Bioinformatics Centre > Research > Structural bioinformatics

One of the major unsolved problems in modern day molecular biology 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 Structural Bioinformatics group focuses on protein structure prediction, protein design and protein structure determination from experimental data (NMR, SAXS), including data obtained from protein ensembles. 

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. These probabilistic models are mainly based on two key ingredients: graphical models (including dynamic Bayesian networks), which are powerful machine learning methods that can be interpreted in the language of statistical physics, and directional statistics, the statistics of angles, directions and orientations. We also extended our statistical approach to RNA 3D structure.

Our probabilistic view on protein structure prediction, simulation and inference is expounded in the recently published book entitled "Bayesian methods in structural bioinformatics" (Springer, April, 2012). The above innovations are available in PHAISTOS version 1.0, our Markov chain Monte Carlo software framework for protein structure simulation.

Research highlights

• For more information on our statistical approach to protein structure prediction, see our articles on probabilistic models of protein structure that appeared in PLoS computational Biology (2006) and PNAS (2008), and the review on probabilistic methods in structural bioinformatics (2009). 
• Our probabilistic model of side chain conformations, Basilisk (BMC Bioinformatics, 2010), abolishes the need for the use of discrete side chain rotamers in conformational sampling.
• We also developed a probabilistic model of RNA structure in atomic detail, see PLoS Computational Biology (2009).
• Our dynamic Bayesian network toolkit Mocapy++ (BMC Bioinformatics, 2010) - which was used to formulate these models -  is freely available from SourceForge.
• After 20 years of controversy and countless publications on the subject, we finally settle the discussion on the validity of so-called potentials of mean force as proposed by Sippl in 1990. Moreover, our results point to important new applications and solve the classic problem of the reference state. See our recent article in PLoS ONE (2010).
• We recently developed a probabilistic protein structure determination method for NMR (NOE) data, using our probabilistic models of protein structure as prior distributions, resulting in models of superior quality (J. Magn. Reson, 2011).
• PHAISTOS version 1.0, our Markov chain Monte Carlo framework for protein structure simulation, is now available from Sourceforge . 

People

Group leader

• Thomas Hamelryck, Associate professor, Group leader

Postdocs

•  None at the moment.

PhD students

• Lubo Antonov
• Simon Olsson
• Jan Valentin

Former members

• Martin Paluszewski, Postdoc
• Wouter Boomsma, PhD student and postdoc
• Tim Harder, PhD student
• Kristoffer E. Johansson, visiting PhD student (supervisor Jakob R. Winther)
• Kasper Stovgaard, PhD student
• Mikael Borg, Postdoc
• Christian Andreetta, PhD student
• Jes Frellsen, PhD student and postdoc

News

• 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
• Lubo Antonov joins the group as PhD student (November, 2012).
• "Bayesian methods in structural bioinformatics" is published by Springer (April, 2012) and available from Amazon.

Funding

• Danish Research Council for Technology and Production Sciences (FTP), "Data driven protein structure prediction" Feb 2007-Feb 2010. 3,800,000 DKK (510,200 EUR).
• Danish Research Council for Strategic Research (NABIIT),  "Simulating proteins on a millisecond time-scale" Sep 2006-Feb 2010. 7,800,000 DKK (1,047,037 EUR). PI: Prof Anders Krogh. In collaboration with Novozymes .
• Danish Research Council for Technology and Production Sciences (FTP), "Protein design: Development of molecular biology and bioinformatics tools" Sep. 2007-Sep. 2010. 5,600,000 DKK (750,900 EUR). Partner in a project of Jakob R. Winther, department of biology, university of Copenhagen.
• Danish Research Council for Technology and Production Sciences (FTP), "Protein structure ensembles from mathematical models - with application to Parkinson's alpha-synuclein" , April 2010-August 2013, 4.280.930 DKK
• Dynamical Systems Interdisciplinary Network, UCPH 2016 initiative (PI: Prof. Susanne Ditlevsen), one PhD student and one postdoc year.

In the press

• One step closer to green chemistry and improved pharmaceuticals. Press release, KU, June, 2008.
• Designerenzymer til grøn kemi. Press release,  Det Frie Forskningsråd (DFF), June, 2009.

Teaching

• Structural bioinformatics course (7.5 ECTS), Block 2, Winter 2012.
• Machine learning course: Advanced topics in data modelling (7.5 ECTS), Block 3, Spring 2012
  

Publications

Articles

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. Antonov, LD., Andreetta, C., Hamelryck, T. (2012) Parallel GPGPU evaluation of small angle X-ray scattering profiles in a Markov chain Monte Carlo framework. Lecture notes in computer science. Accepted.
20. Johansson, KE., Hamelryck, T. (2013) A simple probabilistic model of multibody Interactions in proteins. Proteins. Accepted.
    
21. 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. Accepted.

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. Free PDF@LASR 2009
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

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. PDF@Biopython.org
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.

Collaborations

Denmark

• Wouter Boomsma, Section for Biomolecular Sciences, University of Copenhagen
• Prof. Jan H. Jensen,  Department of chemistry, University of Copenhagen
• Prof. Susanne Ditlevsen, Prof. Michael Sørensen, Department of Mathematical Sciences, University of Copenhagen. Funded by the Dynamical Systems Interdisciplinary Network, UCPH 2016.

International

• Prof. Kanti V. Mardia and Prof. John T. Kent University of Leeds, UK
• Prof. Karolin Luger, Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biology, Colorado State University, USA