Generative probabilistic models extend the scope of inferential structure determination

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 - 2011/12

Y1 - 2011/12

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

SN - 1090-7807

VL - 213

SP - 182

EP - 186

JO - Journal of Magnetic Resonance

JF - Journal of Magnetic Resonance

IS - 1

ER -