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

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Structural Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2112))

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.

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Acknowledgements

We thank Dr. Alexander Lemak and Prof. Cheryl H. Arrowsmith for sharing the SAXS data on sf3636. We also thank Yong Wang, Mustapha Carab Ahmed, and Andreas Haahr Larsen for input and testing of BME. The research and development described here were supported by a grant from The Velux Foundations, a Hallas-Møller Stipend from the Novo Nordisk Foundation, and the Lundbeck Foundation BRAINSTRUC initiative.

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Authors and Affiliations

  1. Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark

    Sandro Bottaro, Tone Bengtsen & Kresten Lindorff-Larsen

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  1. Sandro Bottaro
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  2. Tone Bengtsen
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  3. Kresten Lindorff-Larsen
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Correspondence to Kresten Lindorff-Larsen .

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Editors and Affiliations

  1. Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary

    Zoltán Gáspári

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Bottaro, S., Bengtsen, T., Lindorff-Larsen, K. (2020). Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. In: Gáspári, Z. (eds) Structural Bioinformatics. Methods in Molecular Biology, vol 2112. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0270-6_15

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  • DOI: https://doi.org/10.1007/978-1-0716-0270-6_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0269-0

  • Online ISBN: 978-1-0716-0270-6

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