Protein structure ensembles from mathematical models – Bioinformatics Centre - University of Copenhagen

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Protein structure ensembles from mathematical models - with application to Parkinson's alpha-synuclein

 

 

 

 

 

 

 

 

 

 

 

Principle investigator:

Thomas Hamelryck  

Funding

This project is funded by the Danish Agency for Science, Technology and Innovation (Det Frie Forskningsråd, Teknologi og Produktion). The total amount of the grant is 4.280.930 DKK.   

Summary 

Understanding the 3D molecular structure of proteins is of enormous importance in science, medicine and biotechnology. When determining the 3D structure of a protein using biophysical methods, it is often assumed that a protein molecule has a single, specific shape. Yet in reality, many proteins adopt a number of radically different conformations, that can interchange dynamically. Such a set of conformations is called an ensemble. It is precisely the ensemble aspect of protein structure that plays a major role in important diseases such as Parkinson's, type II diabetes or Alzheimer's. Currently, there are few methods that can handle such ensembles, and the available methods are suboptimal, ad hoc and heuristic. We propose to develop a statistically rigorous and computationally efficient method to determine the structure of protein ensembles, based on recent innovations developed at the Bioinformatics center. The method will be applied to the protein alpha -synuclein, which plays a key role in Parkinson's disease, and for which unique in house data is available. Without the development of powerful novel methods, the valuable information hidden in the alpha-synuclein data will remain inaccessible.

 Publications


  • 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
  • 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
  • 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. To be published in March, 2012. Book description at Springer.
  • Hamelryck, T. An overview of Bayesian inference and graphical models. (2012) In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  • Borg, M., Hamelryck, T. Ferkinghoff-Borg, J. 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.
  • Frellsen, J., Mardia, KV., Borg, M., Ferkinghoff-Borg, J., Hamelryck, T. Towards a probabilistic model of protein structure: The reference ratio method. (2012) In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  • Boomsma, W., Frellsen, J., Hamelryck, T. Probabilistic models of local biomolecular structure and their applications. (2012) In T. Hamelryck et al. (eds). Bayesian methods in structural bioinformatics. Statistics for Biology and Health. Springer-Verlag, Berlin, Heidelberg.
  • An efficient parallel GPU evaluation of small angle X-ray scattering profiles. Antonov, L., Andreetta, C., Hamelryck, T. In  BIOSTEC 2012, 5th Int'l Joint Conf. on Biomedical Engineering Systems and Technologies,  Algarve, Portugal.