TY - JOUR
T1 - Generative probabilistic models extend the scope of inferential structure determination
AU - Olsson, Simon
AU - Boomsma, Wouter Krogh
AU - Frellsen, Jes
AU - Bottaro, Sandro
AU - Harder, Tim Philipp
AU - Ferkinghoff-Borg, Jesper
AU - Hamelryck, Thomas Wim
N1 - Copyright © 2011 Elsevier Inc. All rights reserved.
PY - 2011
Y1 - 2011
N2 - Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inference. In this case, the forcefields are brought in as a prior distribution in the form of a Boltzmann factor. Due to high computational cost, the approach has been only sparsely applied in practice. Here, we demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure in structure determination from experimental data.
AB - Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inference. In this case, the forcefields are brought in as a prior distribution in the form of a Boltzmann factor. Due to high computational cost, the approach has been only sparsely applied in practice. Here, we demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure in structure determination from experimental data.
KW - Algorithms
KW - Bayes Theorem
KW - Electromagnetic Fields
KW - Models, Molecular
KW - Models, Statistical
KW - Nuclear Magnetic Resonance, Biomolecular
KW - Protein Conformation
KW - Protein Structure, Tertiary
KW - Proteins
KW - Temperature
U2 - 10.1016/j.jmr.2011.08.039
DO - 10.1016/j.jmr.2011.08.039
M3 - Letter
C2 - 21993764
VL - 213
SP - 182
EP - 186
JO - Journal of Magnetic Resonance
JF - Journal of Magnetic Resonance
SN - 1090-7807
IS - 1
ER -