Generative probabilistic models extend the scope of inferential structure determination

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Generative probabilistic models extend the scope of inferential structure determination

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09-06-2011, 08:17 PM

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Generative probabilistic models extend the scope of inferential structure determination

Generative probabilistic models extend the scope of inferential structure determination

Publication year: 2011
Source: Journal of Magnetic Resonance, In Press, Accepted Manuscript, Available online 6 September 2011

Simon, Olsson , Wouter, Boomsma , Jes, Frellsen , Sandro, Bottaro , Tim, Harder , ...

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...

Graphical abstract

*Graphical abstract:**Highlights:*? Generative probabilistic models in inferential structure determination are examined. ? We observe increased efficiency and precision compared to previous studies. ? The scope of inferential structure determination has been extended to practioners. ? The scope of inferential structure determination has been extended to practioners.

Source: Journal of Magnetic Resonance

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