Nevan Krogan

Tuesday, 21 February 2017 at 11:00 | Large Operon, EMBL Heidelberg

Nevan Krogan | University of California (USA)

Hosts: Balca Mardin, BioMedX & Benjamin Lang, Structural and Computational Biology Unit


From Systems to Structure: Bridging Networks and Mechanism

There is a wide gap between the generation of large-scale biological data sets and more-detailed, structural and mechanistic studies. However, recent work that explicitly combine data from systems and structural biological approaches is having a profound effect on our ability to predict how mutations and small molecules affect atomic-level mechanisms, disrupt systems-level networks and ultimately lead to changes in organismal fitness. Our group aims to create a stronger bridge between these areas primarily using three types of data: genetic interactions, protein-protein interactions and post-translational modifications. Protein structural information helps to prioritise and functionally understand these large-scale datasets; conversely global, unbiasedly collected datasets helps inform the more mechanistic studies. Our efforts in this respect are presently focused on model organisms, including yeast and bacteria, as well as in mammalian cells, with a particular focus on pathogenesis.


Dr. Krogan was born and raised in Regina, Saskatchewan, Canada and obtained his undergraduate degree from the University of Regina. As a graduate student at the University of Toronto, Dr. Krogan led a project that systematically identified protein complexes in the model organism, Saccharomyces cerevisiae, through an affinity tagging-purification/mass spectrometry strategy. This work led to the characterisation of 547 complexes, comprising over 4000 proteins, and represents the most comprehensive protein-protein interaction map to date in any organism. To complement this physical interaction data, Dr. Krogan developed an approach, termed E-MAP (or epistatic miniarray profile), which allows for high-throughput generation and quantitative analysis of genetic interaction data.