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

Thomas Wim Hamelryck; Wouter Krogh Boomsma; Jesper Ferkinghoff-Borg; Jesper Illemann Foldager; Jes Frellsen; John Haslett; Douglas Theobald

doi:10.1002/9781118866641.ch18

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

Thomas Wim Hamelryck^*, Wouter Krogh Boomsma, Jesper Ferkinghoff-Borg, Jesper Illemann Foldager, Jes Frellsen, John Haslett, Douglas Theobald

^*Corresponding author for this work

4 Citations (Scopus)

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

Original language	English
Title of host publication	Geometry driven statistics
Editors	Ian L. Dryden, John T. Kent
Number of pages	21
Publisher	Wiley
Publication date	2015
Pages	356-376
Chapter	18
ISBN (Print)	9781118866573
ISBN (Electronic)	9781118866641
DOIs	https://doi.org/10.1002/9781118866641.ch18
Publication status	Published - 2015

Keywords

Directional statistics
Dynamic Bayesian networks
Probability kinematics
Protein structure
Reference ratio method

Access to Document

10.1002/9781118866641.ch18

Cite this

Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. / Hamelryck, Thomas Wim ; Boomsma, Wouter Krogh; Ferkinghoff-Borg, Jesper et al.
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

@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",

}

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 -

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

Abstract

Keywords

Access to Document

Fingerprint

Cite this