Greg Bowman

Assistant Professor of Biochemistry and Molecular Biophysics (School of Medicine)
Affiliated Professor of Chemistry
PhD, Stanford University
research interests:
  • Protein biophysics
  • Molecular simulation
  • Markov state models
  • Allostery
  • Drug design
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contact info:

mailing address:

  • 8231 (Medical School Campus)
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Professor Bowman's research focuses on protein biophysics, molecular simulation, Markov state models, allostery, and drug design.

Bowman combines computer simulations and experiments to understand the distribution of different structures a protein adopts and how this ensemble determines a protein’s function.  The overarching objectives are (1) to understand how drugs and mutations exert allosteric control over distant sites and (2) to design drugs and mutations that exploit proteins’ conformational heterogeneity to control function.  Examples of ongoing projects include (1) understanding how mutations give rise to antibiotic resistance, (2) designing allosteric drugs to combat antibiotic resistant infections, (3) understanding allosteric networks in G proteins and designing allosteric anti-cancer drugs, and (4) understanding and interfering with the mechanisms of Ebola infection.  To facilitate these applications, he also develops enhanced sampling algorithms for simulating long timescale dynamics of proteins and nucleic acids.

Selected Publications 

Sukrit Singh and Gregory R. Bowman

Quantifying allosteric communication via both concerted structural changes and conformational disorder with CARDS

J. Chem. Theory Comput. (2017)


Kathryn M. Hart, Chris M. W. Ho, Supratik Dutta, Michael L. Gross & Gregory R. Bowman

Modeling proteins’ hidden conformations to predict antibiotic resistance

Nature Communications 7, Article number: 12965 (2016)


Zimmerman, M.I. and Bowman, G.R.

FAST Conformational Searches by Balancing Exploration/Exploitation Trade-Offs

J. Chem. Theory Comput 11:5747-5757 (2015).


Bowman, G.R. Bolin, E.R., Hart, K.M., Maguire, B.C. and Marqusee, S.

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

PNAS 112:2734-2739 (2015)



Packard Fellowship for Science and Engineering


Burroughs Wellcome Fund Career Award at the Scientific Interface

Miller Research Fellow

Genome Technology Young Investigator Award

Berry Postdoctoral Fellow

Thomas Kuhn Paradigm Shift Award, American Chemical Society

NSF Graduate Research Fellowship

Jean Dreyfus Boissevain Undergraduate Scholarship for Excellence in Chemistry