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Accelerating Molecular Dynamics Simulations for Drug Discovery

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Computational Drug Discovery and Design

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

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Abstract

Accurate prediction of ligand binding thermodynamics and kinetics is crucial in drug design. However, it remains challenging for conventional molecular dynamics (MD) simulations due to sampling issues. Gaussian accelerated MD (GaMD) is an enhanced sampling method that adds a harmonic boost to overcome energy barriers, which has demonstrated significant benefits in exploring protein-ligand interactions. Especially, the ligand GaMD (LiGaMD) applies a selective boost potential to the ligand nonbonded potential energy, significantly improving sampling for ligand binding and dissociation. Furthermore, a selective boost potential is applied to the potential of both ligand and protein residues around binding pocket in LiGaMD2 to further increase the sampling of protein-ligand interaction. LiGaMD and LiGaMD2 simulations could capture repetitive ligand binding and unbinding events within microsecond simulations, allowing to simultaneously characterize ligand binding thermodynamics and kinetics, which is expected to greatly facilitate drug design. In this chapter, we provide a brief review of the status of LiGaMD in drug discovery and outline its usage.

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Author information

Authors and Affiliations

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

    Kushal Koirala, Keya Joshi, Victor Adediwura, Jinan Wang, Hung Do & Yinglong Miao

Authors
  1. Kushal Koirala
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  2. Keya Joshi
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  3. Victor Adediwura
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  4. Jinan Wang
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  5. Hung Do
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  6. Yinglong Miao
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Corresponding author

Correspondence to Yinglong Miao .

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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Koirala, K., Joshi, K., Adediwura, V., Wang, J., Do, H., Miao, Y. (2024). Accelerating Molecular Dynamics Simulations for Drug Discovery. 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_11

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

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