Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

Proteins, physics and probability kinematics : a Bayesian formulation of the protein folding problem. / Hamelryck, Thomas Wim; Boomsma, Wouter Krogh; Ferkinghoff-Borg, Jesper; Foldager, Jesper Illemann; Frellsen, Jes; Haslett, John; Theobald, Douglas.

Geometry driven statistics. ed. / Ian L. Dryden; John T. Kent. Wiley, 2015. p. 356-376.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Hamelryck, TW, Boomsma, WK, Ferkinghoff-Borg, J, Foldager, JI, Frellsen, J, Haslett, J & Theobald, D 2015, Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. in IL Dryden & JT Kent (eds), Geometry driven statistics. Wiley, pp. 356-376. https://doi.org/10.1002/9781118866641.ch18

APA

Hamelryck, T. W., Boomsma, W. K., Ferkinghoff-Borg, J., Foldager, J. I., Frellsen, J., Haslett, J., & Theobald, D. (2015). Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. In I. L. Dryden, & J. T. Kent (Eds.), Geometry driven statistics (pp. 356-376). Wiley. https://doi.org/10.1002/9781118866641.ch18

Vancouver

Hamelryck TW, Boomsma WK, Ferkinghoff-Borg J, Foldager JI, Frellsen J, Haslett J et al. Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. In Dryden IL, Kent JT, editors, Geometry driven statistics. Wiley. 2015. p. 356-376 https://doi.org/10.1002/9781118866641.ch18

Author

Hamelryck, Thomas Wim ; Boomsma, Wouter Krogh ; Ferkinghoff-Borg, Jesper ; Foldager, Jesper Illemann ; Frellsen, Jes ; Haslett, John ; Theobald, Douglas. / Proteins, physics and probability kinematics : a Bayesian formulation of the protein folding problem. Geometry driven statistics. editor / Ian L. Dryden ; John T. Kent. Wiley, 2015. pp. 356-376

Bibtex

@inbook{48bca8314ac544a0a66f064a18579671,
title = "Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem",
abstract = "Proteins are biomolecules that are of great importance in science, biotechnology and medicine. Their function relies heavily on their three-dimensional shape, which in turn follows from their amino acid sequence. Therefore, there is great interest in modelling the three-dimensional structure of proteins in silico given their sequence. We discuss the formulation of a tractable probabilistic model of protein structure that features atomic detail and can be used for protein structure prediction. The model unites dynamic Bayesian networks and directional statistics to cover the short-range features of proteins. Long-range features are added by making use of probability kinematics - a little known variant of Bayesian belief updating first proposed by the probability theorist Richard Jeffrey in the 1950s. The method we describe can be generalized to formulate tractable probabilistic models that involve high dimensionality and need to cover multiple scales",
keywords = "Directional statistics, Dynamic Bayesian networks, Probability kinematics, Protein structure, Reference ratio method",
author = "Hamelryck, {Thomas Wim} and Boomsma, {Wouter Krogh} and Jesper Ferkinghoff-Borg and Foldager, {Jesper Illemann} and Jes Frellsen and John Haslett and Douglas Theobald",
year = "2015",
doi = "10.1002/9781118866641.ch18",
language = "English",
isbn = "9781118866573",
pages = "356--376",
editor = "Dryden, {Ian L.} and Kent, {John T.}",
booktitle = "Geometry driven statistics",
publisher = "Wiley",
address = "United States",

}

RIS

TY - CHAP

T1 - Proteins, physics and probability kinematics

T2 - a Bayesian formulation of the protein folding problem

AU - Hamelryck, Thomas Wim

AU - Boomsma, Wouter Krogh

AU - Ferkinghoff-Borg, Jesper

AU - Foldager, Jesper Illemann

AU - Frellsen, Jes

AU - Haslett, John

AU - Theobald, Douglas

PY - 2015

Y1 - 2015

N2 - Proteins are biomolecules that are of great importance in science, biotechnology and medicine. Their function relies heavily on their three-dimensional shape, which in turn follows from their amino acid sequence. Therefore, there is great interest in modelling the three-dimensional structure of proteins in silico given their sequence. We discuss the formulation of a tractable probabilistic model of protein structure that features atomic detail and can be used for protein structure prediction. The model unites dynamic Bayesian networks and directional statistics to cover the short-range features of proteins. Long-range features are added by making use of probability kinematics - a little known variant of Bayesian belief updating first proposed by the probability theorist Richard Jeffrey in the 1950s. The method we describe can be generalized to formulate tractable probabilistic models that involve high dimensionality and need to cover multiple scales

AB - Proteins are biomolecules that are of great importance in science, biotechnology and medicine. Their function relies heavily on their three-dimensional shape, which in turn follows from their amino acid sequence. Therefore, there is great interest in modelling the three-dimensional structure of proteins in silico given their sequence. We discuss the formulation of a tractable probabilistic model of protein structure that features atomic detail and can be used for protein structure prediction. The model unites dynamic Bayesian networks and directional statistics to cover the short-range features of proteins. Long-range features are added by making use of probability kinematics - a little known variant of Bayesian belief updating first proposed by the probability theorist Richard Jeffrey in the 1950s. The method we describe can be generalized to formulate tractable probabilistic models that involve high dimensionality and need to cover multiple scales

KW - Directional statistics

KW - Dynamic Bayesian networks

KW - Probability kinematics

KW - Protein structure

KW - Reference ratio method

U2 - 10.1002/9781118866641.ch18

DO - 10.1002/9781118866641.ch18

M3 - Book chapter

AN - SCOPUS:84982239355

SN - 9781118866573

SP - 356

EP - 376

BT - Geometry driven statistics

A2 - Dryden, Ian L.

A2 - Kent, John T.

PB - Wiley

ER -

ID: 167913977