Michael Feig
Associate Professor / Associate Professor (Biochemistry and Molecular Biology)Office: 218 Biochemistry
Phone: 517-432-7439 /
Websites: Research Group - Research Group - Area
Awards & Honors
Computational Biochemistry
(Research Description PDF - 2054 kb)Our group uses computational methods for studying the structure and dynamics of biological macromolecules. In particular we are interested in developing and applying new simulation methods for the realistic modeling of supramolecular assemblies and complex cellular environments.
An overarching theme in our group is the development and application of realistic implicit solvent models. Compared to explicit solvent representations, implicit solvent methods are computationally much more efficient and greatly reduce the level of complexity when modeling heterogeneous, non-aqueous environments.
We are particularly interested in studying protein-DNA interactions. In biological systems proteins and nucleic acids interact during gene duplication, transcription, and regulation, but the details on a molecular level are not well understood. We are focusing our efforts on questions of how the DNA mismatch recognition protein MutS recognizes defects in newly replicated DNA and how DNA repair is initiated subsequently. A detailed understanding of the DNA mismatch repair system is relevant for some types of cancer, which may occur as a result of defective mismatch repair. Based on crystallographic structures we are applying computer simulation techniques to investigate energetic and dynamic aspects of the MutS-DNA complex.
In another area of special emphasis we are examining the structure and dynamics of proteins and peptides embedded in phospholipid bilayer membranes. The heterogeneous nature of biological membranes presents challenges for experiments as well as computer simulations. We are developing new methods that facilitate the simulation of membrane environments for the study of important membrane proteins such as ATP-binding cassette (ABC) transporters.
A third area of interest is the prediction of protein structures from its amino acid sequence at levels of accuracy similar to experimental data. While it has become relatively easy to generate native-like models if structural templates are available from related proteins, such models often lack detailed features of amino acid side chain packing in the native structure. We are applying enhanced sampling techniques in combination with accurate energy functions in order to refine approximate protein structures towards the actual native conformation.
Selected Publications
Conformational Sampling of Peptides in Cellular Environments, Seiichiro Tanizaki, Jacob W. Clifford, Brian D. Connelly, Michael Feig, Biophysical Journal 2008, 94, 747-759.Accurate Prediction of Protonation State as a Prerequisite for Reliable MM-PB(GB)SA Binding Free Energy Calculations of HIV-1 Protease Inhibitors, Kitiyaporn Wittayanarakul, Supot Hannongbua, Michael Feig, Journal of Computational Chemistry 2008, 29, 673-685.
Implicit Solvent Simulations of DNA and DNA-Protein Complexes: Agreement with Explicit Solvent vs. Experiment, Jana Chocholousova, Michael Feig, Journal of Physical Chemistry B 2006, 110, 17240-17251.
The unorthodox SNAP50 zinc finger domain contributes to co-operative promoter recognition by human SNAPc, Gauri W. Jawdekar, Andrej Hanzlowsky, Stacy L. Hovde, Blanka Jelencic, Michael Feig, James H. Geiger, and Ronald W. Henry, Journal of Biological Chemistry 2006, 281, 31050-31060.
A New Generalized Born Formalism for Heterogeneous Dielectric Environments: Application to The Implicit Modeling of Biological Membranes, Seiichiro Tanizaki, Michael Feig, Journal of Chemical Physics 2005, 122, 124706.
