New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations

Lee, Michael S.; Feig, Michael; Salsbury, Freddie R.; Brooks, Charles L.

doi:10.1002/jcc.10272

Cited by 473 publications

(684 citation statements)

References 27 publications

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“…28 The resulting dielectric profile (z) for dipalmitoylphosphatidylcholine (DPPC) bilayers is shown in Figure 1. The HDGB model is based on the GBMV model, 26 a generalized Born (GB) version that utilizes the standard molecular volume and can predict the electrostatic solvation free energy very accurately when compared with Poisson theory. 26 In the GBMV formalism, the effective Born radius R is determined according to the Coulomb Field approximation including a correction term.…”

Section: Hdgb Modelmentioning

confidence: 99%

“…45 As of this writing, only 1 simulation study was reported using a united-atom force field. 46 In addition to simulations with the HDGB model, simulations of all 3 proteins were also performed in a homogeneous implicit aqueous environment using the standard GBMV model 26 for comparison with the HDGB model.…”

Section: Hdgb Modelmentioning

confidence: 99%

“…29 Furthermore, the GBIM model was used for folding simulations of the major pVIII coat protein from filamentous bacteriophage fd (50 residues) 33 and other synthetic small peptides. 34 Most recently, we have proposed the extension of the GBMV (GB with molecular volume) method 26 to an implicit membrane model called HDGB. 28 In contrast to the previously described approaches, the HDGB model allows multiple layers of different dielectric constants.…”

Section: Introductionmentioning

confidence: 99%

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Molecular Dynamics Simulations of Large Integral Membrane Proteins with an Implicit Membrane Model

Tanizaki

Feig

2005

J. Phys. Chem. B

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The heterogeneous dielectric generalized Born (HDGB) methodology is an the extension of the GBMV model for the simulation of integral membrane proteins with an implicit membrane environment. Three large integral membrane proteins, the bacteriorhodopsin monomer and trimer and the BtuCD protein, were simulated with the HDGB model in order to evaluate how well thermodynamic and dynamic properties are reproduced. Effects of the truncation of electrostatic interactions were examined. For all proteins, the HDGB model was able to generate stable trajectories that remained close to the starting experimental structures, in excellent agreement with explicit membrane simulations. Dynamic properties evaluated through a comparison of B-factors are also in good agreement with experiment and explicit membrane simulations. However, overall flexibility was slightly underestimated with the HDGB model unless a very large electrostatic cutoff is employed. Results with the HDGB model are further compared with equivalent simulations in implicit aqueous solvent, demonstrating that the membrane environment leads to more realistic simulations.

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Section: Hdgb Modelmentioning

confidence: 99%

Section: Hdgb Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Molecular Dynamics Simulations of Large Integral Membrane Proteins with an Implicit Membrane Model

Tanizaki

Feig

2005

J. Phys. Chem. B

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“…The original parametrization determined for the GBMV2-S 20 model maximizes the agreement with Poisson theory. 11,12 Of the two GBMV2 models tested, GBMV2-S 10 exhibited the lowest T c of 389 K. Nevertheless, the predicted T c is higher than experimental observations. Depending on the sequence variant of the SH3 domain and experimental pH conditions, the reported melting temperature is roughly 340 K. 24 Although the deviation between the experimental temperature and our calculation is less drastic than that observed in the work of Pitera and Swope on modeling the Trp-cage, 21 where they predicted a T c value nearly 85 K higher than the experimental determination, both studies indicate that REMD simulations with GB models tend to overstabilize native structures.…”

Section: Discussionmentioning

confidence: 89%

“…10 The numerical stability of the GBSW model is well suited for MD simulations of proteins, although the dielectric boundary is fundamentally different from the molecular surface in its treatment of the interstitial space between atoms. A truer mimic of the molecular surface is given by the GBMV2 solvent model, 11,12 where an analytical formulation is used to build a molecular volume based on superposition of spherical functions.…”

Section: Introductionmentioning

confidence: 99%

Calculation of Protein Heat Capacity from Replica-Exchange Molecular Dynamics Simulations with Different Implicit Solvent Models

2008

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The heat capacity has played a major role in relating microscopic and macroscopic properties of proteins and their disorder-order phase transition of folding. Its calculation by atomistic simulation methods remains a significant challenge due to the complex and dynamic nature of protein structures, their solvent environment, and configurational averaging. To better understand these factors on calculating a protein heat capacity, we provide a comparative analysis of simulation models that differ in their implicit solvent description and forcefield resolution. Our model protein system is the src Homology 3 (SH3) domain of R-spectrin, and we report a series of 10 ns replica-exchange molecular dynamics simulations performed at temperatures ranging from 298 to 550 K, starting from the SH3 native structure. We apply the all-atom CHARMM22 force field with different modified analytical generalized Born solvent models (GBSW and GBMV2) and compare these simulation models with the distance-dependent dielectric screening of charge-charge interactions. A further comparison is provided with the united-atom CHARMM19 plus a pairwise GB model. Unfolding-folding transition temperatures of SH3 were estimated from the temperature-dependent profiles of the heat capacity, root-mean-square distance from the native structure, and the fraction of native contacts, each calculated from the density of states by using the weighted histogram analysis method. We observed that, for CHARMM22, the unfolding transition and energy probability density were quite sensitive to the implicit solvent description, in particular, the treatment of the protein-solvent dielectric boundary in GB models and their surface-areabased hydrophobic term. Among the solvent models tested, the calculated melting temperature varied in the range 353-438 K and was higher than the experimental value near 340 K. A reformulated GBMV2 model of employing a smoother molecular-volume dielectric interface was the most accurate in reproducing the native conformation and a two-state folding landscape, although the melting transition temperature did not show the smallest deviation from experiment. For the lower-resolution CHARMM19/GB model, the simulations failed to yield a bimodal energy distribution, yet the melting temperature was observed to be a good estimate of higher-resolution simulation models. We also demonstrate that a careful analysis of a relatively long simulation is necessary to avoid trapping in local minima and to find a true thermodynamic transition temperature.

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Molecular Modeling of Nucleic Acid Structure: Electrostatics and Solvation

Bergonzo

Galindo‐Murillo

Cheatham

2013

CP Nucleic Acid Chemistry

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This unit presents an overview of computer simulation techniques as applied to nucleic acid systems, ranging from simple in vacuo molecular modeling techniques to more complete all-atom molecular dynamics treatments that include an explicit representation of the environment. The third in a series of four units, this unit focuses on critical issues in solvation and the treatment of electrostatics. UNITS 7.5 & 7.8 introduced the modeling of nucleic acid structure at the molecular level. This included a discussion of how to generate an initial model, how to evaluate the utility or reliability of a given model, and ultimately how to manipulate this model to better understand the structure, dynamics, and interactions. Subject to an appropriate representation of the energy, such as a specifically parameterized empirical force field, the techniques of minimization and Monte Carlo simulation, as well as molecular dynamics (MD) methods, were introduced as means to sample conformational space for a better understanding of the relevance of a given model. From this discussion, the major limitations with modeling, in general, were highlighted. These are the difficult issues in sampling conformational space effectively-the multiple minima or conformational sampling problems-and accurately representing the underlying energy of interaction. In order to provide a realistic model of the underlying energetics for nucleic acids in their native environments, it is crucial to include some representation of solvation (by water) and also to properly treat the electrostatic interactions. These are discussed in detail in this unit. SubjectNucleic Acid Chemistry; Nucleic Acid Structure and Folding; Structural Analysis of Biomolecules; Experimental Determination of Structure ELECTROSTATICS AND SOLVATIONAccurately modeling the structure and dynamics of nucleic acids with standard ab initio or empirical potentials presents special difficulties due to the highly charged phosphate backbone and the observation that nucleic acids are essentially always hydrated. Even under extremely dehydrating conditions, DNA still has very tightly associated water. To apply an accurate model, some representation of this structural and solvating water is likely necessary. Water has a structural role as both a donor and acceptor of hydrogen bonds, and Internet ResourcesAscona B-DNA Consortium website.

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New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations

Cited by 473 publications

References 27 publications

Molecular Dynamics Simulations of Large Integral Membrane Proteins with an Implicit Membrane Model

Molecular Dynamics Simulations of Large Integral Membrane Proteins with an Implicit Membrane Model

Calculation of Protein Heat Capacity from Replica-Exchange Molecular Dynamics Simulations with Different Implicit Solvent Models

Molecular Modeling of Nucleic Acid Structure: Electrostatics and Solvation

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