Prof Dr Olga Kalinina
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.
Our research and approach
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.
By analyzing the spatial distribution of genetic variants in three-dimensional structures of proteins harboring them and their homologs, we can produce hypotheses about the functional consequences of these variants. For example, if a mutation caused by a single-nucleotide polymorphism lie on an interaction interface with another protein or in a ligand-binding pocket, it may affect the corresponding binding affinity, and mutations lying in the protein core can be detrimental for its stability. We develop methods that can annotate very large datasets in this way, providing insight into the relation between mutations’ annotated pathogenic or functional effect and their location in the three-dimensional structures of proteins and their complexes.
We build machine-learning methods for predicting the impact of mutations using a variety of features related to protein three-dimensional structures, interactions, and evolution. The methods can be trained to predict the impact on protein function, as well as their pathogenicity, which correlates with protein function. Additionally, we explore the possibility of training such methods to predict the impact on more specific phenotypes, such as resistance towards antibacterial compounds.
We employ a data mining technique called frequent subgraph mining to detect recurring structural patterns in three-dimensional structures of a set of distantly related proteins. These structural patterns that are significantly conserved over very long evolutionary distances represent known and novel functionally and structurally important motifs in the corresponding proteins.
We focus on residues that form pockets and cavities in protein structures and apply frequent subgraph mining to residue interaction network in proteins, chemical structures of potential binders, as well as their interactions to detect specific patterns of recognition for particular chemical moieties.
We apply classical methods of molecular dynamics simulations to investigate the impact of mutations that confer resistance in pathogens, as well as during cancer treatment, on the dynamics of the corresponding drug targets. In this way we can explain the mechanisms of resistance development even in cases when the immediate drug binding site is not visibly affected.
In this BMBF-funded project in cooperation with the Technical University Munich and University Hospital Greifswald, we apply our expertise in structural modelling and annotation to investigate novel mechanisms of pathogenesis in cardiac and renal diseases, focussing on the alterations of protein sequences caused by disease-specific alternative splicing events.
We use a combination of phylogenetic reconstruction and structural modelling in order to explore the mechanisms of resistance towards novel antibacterial compounds. We can trace the evolutionary spread of potential resistance factors and thus predict yet unobserved resistances in bacterial populations.
An extended catalogue of tandem alternative splice sites in human tissue transcriptomes
Mironov A, Denisov S, Gress A, Kalinina O, Pervouchine D (2020)
The bottromycin epimerase BotH defines a group of atypical α/β-hydrolase-fold enzymes
Sikandar A, Franz L, Adam S, Santos-Aberturas J, Horbal L, Luzhetskyy A, Truman A, Kalinina O, Koehnke J (2020)
Nat Chem Biol, 16 (9): 1013-1018DOI: 10.1038/s41589-020-0569-y
DIGGER: exploring the functional role of alternative splicing in protein interactions
Louadi Z, Yuan K, Gress A, Tsoy O, Kalinina O, Baumbach J, Kacprowski T, List M (2020)
Nucleic Acids ResDOI: 10.1093/nar/gkaa768
Frequent subgraph mining for biologically meaningful structural motifs
Keller S, Miettinen P, Kalinina O (2020)
SphereCon-a method for precise estimation of residue relative solvent accessible area from limited structural information
Gress A, Kalinina O (2020)
Bioinformatics (Oxford, England), 36 (11): 3372-3378DOI: 10.1093/bioinformatics/btaa159
Resistance-associated substitutions in patients with chronic hepatitis C virus genotype 4 infection
Dietz J, Kalinina O, Vermehren J, Peiffer K, Matschenz K, Buggisch P, Niederau C, Schattenberg J, Müllhaupt B, Yerly S, …, Welsch C, Sarrazin C (2020)
J. Viral Hepat.DOI: 10.1111/jvh.13322.
Non-active site mutants of HIV-1 protease influence resistance and sensitisation towards protease inhibitors
Bastys T, Gapsys V, Walter H, Heger E, Doncheva N, Kaiser R, Groot B, Kalinina O (2020)
Retrovirology, 17 (1)DOI: 10.1186/s12977-020-00520-6
A shift of dynamic equilibrium between the KIT active and inactive states causes drug resistance
Srikakulam S, Bastys T, Kalinina O (2020)
Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes
Ahmad M, Helms V, Kalinina O, Lengauer T (2019)
Journal of chemical theory and computation, 15 (4): 2166-2178DOI: 10.1021/acs.jctc.8b01074
Adenosine-to-Inosine RNA Editing in Mouse and Human Brain Proteomes
Levitsky L, Kliuchnikova A, Kuznetsova K, Karpov D, Ivanov M, Pyatnitskiy M, Kalinina O, Gorshkov M, Moshkovskii S (2019)
Proteomics, 19 (23)DOI: 10.1002/pmic.201900195.
Targeting actin inhibits repair of doxorubicin-induced DNA damage: a novel therapeutic approach for combination therapy
Pfitzer L, Moser C, Gegenfurtner F, Arner A, Foerster F, Atzberger C, Zisis T, Kubisch-Dohmen R, Busse J, Smith R, …, Vollmar A, Zahler S (2019)
Cell Death Dis., 10 (4)DOI: 10.1038/s41419-019-1546-9
Epistatic Interactions in NS5A of Hepatitis C Virus Suggest Drug Resistance Mechanisms
Knops E, Sierra S, Kalaghatgi P, Heger E, Kaiser R, Kalinina O (2018)
Genes, 9 (7)DOI: 10.3390/genes9070343
Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations
Bastys T, Gapsys V, Doncheva N, Kaiser R, Groot B, Kalinina O (2018)
Journal of chemical theory and computation, 14 (7): 3397-3408DOI: 10.1021/acs.jctc.7b01109
Patterns of amino acid conservation in human and animal immunodeficiency viruses
Voitenko O, Dhroso A, Feldmann A, Korkin D, Kalinina O (2016)
Bioinformatics (Oxford, England), 32 (17): 685-DOI: 10.1093/bioinformatics/btw441
StructMAn: annotation of single-nucleotide polymorphisms in the structural context
Gress A, Ramensky V, Büch J, Keller A, Kalinina O (2016)
Nucleic Acids Res, 44 (W1): 463-8DOI: 10.1093/nar/gkw364