Principal investigator Nikolina Sostaric
E-mail address firstname.lastname@example.org
Protein interactions in 3D
In collaboration with Liedewij Laan
Protein-protein interactions (PPIs) are essential for functioning of any living cell. While some proteins form strong interactions, other PPIs are of a transient nature, forming interactions of varying strength and time duration. Moreover, a cell may modulate binding affinity of a protein:protein pair by, e.g., changing the amounts of proteins or introducing/removing post-translational modifications. In this project, we will use bioinformatics to explore PPIs in yeast polarity protein network. Our starting point will be the proximity labelling data (TurboID), where interactors of selected polarity network proteins were identified. Our aim will be to distinguish different types of interactions and learn more about this network.
- Mass spectrometry data analysis
- Performing simultaneous folding and docking of proteins using AlphaFold2-based tools
- Prediction of binding affinities
Branon, T.C., et al. (2018). Nature, 36(9), 880-887. DOI: 10.1038/nbt.4201.
Bryant, P., et al. (2022). Nature Communications, 13, 1265. DOI: 10.1038/s41467-022-28865-w.
RNA distribution clustering
In collaboration with Marianne Bauer
T cells play an important role in our immune system. To be able to effectively clear tumors and viruses, they have to switch from resting to activated state. We previously built distribution profiles of RNAs among different ribosome-bound states, which are a proxy for RNA translation. In this project, we want to learn which types of distributions RNAs follow and how they change upon T cell activation. To this aim, we will cluster RNA distributions based on their shape. We will first cluster profiles with a constraint of Gaussian distributions in each cluster. For comparison, we can use an information-theoretic clustering and k-means. In parallel, we will investigate what distributions fit best the currently found clusters, and variations over conditions.
- Data-analysis methods
- Interpretation of clustering results through the data, as well as through a mathematical analysis
Simulating dynamics of lipid bilayers
In collaboration with Marie-Eve Aubin-Tam
Biomolecular systems are not static but rather in constant motion. Molecular dynamics (MD) simulations give us a glimpse into molecular movements on an atomic scale and allow us to analyze dynamic events in biologic al systems. In this project, we will use computational biology tools, MD simulations in particular, to gain a better understanding of dynamic behavior and of quantitative properties of lipid bilayers. We will build several different lipid bilayers (both of homogeneous and heterogeneous compositions), surround them by water and ions, and simulate these systems to analyze properties such as lateral diffusion of lipids, among others. Ultimately, the results from MD simulations will be integrated with (already obtained) measurements of bilayers’ properties from the wet lab.
- Building molecular systems
- Molecular dynamics simulations trajectory analysis
Marrink, S.J., et al. (2019). Chemical Reviews, 119(9), 6184-6226. DOI: 10.1021/acs.chemrev.8b00460.
Spatial distribution of RNA molecules
In collaboration with Wolkers group (Sanquin, A’dam UMC)
T cells play a central role in human immune system by combating viruses and tumors. When they encounter a cell that needs to be cleared, T cells become activated and produce specific set of proteins, called cytokines, which are vital for target cell clearance. In this project, we will explore the spatial distribution of cytokine RNAs in human T cells. We will analyze 3D microscopy images of T cells in which positions of individual cytokine RNA molecules were captured by smFISH (single molecule fluorescence in situ hybridization). More specifically, we will investigate distances between RNAs, whether they’re forming clusters, polarization of RNA spots within the cells, as well as distribution of nuclear RNAs with respect to transcription sites.
- Analysis of 3D microscopy images
Stueland, M., et al. (2019). Scientific Reports, 9, 8267. DOI: 10.1038/s41598-019-44783-2.