Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach

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Standard

Integrating Molecular Simulation and Experimental Data : A Bayesian/Maximum Entropy Reweighting Approach. / Bottaro, Sandro; Bengtsen, Tone; Lindorff-Larsen, Kresten.

Structural Bioinformatics: Methods and Protocols. red. / Zoltán Gáspári. Humana Press, 2020. s. 219-240 (Methods in Molecular Biology, Bind 2112).

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Harvard

Bottaro, S, Bengtsen, T & Lindorff-Larsen, K 2020, Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. i Z Gáspári (red.), Structural Bioinformatics: Methods and Protocols. Humana Press, Methods in Molecular Biology, bind 2112, s. 219-240. https://doi.org/10.1007/978-1-0716-0270-6_15

APA

Bottaro, S., Bengtsen, T., & Lindorff-Larsen, K. (2020). Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. I Z. Gáspári (red.), Structural Bioinformatics: Methods and Protocols (s. 219-240). Humana Press. Methods in Molecular Biology Bind 2112 https://doi.org/10.1007/978-1-0716-0270-6_15

Vancouver

Bottaro S, Bengtsen T, Lindorff-Larsen K. Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. I Gáspári Z, red., Structural Bioinformatics: Methods and Protocols. Humana Press. 2020. s. 219-240. (Methods in Molecular Biology, Bind 2112). https://doi.org/10.1007/978-1-0716-0270-6_15

Author

Bottaro, Sandro ; Bengtsen, Tone ; Lindorff-Larsen, Kresten. / Integrating Molecular Simulation and Experimental Data : A Bayesian/Maximum Entropy Reweighting Approach. Structural Bioinformatics: Methods and Protocols. red. / Zoltán Gáspári. Humana Press, 2020. s. 219-240 (Methods in Molecular Biology, Bind 2112).

Bibtex

@inbook{f0358468aa1b42e19e361e610ef4e89b,
title = "Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach",
abstract = "We describe a Bayesian/Maximum entropy (BME) procedure and software to construct a conformational ensemble of a biomolecular system by integrating molecular simulations and experimental data. First, an initial conformational ensemble is constructed using, for example, Molecular Dynamics or Monte Carlo simulations. Due to potential inaccuracies in the model and finite sampling effects, properties predicted from simulations may not agree with experimental data. In BME we use the experimental data to refine the simulation so that the new conformational ensemble has the following properties: (1) the calculated averages are close to the experimental values taking uncertainty into account and (2) it maximizes the relative Shannon entropy with respect to the original simulation ensemble. The output of this procedure is a set of optimized weights that can be used to calculate other properties and distributions of these. Here, we provide a practical guide on how to obtain and use such weights, how to choose adjustable parameters and discuss shortcomings of the method.",
keywords = "Conformational ensemble, Integrative structural biology, MD simulations",
author = "Sandro Bottaro and Tone Bengtsen and Kresten Lindorff-Larsen",
year = "2020",
doi = "10.1007/978-1-0716-0270-6_15",
language = "English",
isbn = "978-1-0716-0269-0",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "219--240",
editor = "Zolt{\'a}n G{\'a}sp{\'a}ri",
booktitle = "Structural Bioinformatics",
address = "United States",

}

RIS

TY - CHAP

T1 - Integrating Molecular Simulation and Experimental Data

T2 - A Bayesian/Maximum Entropy Reweighting Approach

AU - Bottaro, Sandro

AU - Bengtsen, Tone

AU - Lindorff-Larsen, Kresten

PY - 2020

Y1 - 2020

N2 - We describe a Bayesian/Maximum entropy (BME) procedure and software to construct a conformational ensemble of a biomolecular system by integrating molecular simulations and experimental data. First, an initial conformational ensemble is constructed using, for example, Molecular Dynamics or Monte Carlo simulations. Due to potential inaccuracies in the model and finite sampling effects, properties predicted from simulations may not agree with experimental data. In BME we use the experimental data to refine the simulation so that the new conformational ensemble has the following properties: (1) the calculated averages are close to the experimental values taking uncertainty into account and (2) it maximizes the relative Shannon entropy with respect to the original simulation ensemble. The output of this procedure is a set of optimized weights that can be used to calculate other properties and distributions of these. Here, we provide a practical guide on how to obtain and use such weights, how to choose adjustable parameters and discuss shortcomings of the method.

AB - We describe a Bayesian/Maximum entropy (BME) procedure and software to construct a conformational ensemble of a biomolecular system by integrating molecular simulations and experimental data. First, an initial conformational ensemble is constructed using, for example, Molecular Dynamics or Monte Carlo simulations. Due to potential inaccuracies in the model and finite sampling effects, properties predicted from simulations may not agree with experimental data. In BME we use the experimental data to refine the simulation so that the new conformational ensemble has the following properties: (1) the calculated averages are close to the experimental values taking uncertainty into account and (2) it maximizes the relative Shannon entropy with respect to the original simulation ensemble. The output of this procedure is a set of optimized weights that can be used to calculate other properties and distributions of these. Here, we provide a practical guide on how to obtain and use such weights, how to choose adjustable parameters and discuss shortcomings of the method.

KW - Conformational ensemble

KW - Integrative structural biology

KW - MD simulations

U2 - 10.1007/978-1-0716-0270-6_15

DO - 10.1007/978-1-0716-0270-6_15

M3 - Book chapter

C2 - 32006288

AN - SCOPUS:85078829996

SN - 978-1-0716-0269-0

T3 - Methods in Molecular Biology

SP - 219

EP - 240

BT - Structural Bioinformatics

A2 - Gáspári, Zoltán

PB - Humana Press

ER -

ID: 238000139