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GRAMM Web Server for Protein Docking

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

Abstract

Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein–protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.

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Acknowledgments

This study was supported by NIH grant R01GM074255 and NSF grant DBI1917263. The authors wish to acknowledge the contribution of Andrey Tovchigrechko who wrote the previous version of the server (GRAMM-X).

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Authors and Affiliations

  1. Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA

    Amar Singh, Matthew M. Copeland, Petras J. Kundrotas & Ilya A. Vakser

Authors
  1. Amar Singh
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  2. Matthew M. Copeland
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  3. Petras J. Kundrotas
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  4. Ilya A. Vakser
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Corresponding authors

Correspondence to Petras J. Kundrotas or Ilya A. Vakser .

Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Singh, A., Copeland, M.M., Kundrotas, P.J., Vakser, I.A. (2024). GRAMM Web Server for Protein Docking. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_5

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

  • eBook Packages: Springer Protocols

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