Research Interests


Characterizing function of biomolecules is no trivial task. While we often think of a protein, for example, as transitioning between a few states, there are in actuality 3N–6 internal degrees of freedom for a molecule with N atoms. So where are all the corresponding motions, and what is their significance to the researcher trying to understand dynamics of a system?

The answer to the first question is that these motions are distributed across a wide range of timescales: local librational motions, for example, happen very quickly (~picoseconds). On the other hand, concerted peptide plane motions within secondary structural elements are somewhat slower, happening in the range of tens of picoseconds to nanoseconds, depending on whether a few or many residues of a protein are involved. Global structural changes, for example, the shift of one or several helices allowing signalling or transport through a membrane, may occur on even slower timescales, typically microseconds or longer, depending on the energetic cost of the motion.

If a system exhibits transitions between two or several well-defined states, it may seem that the faster, more local motions are not so important. However, to understand the dynamics of the system, first, one must consider that all motions present may have influence on experimental measurements, depending on the timescale to which the experiment is sensitive. Therefore, neglecting these motions may easily lead to biased characterization of the dynamics [24]. Second, the distribution of motions contributes to the critical dynamics of the system, where sampling of many configurations provides the ‘grease’ required for larger motions, manifesting as the familiar Arrhenius constant (i.e. entropy contribution) for the slower transition between the major states. Then, to accurately and thoroughly characterize the dynamics of a system, one needs to consider the influence of the distribution of motion both on experimental measurements and the functional dynamics themselves. We perform such analyses on a variety of systems, including membranes, membrane proteins, and fibrils, in order to improve our general understanding of the interplay of dynamics in complex systems.

Principal Investigator

Foto Albert Smith-Penzel

Dr. Albert A. Smith-Penzel

Härtelstr. 16-18, 04107 Leipzig
Room 355.1
Phone +49(0)341 97-15773


Zumpfe, Kai, doctoral student

Experiment and simulation via "detector" analysis

Biological systems exhibit a wide range of motions distributed over multiple decades in correlation times. In the absence of a good model of the distribution of motion, a complete parameterization of the distribution is impossible. Rarely do we have such a model for complex structures such as proteins, so that we need another detailed source of information, which can be provided by simulation. Therefore, our primary methods are nuclear magnetic resonance (NMR) relaxation combined with molecular dynamics (MD) simulation (with the Huster and Hildebrand groups).

In order to use these methods together effectively, we required quantitative comparison of dynamics data obtained via the two methods. We have found that using explicit models for fitting of NMR- and MD- derived results in severe challenges to achieving such comparison [28]; therefore we instead use the detector analysis [25,27].  Detector analysis assumes that NMR relaxation rate constants and MD-derived time-correlation functions are the result of a distribution of correlation times. We may quantify the amplitude of motion for windows of correlation times, with the windows defined by detector sensitivities and where each window yields a detector response. While this approach does not provide a precise description of the dynamics, detectors yield quantitative parameters that may easily be compared between different methods, thus allowing more thorough analysis via other methodology.

Detector concept illustration. Re-orientational dynamics, as characterized by NMR, results in a distribution of correlation times (1–S2)θ(z) (black line). Whereas the order parameter, S2, captures the integral of the entire distribution, i.e. the total amplitude of motion, detectors act as filters where the detector sensitivities, ρn(z) (colored lines), define the filters, such that the amplitude of motion may be determined only for well-defined range of correlation times. In the illustration above, each sensitivity selects part of the total motion (shaded regions), where the integral of that region yields the detector response (bar plots, right)  [25]

New approaches to analysis

With quantitative agreement of experiment and simulation, we then apply novel approaches to gain new insight into dynamic systems. In HET-s fibrils, we use a combination of eigenmode analysis and detector analysis to show the role of local and collective motions in bringing about a broad distribution of correlation times [28]. Recently, we have used ‘frames’ to separate different types of motion in POPC lipid membranes in MD. Subsequent analysis with detectors and combination of MD and NMR data led to a comprehensive description of the motion as a function of position and correlation time, yielding the dynamic landscape of the motion.

Dynamic landscape of POPC membranes. By combining NMR relaxation data and MD simulation, analyzed with detectors and a new frame analysis, we are able to characterize the dynamic landscape of POPC, a description of the amplitude of re-orientational dynamics as a function of molecule position and correlation time [31]

Software Development

Precise analysis of dynamics data is critical to extracting and comparing dynamics information. For this purpose, we provide several software packages for NMR spectrum analysis and for analysis of dynamics data via detectors.

INtelligent Fitting Of Spectra (INFOS)

MATLAB-based analysis of spectra processed with Bruker Topspin or NMRPipe (INFOS is available without a MATLAB license with compiled MATLAB). We provide peak-picking and spectrum parameterization (peak positions, width, and height for spectra with arbitrary dimensionality). Spectrum fitting includes  advanced calculation of peak shapes, where acquisition and processing parameters are used to determine the precise shapes. Series of spectra may also be jointly fitted when peak amplitudes vary according to a user-defined function. A divide-and-conquer approach is used to fit large spectra, yielding highly efficient performance.


DIstortion Free Relaxation Analysis TEchnique (DIFRATE)

MATLAB-based fitting of NMR relaxation rate constants using the detector approach. Includes analysis of a variety of experiments (T1, T2, NOE, T1ρ, order parameters, cross-correlated cross-relaxation, etc.), for both solid- and solution-state NMR.


pyDIFRATE (beta-version)

Python-based analysis of NMR relaxation and MD trajectories using the detector approach. The current version is fully functional and demonstrated recently for two publications. However, note that an upcoming version will not be fully compatible with this beta release.

Smith, A. A. Interpreting NMR dynamic parameters via the separation of reorientational motion in MD simulation. arXiv (2021)


Smith, A. A., Vogel, A., Engberg, O., Hildebrand, P. W., Huster, D. A method to construct the dynamic landscape of a bio-membrane with experiment and simulation. Nat. Commun. (2021) In press


Education/Professional Experience

2003-2007     Bachelors Degree, University of Mount Union, Alliance, Ohio, USA

2007-2012     Ph.D., Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

2012-2018     Postdoctoral Researcher, ETH-Zürich, Zürich, Switzerland

2019-2020     Research Associate, University of Leipzig, Medical Faculty, Institute for Medical Physics and Biophysics, Leipzig, Germany

2021-              Research project leader, DFG-funded project: “Disentangling dynamics in biomolecules with experiment and simulation”, University of Leipzig, Medical Faculty, Institute for Medical Physics and Biophysics, Leipzig, Germany


  1. Smith, A.A. Interpreting NMR dynamic parameters via the separation of reorientaational motion in MD simulation. J. Magn. Res. Open 10-11 (2022) 100045. [DOI]
  1. Smith,A. A., Pacull, E.M., Stecher, S., Hildebrand, P.W., Vogel, A., Huster, D. Analysis of the Dynamics of the Human Growth Hormone Secretagogue Receptor Reveals Insights into the Energy Landscape of the Molecule. Angew Chemie Int Ed Engl (2023) e 20230203 [DOI; PubMed]
  1. Malär, A. A., Callon, M., Smith, A. A., Wang, S., Lecoq, L. Pérez-Segura, C., Hadden-Perilla, J. A., Böckmann, A., Meier, B.H. Experimental characterization of the Hepatitis B virus capsid dynamics by solid-state NMR. Front. Mol. Biosci. 8 (2022) 807577. [DOI]
  1. Smith, A. A., Vogel, A., Engberg, O., Hildebrand, P. W., Huster, D. A method to construct the dynamic landscape of a bio-membrane with experiment and simulation. Nat. Commun. (2022), [DOI;PubMed]
  1. Zumpfe, K.  Smith, A. A. Model-free or not? Front. Mol. Biosci. 8 (2021) 727553. [DOI]
  1. Smith, A. A., Ernst, M., Meier, B. H., Ferrage, F. How wide is the window opened by high-resolution relaxometry on the internal dynamics of proteins in solution? J. Biomol. NMR. 75 (2021) 119-131. [DOI;PubMed]
  1. Smith, A. A., Ernst, M., Riniker, S., Meier, B. H. Localized and collective motions in HET-s(218-289) fibrils from combined NMR relaxation and MD simulation. Angew. Chem. Int. Ed. 58 (2019) 9383-9388. [DOI;PubMed]
  1. Smith, A. A., Ernst, M., Meier, B. H., Ferrage, F. Reducing bias in the analysis of solution-state NMR data with dynamics detectors. J. Chem. Phys. 151 (2019) 034102. [DOI;PubMed]
  1. Penzel, S., Smith, A. A., Ernst, M., Meier, B. H. Setting the magic angle for fast magic-angle spinning probes. J. Magn. Res. 293 (2018) 115-122. [DOI;PubMed]
  1.  Smith, A. A., Ernst M., Meier B. H. Optimized ‘detectors’ for dynamics analysis in solid-state NMR. J. Chem. Phys. 148 (2018) 045104. [DOI]
  1. Smith A. A., Ernst, M., Meier, B. H. Because the light is better here: correlation-time analysis by NMR. Angew. Chem. Int. Ed. 129 (2017) 13778-13783. [DOI]
  1. Smith. A. A. INFOS: spectrum fitting software for NMR analysis. J. Biomol. NMR. 67 (2017) 77-94. [DOI]
  1. Smith, A. A., Ravotti, F., Testori, E., Cadalbert, R., Ernst, M., Böckmann, A., Meier, B. H. Partially-deuterated samples of HET-s(218-289) fibrils: assignment and deuterium isotope effect. J. Biomol. NMR. 67 (2017) 109-119. [DOI]
  1. Smith, A. A., Testori, E., Cadalbert, R., Meier, B. H., Ernst, M. Characterization of fibril dynamics on three timescales by solid-state NMR. J. Biomol. NMR. 65 (2016) 171-91. [DOI]
  1. Kaushik, M., Bahrenberg, T., Can, T.V., Caporini, M.A., Silvers, R., Heiliger, J., Smith, A. A., Schwalbe, H., Griffin, R. G., Corzilius. B. Gd(III) and Mn(II) complexes for dynamic nuclear polarization: small molecular chelate polarizing agents and applications with site-direct spin labeling of proteins. Phys. Chem. Chem. Phys. 18 (2016) 27205-27218. [DOI]
  1. Penzel, S., Smith, A. A., Agarwal, V., Hunkeler, A., Org, M. L., Samoson, A., Böckmann, A., Ernst, M., Meier, B. H. Protein resonance assignment at MAS frequencies approaching 100 kHz: a quantitative comparison of J-coupling and dipolar-coupling based transfer methods. J. Biomol. NMR. 63 (2015) 165-186. [DOI]
  1. Corzilius, B., Andreas, L. B., Smith, A. A., Ni, Q. Z., Griffin, R. G. Paramagnetic induced signal quenching in MAS-DNP experiments in frozen homogeneous solutions.” J. Magn. Reson. 240 (2014) 113-123. [DOI]
  1. Corzilius, B., Michaelis, V. K., Penzel, S. A., Ravera, E., Smith, A. A. Luchinat, C., Griffin, R. G. Dynamic Nuclear Polarization of 1H, 13C, and 59Co in a Tris(ethylenediamine)cobalt(III) Crystalline Lattice Doped with Cr(III). J. Am. Chem. Soc. 136 (2014) 11716-11727. [DOI].
  1. Smith, A. A., Corzilius, B., Haze, O., Swager, T. M., Griffin. R. G. Observation of strongly forbidden solid effect DNP transitions via electron-electron double resonance detected NMR. J. Chem. Phys. 139 (2013) 214201. [DOI]
  1. Ong, T. C., Mak-Jurkauskas, M. L., Walish, J. J., Michaelis, V. K., Corzilius, B., Smith, A. A., Clausen, A. M., Cheetham, J. C., Swager, T. M., Griffin, R. G. Solvent-free dynamic nuclear polarization of amorphous and crystalline ortho-terphenyl. J. Phys. Chem. B. 117 (2013) 3040-3046. [DOI]
  1. Michaelis, V. K., Corzilius, B., Smith, A. A., Griffin, R. G. Dynamic Nuclear Polarization of 17O: Direct Polarization. J. Phys. Chem. B. 117 (2013) 14894-14906. [DOI]
  1. Michaelis, V. K., Smith, A. A., Corzilius, B., Haze, O., Swager, T. M., Griffin, R. G. High-Field 13C Dynamic Nuclear Polarization with a Radical Mixture. J. Am. Chem. Soc. 135 (2013) 2935-2938. [DOI]
  1. Corzilius, B., Smith, A. A., Griffin., R. G. Solid effect in magic angle spinning dynamic nuclear polarization. J. Am. Chem. Soc. 137 (2012) 054201. [DOI]
  1. Smith, A. A., Corzilius, B., Barnes, A. B., Maly, T., Griffin, R. G. Solid effect dynamic nuclear polarization and polarization pathways. J. Chem. Phys. 136 (2012) 015101. [DOI]
  1. Smith, A. A., Corzilius, B., Bryant, J. A., DeRocher, R., Woskov, P. P., Temkin, R. J., Griffin, R. G. A 140 GHz Pulsed EPR/212 MHz NMR Spectrometer for DNP Studies. J. Magn. Res. 223 (2012) 170-179. [DOI]
  1. Kiesewetter, M. K., Corzilius, B., Smith, A. A., Griffin, R. G., Swager, T. M. Dynamic nuclear polarization with a water-soluble rigid biradical. J. Am. Chem. Soc. 134 (2012) 4537-4540. [DOI]
  1. Haze, O., Corzilius, B., Smith, A. A., Griffin, R. G., Swager, T. M. Water-soluble narrow-line radicals for dynamic nuclear polarization. J. Am. Chem. Soc. 134 (2012) 14287-14290.
  1. Dane, E. L., Corzilius, B., Rizzato, E., Stocker, P., Maly, T., Smith, A. A., Griffin, R. G., Ouari, O., Tordo, P., Swager, T. M. Rigid orthogonal bis-TEMPO biradicals with improved solubility for dynamic nuclear polarization. J. Org. Chem. 77 (2012) 1789-1797. [DOI]
  1. Yokoyama, K., Smith, A. A., Corzilius, B., Griffin, R. G., Stubbe, J. A. Equilibration of tyrosyl radicals (Y356•, Y731•, Y730•) in the radical propagation pathway of the Escherichia coli class Ia ribonucleotide reductase. J. Am. Chem. Soc. 133 (2011) 18420-18432. [DOI]
  1. Corzilius, B. Smith, A. A., Barnes, A. B., Luchinat, C., Bertini, I., Griffin, R. G. High-field dynamic nuclear polarization with high-spin transition metal ions. J. Am. Chem. Soc. 133 (2011) 5648-5651. [DOI]
  1. Hu, K.-N., Debelouchina, G.T., Smith, A. A., Griffin, R. G. Quantum mechanical theory of dynamic nuclear polarization in solid dielectrics. J. Chem. Phys. 134 (2011) 125105. [DOI]
  1. Maly, T., Andreas, L.B., Smith, A. A., Griffin, R. G. 2H-DNP-enhanced 2H–13C solid-state NMR correlation spectroscopy. Phys. Chem. Chem. Phys. 12 (2010) 5872-5878. [DOI]
  1. Smith, A. A., Drake, M. D., Rahim, A.K., Roberts, J.D. Determination of the dihedral angle of the monoanion of succinic acid aprotic media. J. Phys. Chem. A. 112 (2008) 12367-12371. [DOI]
  1. Smith, A. A., Gujrati, P. D. Solubility in compressible polymers: Beyond the regular solution theory. Eur. Polym. J. 43 (2006) 425-459. [DOI]