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 -