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Integrative Structural Biology with Hybrid Methods pp 199–217Cite as

Hybrid Methods for Macromolecular Modeling by Molecular Mechanics Simulations with Experimental Data

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1105))

Abstract

Hybrid approaches for the modeling of macromolecular complexes that combine computational molecular mechanics simulations with experimental data are discussed. Experimental data for biological molecular structures are often low-resolution, and thus, do not contain enough information to determine the atomic positions of molecules. This is especially true when the dynamics of large macromolecules are the focus of the study. However, computational modeling can complement missing information. Significant increase in computational power, as well as the development of new modeling algorithms allow us to model structures of biological macromolecules reliably, using experimental data as references. We review the basics of molecular mechanics approaches, such as atomic model force field, and coarse-grained models, molecular dynamics simulation and normal mode analysis and describe how they could be used for flexible fitting hybrid modeling with experimental data, especially from cryo-EM and SAXS.

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Acknowledgements

We thank Sandhya P. Tiwari and Ashutosh Srivastava for carefully reading the manuscript and providing comments. This work was supported by FOCUS for Establishing Supercomputing Center of Excellence, JSPS KAKENHI Grant Number 17K07305, 16K07286, 26119006, 15K21711 and RIKEN Dynamic Structural Biology Project.

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Authors and Affiliations

  1. RIKEN R-CCS, Kobe, Hyōgo, Japan

    Osamu Miyashita & Florence Tama

  2. Department of Physics and ITbM, Nagoya University, Nagoya, Japan

    Florence Tama

Authors
  1. Osamu Miyashita
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  2. Florence Tama
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Correspondence to Florence Tama .

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Editors and Affiliations

  1. Institute for Protein Research, Osaka University, Suita, Osaka, Japan

    Haruki Nakamura

  2. European Bioinformatics Institute, Cambridgeshire, UK

    Gerard Kleywegt

  3. Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Stephen K. Burley

  4. Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA

    John L. Markley

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Miyashita, O., Tama, F. (2018). Hybrid Methods for Macromolecular Modeling by Molecular Mechanics Simulations with Experimental Data. In: Nakamura, H., Kleywegt, G., Burley, S., Markley, J. (eds) Integrative Structural Biology with Hybrid Methods. Advances in Experimental Medicine and Biology, vol 1105. Springer, Singapore. https://doi.org/10.1007/978-981-13-2200-6_13

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