Treecode-based generalized Born method

Xu, Zhenli; Cheng, Xiaolin; Yang, Haizhao

doi:10.1063/1.3552945

Cited by 12 publications

(11 citation statements)

References 77 publications

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“…A great variety of flavors and implementations of the GB are available, several of which implemented in molecular modeling packages such as AMBER, CHARMM, GROMACS, NAMD, OpenMM, or TINKER . The specific choice depends on specific needs: a protein folding simulation may call for one flavor of the GB model, while a study of a protein in a membrane environment will need quite another .…”

Section: Implicit Solvent Modelsmentioning

confidence: 99%

Water models for biomolecular simulations

Onufriev

Izadi²

2017

WIREs Comput Mol Sci

171

225

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Modern simulation and modeling approaches to investigation of biomolecular structure and function rely heavily on a variety of methods—water models—to approximate the influence of solvent. We give a brief overview of several distinct classes of available water models, with the emphasis on the conceptual basis at each level of approximation. The main focus is on classes of models most widely used in atomistic simulations, including popular implicit and explicit solvent models. Among the latter, nonpolarizable N‐point models are covered in most detail, including some recent methodological advances and nuances. Notes on practical availability and usage in biomolecular simulations are included. Atomistic simulations that were hardly possible only a short while ago have revealed significant problems that can be traced to deficiencies of most commonly used N‐point water models. Recently developed models of this class approximate experimental properties of liquid water much closer than before, and show promise in practical biomolecular simulations. Obstacles to wider adoption of these more accurate water models, both technical and conceptual, are discussed. It is argued that verifying robustness of simulation results to the choice of water model can be of immediate benefit even in the absence of a clear replacement for older models; a specific strategy is proposed. The review is concluded with a discussion on how force‐field development efforts can benefit from better solvent models, and vice versa. WIREs Comput Mol Sci 2018, 8:e1347. doi: 10.1002/wcms.1347 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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Section: Implicit Solvent Modelsmentioning

confidence: 99%

Water models for biomolecular simulations

Onufriev

Izadi²

2017

WIREs Comput Mol Sci

171

225

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“…These simple models include distance-dependent dielectric functions, analytic continuum methods (Schaefer & Karplus, 1996), the so-called Effective Energy Function (EEF) approach (Lazaridis & Karplus, 1999) and the improved ABSINTH model (Vitalis & Pappu, 2009) and generalized Born (GB) models. The GB model is one of the most popular models and was developed by Still et al in 1990 (Still et al , 1990) and subsequently revised by several others (Anandakrishnan et al , 2011; Bashford & Case, 2000; Brown & Case, 2006; Chen, 2010; Chen et al , 2006; Clark et al , 2009; Dominy & Brooks, 1999; Feig & Brooks, 2004; Feig et al , 2004, 2008; Gallicchio et al , 2002, 2009; Grant et al , 2007; Grycuk, 2003; Im et al , 2003b; Jorgensen et al , 2004; Labute, 2008; Lee et al , 2002; Onufriev et al , 2000, 2002; Osapay et al , 1996; Tjong & Zhou, 2007b; Tsui & Case, 2000; Xu et al , 2011; Zhu et al , 2005). The GB model describes the solvent as a continuum medium, similar to the PB model, but provides a faster calculation of solvation energies and forces.…”

Section: Modeling Solvation With Low Detail: Continuum Approximationsmentioning

confidence: 99%

Biomolecular electrostatics and solvation: a computational perspective

Ren

Chun

Thomas

et al. 2012

Quart. Rev. Biophys.

166

168

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An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide a background to understand the different types of solvation models.

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“…1 C, right panel). In addition, the overall RMSF pattern for the implicit solvent simulation matches that for the explicit solvent trajectory, despite a minor difference in the dihydrouracil arm (nucleotides [8][9][10][11][12][13][14], in which the implicit solvent simulation exhibits slightly larger RMSF values (Fig. 1 C, right panel).…”

Section: Trna Simulationmentioning

confidence: 61%

“…Numerous different implicit solvent models have been applied extensively in simulations of proteins, with some success claimed for particular systems. One widely used approach is based on the generalized-Born model with an added surface area term (GBSA) (10)(11)(12)(13)(14)(15). However, this GBSA technique encounters serious difficulties in simulations for nucleic acids, especially RNAs (1), except for a few reported studies of small RNA duplexes and RNA tetra-loops (16)(17)(18)(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

A Novel Implicit Solvent Model for Simulating the Molecular Dynamics of RNA

Liu

Haddadian

Sosnick

et al. 2013

Biophysical Journal

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Although molecular dynamics simulations can be accelerated by more than an order of magnitude by implicitly describing the influence of the solvent with a continuum model, most currently available implicit solvent simulations cannot robustly simulate the structure and dynamics of nucleic acids. The difficulties become exacerbated especially for RNAs, suggesting the presence of serious physical flaws in the prior continuum models for the influence of the solvent and counter ions on the nucleic acids. We present a novel, to our knowledge, implicit solvent model for simulating nucleic acids by combining the Langevin-Debye model and the Poisson-Boltzmann equation to provide a better estimate of the electrostatic screening of both the water and counter ions. Tests of the model involve comparisons of implicit and explicit solvent simulations for three RNA targets with 20, 29, and 75 nucleotides. The model provides reasonable agreement with explicit solvent simulations, and directions for future improvement are noted.

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Treecode-based generalized Born method

Cited by 12 publications

References 77 publications

Water models for biomolecular simulations

Water models for biomolecular simulations

Biomolecular electrostatics and solvation: a computational perspective

A Novel Implicit Solvent Model for Simulating the Molecular Dynamics of RNA

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