ArticleBiophysical journal2015
Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations.
Article in Biophysical journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An understanding of coronavirus and exploring the molecular dynamics simulations to find promising candidates against the Mpro of nCoV to combat the COVID-19: A systematic review.Journal of infection and public health · 2022Pooled it
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- Molecular Dynamics Simulations of RNA Stem-Loop Folding Using an Atomistic Force Field and a Generalized Born Implicit Solvent.ACS omega · 2025Article
- Robust Conformational Space Exploration of Cyclic Peptides by Combining Different MD Protocols and Force Fields.Journal of chemical theory and computation · 2025Article
- Disrupting Amyloid Filaments of Tau by Means of Electric Fields.The journal of physical chemistry. B · 2025Article
- Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.Journal of chemical theory and computation · 2025Article
- Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution.The journal of physical chemistry. B · 2025Article
- Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].Living journal of computational molecular science · 2025Article
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- Binding affinity between coronavirus spike protein and human ACE2 receptor.Computational and structural biotechnology journal · 2024Review
- Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.Journal of chemical theory and computation · 2024Article
- A general graph neural network based implicit solvation model for organic molecules in water.Chemical science · 2024Article
- Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains.The journal of physical chemistry. B · 2024Article
- Interfacial Interaction between Functionalization of Polysulfone Membrane Materials and Protein Adsorption.Polymers · 2024Article
- Design, synthesis,Heliyon · 2024Article
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
- Liquid-Vapor Coexistence and Spontaneous Evaporation at Atmospheric Pressure of Common Rigid Three-Point Water Models in Molecular Simulations.The journal of physical chemistry. B · 2024Article
- The Potential of Cyclodextrins as Inhibitors for the BM2 Protein: An In Silico Investigation.Molecules (Basel, Switzerland) · 2024Article
22 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Adequate sampling of conformation space remains challenging in atomistic simulations, especially if the solvent is treated explicitly. Implicit-solvent simulations can speed up conformational sampling significantly. We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package. We systematically investigate small (dihedral angle flips in a protein), large (nucleosome tail collapse and DNA unwrapping), and mixed (folding of a miniprotein) conformational changes, with nominal simulation times ranging from nanoseconds to microseconds depending on system size. The speedups in conformational sampling for GB relative to PME simulations, are highly system- and problem-dependent. Where the simulation temperatures for PME and GB are the same, the corresponding speedups are approximately onefold (small conformational changes), between ∼1- and ∼100-fold (large changes), and approximately sevenfold (mixed case). The effects of temperature on speedup and free-energy landscapes, which may differ substantially between the solvent models, are discussed in detail for the case of miniprotein folding. In addition to speeding up conformational sampling, due to algorithmic differences, the implicit solvent model can be computationally faster for small systems or slower for large systems, depending on the number of solute and solvent atoms. For the conformational changes considered here, the combined speedups are approximately twofold, ∼1- to 60-fold, and ∼50-fold, respectively, in the low solvent viscosity regime afforded by the implicit solvent. For all the systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effective viscosity) decreases; and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity rather than possible differences in free-energy landscapes between the solvent models.
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