4.7 Article

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices

Journal

JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 62, Issue 14, Pages 3415-3425

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.2c00433

Keywords

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Funding

  1. OTKA [K 134260, K 135150]
  2. Ministry of Innovation and Technology of Hungary [PD 134416]
  3. National Research, Development and Innovation Fund
  4. Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences
  5. New National Excellence Program of the Ministry for Innovation and Technology [UUNKP-21-5]
  6. University of Florida

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Molecular dynamics is a core method for studying the dynamic evolution of molecular systems. This study introduces a new approach for sampling frames from large MD trajectories, which is a linearly scaling alternative to traditional clustering algorithms. The method shows improved efficiency and higher conformational diversity for selecting frames.
Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

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