Projects

Molecular Mechanisms of Disease Mutations

Figure2Mutations can render proteins nonfunctional and may be responsible for many diseases. However, the molecular mechanisms of their effects on proteins remain unclear. It is extremely difficult to model impacts produced by mutations, to build an actual model of a mutated protein and estimate the quantitative changes in protein stability, binding affinity and perturbation effect on relevant biochemical pathways. These tasks cannot be directly achieved by machine learning methods that train their models on existing datasets to distinguish known disease-associated from neutral mutations. We develop computational approaches to make structural models of mutated proteins and estimate the effects of mutations on protein interaction networks, binding and pathways. Our methods are based on using molecular mechanics force fields, statistical potentials, fast side-chain optimization and systems analysis algorithms.



Developing mathematical models to explore background mutational processes in cancer

Figure2 Much remains unknown about the progression and heterogeneity of mutational processes in different cancers and their diagnostic and clinical potential. We develop methods and mathematical models to explore DNA context-dependent mutational patterns and underlying somatic cancer mutagenesis. We construct and analyze mutational profiles of cancer samples, that are categorized based on cancer type and primary tumor sites and are normalized by removing the bias from mutational hotspots with recurring mutations.We identify the combinations of underlying mutagenic processes including those related to infidelity of DNA replication and repair machinery, and various other endogenous and exogenous mutagenic factors. In addition, we derive mutagen or cancer-specific mutational background models and apply them to calculate expected DNA and protein site mutability to decouple relative contributions of mutagenesis and selection in carcinogenesis.


Probing DNA-protein interactions in nucleosomes by combining experimental footprinting and computational modeling

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The crucial role of nucleosomes in chromatin function relies on its dynamics. We characterize the dynamics of nucleosomes on a microsecond timescale and analyze the coupling between conformations of histone tails and DNA geometry. We study the formations of DNA bulging and twist defects; this lead to a reorganization of histone-DNA interactions, suggestive of the formation of initial nucleosome sliding intermediates. We characterize the dynamics of histone tails upon their condensation on the core and linker DNA. We specifically focus on variant nucleosomes and design computational algorithms to identify their characteristic sequence and functional features and build their structural models. To refine our models we use experimental hydroxyl footprinting (HRF) technique. It probes nucleosome organization in solution with a high single-nucleotide precision unattainable by other methods. We propose an integrative modeling method for constructing high-resolution atomistic models of nucleosomes based on HRF experiments and computational modeling. Our method precisely identifies DNA positioning on nucleosome by combining HRF data for both DNA strands with the pseudo-symmetry constraints. As a result we used our integrative approach to precisely model the atomistic structure of centromeric nucleosome in yeast.


Evolution of protein interactions and binding sites

We investigate the evolution of protein-protein interactions and binding sites. As a result, major phyletic branches with the largest expansion in the number of novel binding sites have been identified. Many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We study evolutionary and physical mechanisms of protein homooligomer formation that are very diverse and not well understood. Certain homooligomeric states may be conserved within protein subfamilies, therefore providing the specificity to particular substrates while minimizing interactions with unwanted partners. In addition, transitions between different oligomeric states may regulate protein activity and support the switch between different pathways.

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