The research aims to identify causes of cancer progression - to find which extrinsic and intrinsic factors can contribute to cancer mutation occurrence in DNA - and to discover molecular mechanisms determining how these mutations can affect proteins, protein interactions and dynamic behaviour of chromatin. This will be achieved by designing new computational methods that perform molecular modeling, molecular dynamics simulations and machine learning. High performance computing infrastructure is crucial to achieve these goals as simulations are extremely computational resource-consuming, especially for large macromolecular systems. These studies will offer key insights into the basis of mutagenesis and DNA repair and their contribution to cancer etiology - the knowledge of which is a prerequisite for developing novel targeted therapeutic strategies. Dr. Panchenko plans to use free open-source software packages like NAMD and Gromacs to run MD simulations. These simulations are extremely computational resource-consuming, especially for large macromolecular systems. She will perform modeling and molecular dynamics simulations and enhanced sampling on very large systems such as nucleosome and its complexes with other biomolecules. As an example, a full nucleosome in solvent may contain up to 500K to one million atoms. The goal of such simulations is to characterize the dynamics of the system on time scales as close to the biologically relevant as possible, which could be microseconds, milliseconds or even seconds.
