Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

Ramon Crehuet; Pedro J. Buigues; Xavier Salvatella; Kresten Lindorff-Larsen

doi:10.3390/e21090898

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

Ramon Crehuet, Pedro J. Buigues, Xavier Salvatella, Kresten Lindorff-Larsen

Biomolecular Sciences

10 Citations (Scopus)

7 Downloads (Pure)

Abstract

Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

Original language	English
Article number	898
Journal	Entropy
Volume	21
Issue number	9
Number of pages	17
ISSN	1099-4300
DOIs	https://doi.org/10.3390/e21090898
Publication status	Published - 1 May 2020

Keywords

Bayesian methods
maximum entropy
chemical shifts
intrinsically disordered proteins
protein ensembles
structural modelling
NMR
molecular dynamics

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10.3390/e21090898Licence: CC BY

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical ShiftsFinal published version, 1.92 MBLicence: CC BY

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title = "Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts",

abstract = "Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.",

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T1 - Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

AU - Crehuet, Ramon

AU - Buigues, Pedro J.

AU - Salvatella, Xavier

AU - Lindorff-Larsen, Kresten

PY - 2020/5/1

Y1 - 2020/5/1

N2 - Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

AB - Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

KW - Bayesian methods

KW - maximum entropy

KW - chemical shifts

KW - intrinsically disordered proteins

KW - protein ensembles

KW - structural modelling

KW - NMR

KW - molecular dynamics

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M3 - Journal article

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JO - Entropy

JF - Entropy

IS - 9

M1 - 898

ER -

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

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Keywords

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