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Computational Modeling and Experimental Data
One of our goals is to develop computational algorithms to obtain atomic level structural models of biological complexes by utilizing multiple experimental data. Currently, atomic level structures of biological molecules are mostly determined by X-ray crystallography.
However, some important biological systems are difficult to study by crystallography. Other techniques such as cryo-Electron Microscopy (cryo-EM), Small Angle X-ray Scattering (SAXS), Atomic Force Microscopy and X-ray Free Electron Lasers (XFEL) could be useful to provide additional information, however these provide only low-resolution structural information, such as overall shapes of the complexes
Combining X-ray, Cryo-EM and Molecular Dynamics Simulation
We use computational modeling techniques, which utilize simple concepts of mechanics in physics, to simulate the dynamics of biological molecules and combine various experimental data into the modeling process (hybrid approach). Multi-scale modeling, which describes the system at different level of details, from atomic description to continuum representation, needs to be used to study a variety of biological systems. Accurate description of molecular mechanics and computational algorithms for efficient sampling are the key components of these researches.
We develop these tools to study important biological molecules in collaboration with experimental groups. Previously we studied important macromolecular machines, such as ribosomes and myosin, and revealed the mechanisms of their conformational transitions.
We often employ coarse-grained models, in which not all of the atoms in the system are considered explicitly; rather each residue is reduced to a few points, which reduces the computational complexity of the systems and therefore speeds up calculations. Coarse grained models can be used with various computational techniques (MD simulations, Normal Mode Analysis). In addition, reduced representation of biological molecules from low-resolution data can be combined with coarse grained models to study dynamics of biological molecules.
We take advantage of coarse grained models to study dynamics of large biomolecules such as gap junction channels but also in the development of computational tools to interpret cryo-EM, XFEL, AFM data
Integrative modeling - input experimental data, integrate experimental data via computational tools to get understanding on biological function
Sorting X-ray Free electron laser data with Machine Learning Methods
To simulate experimental data, we need to capture the physical process of the measurements
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