CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Wang, Kye Won; Lee, Jumin; Zhang, Han; Suh, Donghyuk; Im, Wonpil

doi:10.1021/acs.jpcb.2c05294

Cited by 5 publications

(4 citation statements)

References 93 publications

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“…The default solvent dielectric constant of 78.5 and a protein interior dielectric constant of 1 were applied. The OBC GB model with igb = 5 was selected. , The conformational entropy change (− T Δ S ) was neglected in this calculation. Δ G bind , therefore, was the sum of the electrostatic (Δ E elec ), van der Waals (Δ E vdw ), nonpolar solvation, and GB polar solvation terms.…”

Section: Methodsmentioning

confidence: 99%

CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein–Ligand Binding Modes

Guterres

2023

J. Chem. Inf. Model.

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Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is critical in early phase structure-based drug discovery. However, there is a persistent challenge in docking that can be attributed to the induced fit effect, as receptor binding sites undergo induced fit conformational changes upon ligand binding to achieve better binding modes. In this work, based on CHARMM-GUI LBS Finder & Refiner and High-Throughput Simulator, we present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow to generate reliable protein–ligand binding modes. The CGUI-IFD workflow generates an ensemble of receptor binding site conformations through ligand-binding site (LBS) refinement, runs rigid receptor docking, and performs high-throughput molecular dynamics (MD) simulations of protein–ligand complex structures in explicit solvents. The results are evaluated based on the ligand root-mean-square deviation (RMSD)-based binding stability and the molecular mechanics generalized Born surface area binding energy. For a benchmark test, we used 258 cross-docking protein–ligand pairs across 41 target proteins from the Schrodinger IFD-MD data set. The application of CGUI-IFD on this data set shows 80% success rate (within 2.5 Å RMSD from the experimental structures). We expect that the CGUI-IFD workflow can be useful to generate reliable ligand binding modes for cross-docking cases.

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Section: Methodsmentioning

confidence: 99%

CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein–Ligand Binding Modes

Guterres

2023

J. Chem. Inf. Model.

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“…This allows for faster simulations and the ability to study larger systems or longer time scales with reduced computational resources. While implicit models can speed up simulations and are useful for exploring conformational space, they may lack the detailed representation of specific solvent-solute interactions, such as hydrogen bonding or the precise effect of solvent molecules on the solvation shell of the ligand and receptor [22,23].…”

Section: Incorporation Of Solventmentioning

confidence: 99%

Molecular Docking in Drug Discovery: Techniques, Applications, and Advancements

Aguiar,

Camps

2024

Preprint

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Objective: The primary objective of this study is to conduct a comprehensive review of the significance of molecular docking in the field of drug discovery. This includes an examination of the various approaches and methods used in molecular docking, as well as an exploration of the techniques used for interpreting and validating docking results. Methods: To gather relevant data, a systematic search was conducted using Web of Science, PubMed, and Google Scholar. The search focused on articles related to molecular docking methodologies and their applications in drug discovery. Additionally, alternative techniques that can be used for more precise simulations of ligand-protein interactions were also considered. Results: Molecular docking has proven to be an incredibly rich and valuable process in the field of drug discovery. Its flexibility allows for the incorporation of advanced computational techniques, thereby enhancing the reliability and efficiency of drug discovery processes. The results of the study highlight the significant strides made in the field of molecular docking, demonstrating its potential to revolutionize drug discovery. Conclusions: Molecular docking continues to evolve, with new advancements being made regularly. Despite the challenges faced, these advancements have significantly contributed to the enhancement of molecular docking, solidifying its position as a crucial tool in the field of drug discovery.

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“…20 The computational advantages of using a simplified model such as Generalized Born (GB) are leveraged by enriching the CHARMM-GUI toolkit of the functionality to set up calculations with different GB flavors for different MD engines. 21 One of the fields where electrostatic computations proved to be useful is pK a estimation. In this virtual special issue, several methodological contributions to this topic are presented: one concerns the characterization of the ensemble of protonation microstates of a protein at a given pH; 22 another allows the adoption of the CHARMM36m force field in the stochastic titration Constant pH Molecular Dynamics (CpHMD) method; 23 upon (de)protonation in constant-pH MD with periodic boundary conditions; 24 and one deals with modifying the reactive molecular dynamics approach providing more accurate and explainable pK a values for some titratable residues.…”

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confidence: 99%

“…Along the same lines, but following a different model, a coarse-grained version of ddCOSMO was presented as an efficient way to compute the solvation energy of large solutes within a polarizable continuum medium in a linear scaling computational time . The computational advantages of using a simplified model such as Generalized Born (GB) are leveraged by enriching the CHARMM-GUI toolkit of the functionality to set up calculations with different GB flavors for different MD engines …”

mentioning

confidence: 99%