Michael Feig

We use computers to study biology at the molecular level. We have worked on the accurate modeling of protein structures since CASP4. A key contribution has been the refinement of homology models via physics-based molecular dynamics simulations. 

Since the advent of AlphaFold, we have shifted our interest to the modeling of conformational ensembles focusing on:

* predictions of state-specific structures
* dynamic ensembles consistent with experimental data

Biological macromolecules are not static objects but their structures fluctuate dynamically. 

In addition to thermal fluctuations, transitions between different states are especially important for biological function.

Specific systems that we have studied in detail include RNA polymerase II, the MutS DNA mismatch recognition system, kinases, and the NS3/4a viral protease.

We capture dynamics traditionally via molecular dynamics computer simulations and more recently via generative AI models.

Topics that we are currently interested in:

* Conformational sampling of highly dynamic regions
* Generation of structural ensembles consistent with experimental data, especially from NMR or cryoEM
* Rapid generation of functional states from a single input structure

# Titles
* Professor

# Education
* Ph.D. 1999, University of Houston
* M.S. 1994, Technical University of Berlin

# Previous Appointments
* Alfred P. Sloan Fellow, 2005
* Postdoctoral Fellow, 1999-2003, The Scripps Research Institute

#Links 
* [Feig Lab Website](http://feig.bch.msu.edu/) 
* [Current Research](http://feig.bch.msu.edu/web/research/)
* [Google Scholar Citations](https://scholar.google.com/citations?user=DkeaoNoAAAAJ&hl=en)