Aaron Frank

Assistant Professor of Chemistry; Assistant Professor of Biophysics

Ph.D. Chemistry, University of California, Irvine
Postdoctoral Fellow, University of Michigan - Ann Arbor

Research Focus: Integrative modeling and simulations biomolecules

Phone: 734.615.0609
E-mail: afrankz@umich.edu

In order to understand the relationship between molecular structure and dynamics and biological function, the Frank research group seeks to develop and deploy integrative modeling tools to elucidate the structure and dynamics of biologically relevant molecules. Our methods will utilize readily accessible experimental observables from a variety of sources to first guide structure prediction efforts and then guide atomistic simulations to map the entire conformational landscape of these molecules. We are primarily interested in using our methods to understand how functional ribonucleic acids — either by themselves or in concert with other molecules — achieve specific cellular functions. 

Frank Research Group



2013-2015 Presidential Postdoctoral Fellow Program (PPFP) Award
National Science Foundation (NSF) Predoctoral Award
Rackham Merit Fellowship (University of Michigan)
2004-2006 Minority Access to Research Careers (MARC) Research Fellow


Representative Publications

  1. Can Holo NMR Chemical Shifts be Directly Used to Resolve RNA-Ligand Poses?, Frank AT, Journal of Chemical Information and Modeling, 2016.

  2. Slowdown of Interhelical Motions Induces a Glass Transition in RNA. Frank AT, Zhang Q, Al. Hashimi HM, Andricioaei I. Biophysical Journal, 2015 Jun; 108(12): 2876-2885.

  3. Predicting Protein Backbone Chemical Shifts From Cα Coordinates: Extracting High ResolutionExperimental Observables from Low Resolution Models. Frank AT, Law SM, Ahlstrom LS, Brooks CL. JCTC, 2014 Dec; 11(1): 325-331

  4. A Simple and Fast Approach for Predicting 1H and 13C Chemical Shifts: Toward Chemical Shift-Guided Simulations of RNA. Frank AT, Law SM, Brooks CL. J. Phys. Chem. B, 2014 Oct118(42) 12168-12175.

  5. PCASSO: A fast and efficient Cα‐based method for accurately assigning protein secondary structure elements. Law SM, Frank AT, Brooks CL, J. Comp. Chem. 2014 Jul; 35(24): 1757-1761.

  6. Prediction of RNA 1H and 13C Chemical Shifts–A Structure Based Approach. Frank AT, Bae SH, Stelzer AC. J. Phys. Chem. B, 2013 Sep; 117 (43), 13497–13506

  7. Utility of 1H NMR Chemical Shifts in Determining RNA Structure and Dynamics. Frank AT, Horowitz S, Andricioaei I, Al-Hashimi HM, J. Phys. Chem. B, 2013 Jan; 117(7): 2045–2052

  8. Discovery of HIV-1 inhibitors by targeting an RNA dynamic ensemble. Stelzer AC, Frank AT, Jeremy KD, Michael S, Marta GHJ, Andricioaei I, Markovitz DM, Al- Hashimi HM, Nature Chem Biol. 2011 Jun; 7(8): 553-559

  9. Constructing atomic-resolution RNA structural ensembles using MD and motionally decoupled NMR RDCs. Stelzer AC, Frank AT, Bailor MH, Andricioaei I, Al-Hashimi HM, Methods. 2009 Oct; 49(2): 167-73

  10. Constructing RNA dynamical ensembles by combining MD and motionally decoupled NMR RDCs: new insights into RNA dynamics and adaptive ligand recognition. Frank AT, Stelzer AC, Al-Hashimi HM, Andricioaei I, Nucleic Acid Res. 2009 Oct; 49(2): 167-17