Group header

One of the major unsolved problems in molecular biology today is the protein folding problem: given an amino acid sequence, predict the overall three-dimensional structure of the corresponding protein. It has been known since the seminal work of Christian B. Anfinsen in the early seventies that the sequence of a protein encodes its structure, but the exact details of the encoding still remain elusive.

Since the protein folding problem is of enormous practical, theoretical and medical importance - and in addition forms a fascinating intellectual challenge - it is often called the holy grail of bioinformatics. The Statistical Structural Biology group focuses on Bayesian, probabilistic models of protein structure and their application to protein structure prediction, protein design and protein structure determination from experimental data (NMR, SAXS), including data obtained from protein ensembles. Recently, we started working on evolutionary models of protein structure evolution.

We are tackling the protein structure prediction problem from an original angle. Our group develops sophisticated probabilistic models that describe various aspects of protein structure, and uses these models in prediction, design and structure determination. We also extended our statistical approach to RNA 3D structure. Currently, our probabilistic models are mainly based on three key ingredients:

  1. Graphical models (including dynamic Bayesian networks), which are powerful machine learning methods that can be interpreted in the language of statistical physics.
  2. Directional statistics, the statistics of angles, directions and orientations. When combined with graphical models, this allows the formulation of efficient and flexible probabilistic models of protein structure.
  3. Probabilistic programming and deep learning: modern probabilistic programming languages such as STANEdwardPyro and pyMC3 offer unprecedented opportunities for formulating probabilistic model of protein structure. Incorporating deep learning architectures in these models allow combining Bayesian modelling with powerful machine learning methods.

Our probabilistic view on protein structure prediction, simulation and inference features prominently in the book "Bayesian methods in structural bioinformatics" (Springer, April, 2012)

Book cover

Research highlights


  • Our dynamic Bayesian network toolkit Mocapy++ (BMC Bioinformatics, 2010) - which was used to formulate the probabilistic models of protein and RNA structure -  is freely available from SourceForge.
  • PHAISTOS version 1.0, our Markov chain Monte Carlo framework for protein structure simulation, is available from Sourceforge

RNA sample

Group leader

  • Thomas Hamelryck, Associate Professor
  • Bioinformatics Centre, Department of Biology, University of Copenhagen (50%)
  • Image group, Department of Computer Science, University of Copenhagen (50%)
  • Address:
    Statistical Structural Biology Group
    Bioinformatics center, Department of Biology
    University of Copenhagen
    Ole Maaloes Vej 5
    DK-2200 Copenhagen N


  • In 2018, we got funding for applying probabilistic programming to automatic information extraction from invoices (Innovationsfonden) and ancestral protein structure prediction (Danish research council). 
  • Thomas Hamelryck is since August, 2016 employed 50% at the Department of Biology and 50% at the Department of Computer Science in order to promote new initiatives between the two departments. The emphasis lies on bioinformatics, machine learning, probabilistic programming and deep learning.
  • The structure group is part of the research initiative Dynamical Systems Interdisciplinary Network, led by Prof. Susanne Ditlevsen and funded by UCPH 2016. The project involves 7 teams from the University of Copenhagen. The network will consolidate existing inter-disciplinary collaboration and initiate new collaboration across faculties.




Selected peer reviewed articles (2005-now)

  1. Hamelryck T. (2005) An amino acid has two sides: A new 2D measure provides a different view of solvent exposure. Proteins Struct. Func. Bioinf. 59, 38-48. PDF
  2. Boomsma, W., Hamelryck, T. (2005) Full Cyclic Coordinate Descent: Solving the protein loop closure problem in Calpha space, BMC Bioinformatics 6:159 Abstract&PDF@BioMed
  3. Hamelryck, T., Kent, J., Krogh, A. (2006) Sampling realistic protein conformations using local structural bias. PLoS Comp. Biol. 2(9): e131 PDF@PLoS
  4. Paluszewski, M., Hamelryck, T. and Winter, P. (2006) Reconstructing protein structure from solvent exposure using Tabu Search. Algorithms Mol. Biol. 1:20. PDF@AlgMolBiol.
  5. Won, KJ., Hamelryck, T., Prugel-Bennett, A. and Krogh, A. (2007) An evolving method for learning HMM Structure: prediction of protein secondary structure. BMC Bioinformatics 8, 357 PDF@BMC Bioinformatics
  6. Boomsma, W., Mardia, KV., Taylor, CC., Ferkinghoff-Borg, J., Krogh, A. and Hamelryck, T. (2008) A generative, probabilistic model of local protein structure. Proc. Natl. Acad. Sci. USA 105, 8932-8937  PDF@PNAS, Video lecture by Wouter Boomsma
  7. Hamelryck, T. (2009) Probabilistic models and machine learning in structural bioinformatics. Statistical Methods in Medical Research Review. 18, 505-526.  PDF
  8. Cock, P., Antao, T., Chang, J., Chapman, B., Cox, C., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., de Hoon, M. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11),1422-1423. Free PDF@Bioinformatics
  9. Frellsen, J., Moltke, I., Thiim, M., Mardia, KV., Ferkinghoff-Borg, J., Hamelryck, T. (2009) A probabilistic  model of RNA conformational space. PLoS Comp. Biol. 5(6), e1000406 Free PDF@PLOS, Video of a presentation by Jes Frellsen
  10. Paluszewski, M., Hamelryck, T. (2010) Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks. BMC Bioinformatics 11:126. Free PDF@BMC 
  11. Harder, T., Boomsma, W., Paluszewski, M., Frellsen, J., Johansson, KE., Hamelryck, T. (2010) Beyond rotamers: A generative , probabilistic model of side chains in proteins. BMC Bioinformatics 11:306. Free PDF@BMC
  12. Stovgaard, K., Andreetta, C., Ferkinghoff-Borg, J., Hamelryck, T. (2010) Calculation of accurate small angle X-ray scattering curves from coarse-grained protein models. BMC Bioinformatics 11:429.  PDF@BMC Bioinformatics
  13. Hamelryck, T., Borg, M., Paluszewski, M., Paulsen, J.,  Frellsen, J., Andreetta, C., Boomsma, W. Bottaro, S., Ferkinghoff-Borg, J. (2010) Potentials of mean force for protein structure prediction vindicated, formalized and generalized. PLoS ONE 5(11): e13714. PDF@PLoS ONE , Preprint@arXiv
  14. Olsson, S., Boomsma, W., Frellsen, J., Bottaro, S., Harder, T., Ferkinghoff-Borg, J., Hamelryck, T. (2011) Generative probabilistic models extend the scope of inferential structure determination. J. Magn. Reson. 213(1), 182-6. PDF
  15. Harder, T., Borg, M., Boomsma, W., Røgen,  P., Hamelryck, T. (2012) Fast large-scale clustering of protein structures using Gauss integrals. Bioinformatics 28, 510-515. PDF@Bioinformatics.
  16. Bottaro, S., Boomsma, W., Johansson, K.E., Andreetta, C., Hamelryck, T., Ferkinghoff-Borg, J. (2012) Subtle Monte Carlo updates in dense molecular systems. J. Chem. Theory Comput. 8, 695–702. PDF@ACS
  17. Harder, T., Borg, M., Bottaro, S., Boomsma, W.,  Olsson, S., Ferkinghoff-Borg, J., Hamelryck, T. (2012)  An efficient null model for conformational fluctuations in proteins.  Structure, 20, 1028-1039. PDF@Structure.
  18. Mardia, KV., Kent, JT., Zhang, Z., Taylor, C., Hamelryck, T. (2012) Mixtures of concentrated multivariate sine distributions with applications to bioinformatics. J. Appl. Stat. 39, 2475-2492. PDF
  19. Johansson, KE., Hamelryck, T. (2013) A simple probabilistic model of multibody Interactions in proteins. Proteins 81, 1340-50.
  20. Boomsma, W., Frellsen, J., Harder, T., Bottaro, S., Johansson, KE., Tian, P., Stovgaard, K., Andreetta, C., Olsson, S., Valentin, J., Antonov, L., Christensen, A., Borg, M., Jensen, J., Lindorff-Larsen, K., Ferkinghoff-Borg, J., Hamelryck, T. (2013) PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure. J. Comput. Chem. 34, 1697-705. PDF
  21. Valentin, J., Andreetta, C., Boomsma, W., Bottaro, S., Ferkinghoff-Borg, J., Frellsen, J., Mardia, KV, Tian, P., Hamelryck, T. (2013) Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method. Proteins 82:288–299. PDF@Proteins
  22. Olsson, S., Frellsen, J., Boomsma, W., Mardia, KV., Hamelryck, T. (2013) Inference of structure ensembles of flexible biomolecules from sparse, averaged data. PLoS ONE. 8(11): e79439. Article@PLoS ONE
  23. Christensen, AS., Linnet, TE., Borg, M., Boomsma, W., Lindorff-Larsen, K., Hamelryck, T., Jensen, J. (2013) Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics. PLoS ONE. 8(12):e84123 . Article@PLoS ONE
  24. Christensen AS., Hamelryck T., Jensen JH. (2014) FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptide models. PeerJ. 2:e277 Article@PeerJ
  25. Olsson, S., Vögeli, B., Cavalli, A., Boomsma, W., Ferkinghoff-Borg, J., Lindorff-Larsen, K., Hamelryck, T. (2014) Probabilistic approach to the determination of native state ensembles of proteins. J. Chem. Theory Comput. 10(8):3484-3491. Article@JCTC
  26. Boomsma, W., Tian, P., Ferkinghoff-Borg, J., Hamelryck, T., Lindorff-Larsen, K. , Vendruscolo, M. (2014) Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts. Proc. Natl. Acad. Sci. USA. 111(38):13852-13857. Article@PNAS
  27. Bratholm, LA.,  Christensen, AS., Hamelryck, T., Jensen, JH. (2015) Bayesian inference of protein structure from chemical shift data. PeerJ. 3:e861; DOI 10.7717/peerj.861
  28. Antonov, LD., Olsson, S.,  Boomsma, W., Hamelryck, T. (2016) Bayesian inference of protein ensembles from SAXS data. Phys. Chem. Chem. Phys. DOI: 10.1039/C5CP04886A Article@PCCP
  29. Johansson, KE., Johansen, NT., Christensen, S., Horowitz, S., Bardwell, JC., Olsen, JG., Willemoës, M., Lindorff-Larsen, K., Ferkinghoff-Borg, J., Hamelryck, T. and Winther, JR. (2016) Computational redesign of thioredoxin is hypersensitive toward minor conformational changes in the backbone template. J. Mol. Biol., 428:4361-4377.
  30. Golden, M., Garcia-Portugues, E., Sørensen, M., Mardia, KV., 

    Hamelryck, T., and Hein, J. 

    (2017) A generative angular model of protein structure evolution. Mol. Biol. Evol. 34:2085–2100 Article@MBE
  31. Postic, G., Hamelryck, T., Chomilier, J., Stratmann, D. (2018) MyPMFs: a simple tool for creating statistical potentials to assess protein structural models. Biochimie. 151:37–41 Article@Biochimie
  32. Garcia-Portugues, E., Sorensen, M., Mardia, KV. and Hamelryck, T. (2019) Langevin diffusions on the torus: estimation and applications. Statistics and Computing. 29:1-22 Article@Stat Comput

Selected conference proceedings

  1. Won, KJ., Hamelryck, T., Prugel-Bennet, A., Krogh, A. (2005) Evolving hidden Markov models for protein secondary structure prediction. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pp. 33-40, Edinburgh. PDF
  2. Kent, J.T., Hamelryck, T. (2005) Using the Fisher-Bingham distribution in stochastic models for protein structure. In S. Barber, P.D. Baxter, K.V.Mardia, & R.E. Walls (Eds.), LASR 2005 - quantitative biology, shape analysis, and wavelets, pp. 57-60. Leeds university press, Leeds, UK. PDF@LASR
  3. Boomsma, W., Kent, J.T., Mardia, K.V., Taylor, C.C. & Hamelryck, T. (2006) Graphical models and directional statistics capture protein structure. In S. Barber, P.D. Baxter, K.V.Mardia, & R.E. Walls (Eds.), LASR 2006 - Interdisciplinary statistics and bioinformatics, pp. 91-94. Leeds university press, UK. PDF@LASR
  4. Boomsma, W., Borg, M., Frellsen, J., Harder, T., Stovgaard, K., Ferkinghoff-Borg, J., Krogh, A., Mardia, KV. and Hamelryck, T. (2008) PHAISTOS: protein structure prediction using a probabilistic model of local structure.  Proceedings of CASP8, Cagliari, Sardinia, Italy, December 3-7 2008. pp 82-83. PDF@CASP8
  5. Borg, M., Mardia, KV., Boomsma, W., Frellsen, J., Harder, T., Stovgaard, K., Ferkinghoff-Borg, J., Røgen, P., Hamelryck, T. A probabilistic approach to protein structure prediction: PHAISTOS in CASP9. LASR 2009 - Statistical tools for challenges in bioinformatics, pp. 65-70. Leeds university press, Leeds, UK. PDF@LASR
  6. Paulsen, J., Paluszewski, M., Mardia, KV., Hamelryck, T. (2010) A probabilistic model of hydrogen bond geometry in proteins. LASR 2010 - High-throughput sequencing, proteins and statistics, pp. 61-64. Leeds university press, Leeds, UK. PDF@LASR
  7. Mardia, KV.,  Frellsen, J.,  Borg, M.,  Ferkinghoff-Borg, J., Hamelryck, T. (2011) A statistical view on the reference ratio method, LASR 2011 - High-throughput sequencing, proteins and statistics, pp. 55-61. Leeds university press, Leeds, UK. PDF@LASR
  8. Antonov, L., Andreetta, C., Hamelryck, T.  (2012) An efficient parallel GPU evaluation of small angle X-ray scattering profiles. In  BIOSTEC 2012, 5th Int'l Joint Conf. on Biomedical Engineering Systems and Technologies,  102-108, Algarve, Portugal. PDF
  9. Hamelryck, T., Haslett, J., Mardia, K., Kent, JT., Valentin, J., Frellsen, J., Ferkinghoff-Borg, J. (2013) On the reference ratio method and its application to statistical protein structure prediction. LASR 2013 - Statistical models and methods for non-Euclidean data with current scientific applications. Leeds university press, Leeds, UK. PDF@LASR
  10. Olsson, S., Hamelryck, T. (2013) On the significance of the reference ratio method in inferential structure determination of biomolecules. LASR 2013 - Statistical models and methods for non-Euclidean data with current scientific applications. Leeds university press, Leeds, UK. PDF@LASR
  11. Frellsen, J., Hamelryck, T., Ferkinghoff-Borg, J. (2013) Combining the multicanonical ensemble with generative probabilistic models of local biomolecular structure. 59th ISI World Statistics Congress. Hong Kong, China. 25-30 August, 2013. PDF
  12. Al-Sibahi, AS., Hamelryck, T., Henglein, F. (2018) Probabilistic programming for voucher information extraction. PROBPROG 2018,  MIT, Cambridge, MA, USA, 4-6 October, 2018.

Books and book chapters

  1. Chang, J.,  Chapman, B.,  Friedberg, I., Hamelryck, T., de Hoon, M., Cock, P., Antao, T., Talevich, E., Wilczyński, B. (2012) Biopython tutorial and cookbook. Biopython project.
  2. Boomsma, W., Bottaro, S., Hamelryck, T., Frellsen, J., Andreetta, C., Borg, M., Harder, T., Johansson, KE., Stovgaard, S., Tian, P. (2012) Phaistos user manual (version 1.0). University of Copenhagen. PDF@SourceForge
  3. Paluszewski, M., Frellsen, J., Hamelryck, T.  (2009) Mocapy++: A C++ toolkit for inference and learning in dynamic Bayesian networks. University of Copenhagen. PDF
  4. Hamelryck, T., Mardia, KV., Ferkinghoff-Borg, J., Editors. (2012) Bayesian methods in structural bioinformatics. Book in the Springer series "Statistics for biology and health", 385 pages, 13 chapters. Springer Verlag, March, 2012. Book description at Springer.
  5. Hamelryck, T. (2012) An overview of Bayesian inference and graphical models.  In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  6. Borg, M., Hamelryck, T. Ferkinghoff-Borg, J. (2012) On the physical relevance and statistical interpretation of knowledge based potentials.  In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  7. Frellsen, J., Mardia, KV., Borg, M., Ferkinghoff-Borg, J., Hamelryck, T. (2012) Towards a probabilistic model of protein structure: The reference ratio method. In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  8. Boomsma, W., Frellsen, J., Hamelryck, T. (2012) Probabilistic models of local biomolecular structure and their applications. In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  9. Antonov, LD., Andreetta, C., Hamelryck, T. (2013) Parallel GPGPU evaluation of small angle X-ray scattering profiles in a Markov chain Monte Carlo framework. In J. Gabriel et al. (eds.). BIOSTEC 2012, CCIS, 357, 222-235. PDF@Springer
  10. Hamelryck, T., Boomsma, W., Ferkinghoff-Borg, J., Foldager, J., Frellsen, J., Haslett, J., Theobald, D. (2015). Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. In Geometry Driven Statistics, Wiley.

Some public outreach

  1. One step closer to green chemistry and improved pharmaceuticals. Press release, KU, June, 2008.
  2. Designerenzymer til grøn kemi. Press release,  Det Frie Forskningsråd (DFF), June, 2009.
  3. Machine Learning & Molecules conference, Copenhagen, November 9-10th 2017
  4. Flere unge skal have en fremtid med AI og Machine Learning. TechSavvy, 2018