Drug Bioinformatics (Prof. Kalinina)

Drug Bioinformatics (Prof. Kalinina)

About us

Bioinformatics is instrumental in all areas of molecular biology, from analysis of genome sequences towards predicting three-dimensional structure of drug-target complexes. We apply cutting-edge bioinformatics and computer science techniques for discovery of novel resistance mechanisms and predicting mode-of-action of bioactive compounds

Head of the Group

Prof. Dr. Olga Kalinina

Olga Kalinina received a M.Sc. degree with distinction in mathematics from the Moscow State University in 2003 and a Ph.D. from the Engelhardt Institute for Molecular Biology of the Russian Academy of Sciences in 2007. She continued her research in bioinformatics as a postdoctoral fellow at the European Laboratory for Molecular Biology, for which she was awarded an EMBO Long Term Fellowship from the European Molecular Biology Organization, and later at the University of Heidelberg, working with Prof. Dr. Robert B. Russell (2007-2011). In 2012, she established her independent junior group at the Max Planck Institute for Informatics in the Department for Computational Biology and Applied Algorithmics lead by Prof. Dr. Dr. Thomas Lengauer, where she was researching resistance mechanisms in human viruses until 2018.

"It is fascinating from a theoretical point of view and essential for practical applications in the biomedical research to use modern computer science methods for the discovery of novel means to fight infectious diseases."

Our Projects

One particular focus of our group is the development of machine learning tools for predicting functional consequences of genetic variants that can be associated with a particular disease or resistance phenotype. In doing so, we aim to predict not only the direction and the magnitude of the effect, i.e. whether a certain variant is likely to be pathogenic or cause resistance to a drug, but also the exact molecular mechanism, which is responsible for it. We do so by combining phylogenetic methods with approaches from structural bioinformatics: computational modelling three-dimensional structure of proteins, their interactions, and dynamics, united in a robust machine learning framework.

A particular emphasis of this line of work is discovery of novel resistance mechanisms. Another focus of the research group is investigation of protein-drug interactions and drug-binding pockets with data-mining graph theory-based approaches. We aim to describe protein functional motifs and drug-binding patterns in them, and eventually develop novel machine-learning tool for prediction of drug affinity based on structural descriptors of protein-drug interactions.