.

CISBIO brings together scientists from a wide range of different fields to develop innovative interdisciplinary approaches to understanding biological problems.  A key component of the interdisciplinary strategy is a repeated cycle of experimentation and modelling.

Professor Sternberg has been involved in schools outreach programmes funded by the Royal Society and Rolls Royce.

Professor Sternberg entered Bioinformatics via his D.Phil in Biophysics (Oxford). He obtained a first degree in Physics (Cambridge) and a Masters in Computing (Imperial College). He has worked at Oxford, Birkbeck College, Cancer Research UK and joined Imperial College in 2001.

The main research interests of his group are:

  • Prediction of protein structure and function
  • Prediction of macromolecular docking and interactions
  • Prediction of the effect of genetic variants particularlly those associate with disease
  • Network modelling for Systems Biology
  • Logic-based drug design

Professor Sternberg's group web page is located at and includes links to programs and resources available for use by the community.

Full details of his research interests can be found at



Sillitoe I, Andreeva A, Blundell TL, et al., 2019, Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation, Nucleic Acids Research, ISSN:0305-1048

Lenhard B, Sternberg MJE, 2019, Computation resources for molecular biology: Special issue 2019, Journal of Molecular Biology, Vol:431, ISSN:0022-2836, Pages:2395-2397

Ittisoponpisan S, Islam S, Khanna T, et al., 2019, Can predicted protein 3D-structures provide reliable insights into whether missense variants are disease-associated?, Journal of Molecular Biology, Vol:431, ISSN:0022-2836, Pages:2197-2212

Leal Ayala LG, David A, Jarvelin MR, et al., 2019, Identification of disease-associated loci using machine learning for genotype and network data integration, Bioinformatics, ISSN:1367-4803

Ofoegbu T, David A, Kelley L, et al., 2019, PhyreRisk: a dynamic web application to bridge genomics, proteomics and 3D structural data to guide interpretation of human genetic variants, Journal of Molecular Biology, Vol:431, ISSN:0022-2836, Pages:2460-2466

More Publications