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 Citationer (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.

Originalsprog	Engelsk
Artikelnummer	898
Tidsskrift	Entropy
Vol/bind	21
Udgave nummer	9
Antal sider	17
ISSN	1099-4300
DOI	https://doi.org/10.3390/e21090898
Status	Udgivet - 1 maj 2020

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10.3390/e21090898Licens: CC BY

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical ShiftsForlagets udgivne version, 1,92 MBLicens: CC BY

Citationsformater

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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

U2 - 10.3390/e21090898

DO - 10.3390/e21090898

M3 - Journal article

SN - 1099-4300

VL - 21

JO - Entropy

JF - Entropy

IS - 9

M1 - 898

ER -

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

Abstract

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