ArticleJournal of chemical theory and computation2019
How Effectively Can Adaptive Sampling Methods Capture Spontaneous Ligand Binding?
Article in Journal of chemical theory and computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Dynamic mechanism for subtype selectivity of endocannabinoids.The Journal of biological chemistry · 2026Article
- Thermodynamic Interpolation: A Generative Approach to Molecular Thermodynamics and Kinetics.Journal of chemical theory and computation · 2025Article
- Water will Find Its Way: Transport through Narrow Tunnels in Hydrolases.Journal of chemical information and modeling · 2024Article
- Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites.Journal of chemical theory and computation · 2024Article
- The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition.Communications biology · 2024Article
- CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules.JACS Au · 2024Article
- Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters.Mini reviews in medicinal chemistry · 2024Review
- Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.Journal of chemical theory and computation · 2023Article
- Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding.Molecules (Basel, Switzerland) · 2023Review
- Distinct activation mechanisms regulate subtype selectivity of Cannabinoid receptors.Communications biology · 2023Article
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
- Changes in Protonation States of In-Pathway Residues can Alter Ligand Binding Pathways Obtained From Spontaneous Binding Molecular Dynamics Simulations.Frontiers in molecular biosciences · 2022Article
- Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?PLoS computational biology · 2021Article
- High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling.Chemical science · 2021Article
- Markov State Models to Elucidate Ligand Binding Mechanism.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Data-Driven Molecular Dynamics: A Multifaceted Challenge.Pharmaceuticals (Basel, Switzerland) · 2020Review
- Protein-ligand binding with the coarse-grained Martini model.Nature communications · 2020Article
- Multisecond ligand dissociation dynamics from atomistic simulations.Nature communications · 2020Article
- Spontaneous Ligand Access Events to Membrane-Bound Cytochrome P450 2D6 Sampled at Atomic Resolution.Scientific reports · 2019Article
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2 authors.
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Abstract
Molecular dynamics (MD) simulations that capture the spontaneous binding of drugs and other ligands to their target proteins can reveal a great deal of useful information, but most drug-like ligands bind on time scales longer than those accessible to individual MD simulations. Adaptive sampling methods-in which one performs multiple rounds of simulation, with the initial conditions of each round based on the results of previous rounds-offer a promising potential solution to this problem. No comprehensive analysis of the performance gains from adaptive sampling is available for ligand binding, however, particularly for protein-ligand systems typical of those encountered in drug discovery. Moreover, most previous work presupposes knowledge of the ligand's bound pose. Here we outline existing methods for adaptive sampling of the ligand-binding process and introduce several improvements, with a focus on methods that do not require prior knowledge of the binding site or bound pose. We then evaluate these methods by comparing them to traditional, long MD simulations for realistic protein-ligand systems. We find that adaptive sampling simulations typically fail to reach the bound pose more efficiently than traditional MD. However, adaptive sampling identifies multiple potential binding sites more efficiently than traditional MD and also provides better characterization of binding pathways. We explain these results by showing that protein-ligand binding is an example of an exploration-exploitation dilemma. Existing adaptive sampling methods for ligand binding in the absence of a known bound pose vastly favor the broad exploration of protein-ligand space, sometimes failing to sufficiently exploit intermediate states as they are discovered. We suggest potential avenues for future research to address this shortcoming.
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