Continuum Electrostatics Solvent Modeling with the Generalized Born Model

Onufriev, Alexey V.

doi:10.1002/9783527629251.ch6

Cited by 47 publications

(75 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Langevin dynamics was used for temperature regulation with a collision frequency (effective viscosity) g ¼ 0.01 ps -1 . The critical advantage of this low effective solvent viscosity regime, successfully used in simulation of nucleic acids, proteins, and their complexes (39,43,48,49), is that it offers~100-fold speedup of large conformational transitions relative to the more traditional explicit solvent simulations, for the types of structures considered here (44,50). For explicit solvent calculations we used the GROMACS package (51), with the AMBER ff10 force field at 300 K for 100 ns in the explicit solvent (PME, TIP3P water (52), with 0.145 M NaCl).…”

Section: Structure Preparation Summary Simulation Protocolsmentioning

confidence: 99%

Partially Assembled Nucleosome Structures at Atomic Detail

Rychkov

Ilatovskiy

Nazarov

et al. 2017

Biophysical Journal

View full text Add to dashboard Cite

The evidence is now overwhelming that partially assembled nucleosome states (PANS) are as important as the canonical nucleosome structure for the understanding of how accessibility to genomic DNA is regulated in cells. We use a combination of molecular dynamics simulation and atomic force microscopy to deliver, in atomic detail, structural models of three key PANS: the hexasome (H2A$H2B)$(H3$H4) 2 , the tetrasome (H3$H4) 2 , and the disome (H3$H4). Despite fluctuations of the conformation of the free DNA in these structures, regions of protected DNA in close contact with the histone core remain stable, thus establishing the basis for the understanding of the role of PANS in DNA accessibility regulation. On average, the length of protected DNA in each structure is roughly 18 basepairs per histone protein. Atomistically detailed PANS are used to explain experimental observations; specifically, we discuss interpretation of atomic force microscopy, Fö rster resonance energy transfer, and small-angle x-ray scattering data obtained under conditions when PANS are expected to exist. Further, we suggest an alternative interpretation of a recent genome-wide study of DNA protection in active chromatin of fruit fly, leading to a conclusion that the three PANS are present in actively transcribing regions in a substantial amount. The presence of PANS may not only be a consequence, but also a prerequisite for fast transcription in vivo.

show abstract

Section: Structure Preparation Summary Simulation Protocolsmentioning

confidence: 99%

Partially Assembled Nucleosome Structures at Atomic Detail

Rychkov

Ilatovskiy

Nazarov

et al. 2017

Biophysical Journal

View full text Add to dashboard Cite

show abstract

“…Atomistic MD simulation methods can be divided into two broad classes: those that treat solvent explicitly and those that treat solvent implicitly (10,11). The main objective of this study is to compare the speed of conformational sampling within these two very different approaches to treating solvent effects.…”

Section: Introductionmentioning

confidence: 99%

Speed of Conformational Change: Comparing Explicit and Implicit Solvent Molecular Dynamics Simulations

Anandakrishnan

Drozdetski

Walker

et al. 2015

Biophysical Journal

172

192

View full text Add to dashboard Cite

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.

show abstract

“…51,52 Other benefits include instantaneous dielectric response from the solvent due to changes in solute charge state, and the elimination of "noise" in the energy landscape due to small variations in solvent structure. 53 Consequently, implicit solvent models are often used for applications where it is important to explore a large number of conformational states, such as for protein folding, 54 replica exchange, 55 and docking simulations. 56 However, the functional form for the most widely used practical implicit solvent model for MD, the GB model, scales as ∼n 2 , where n represents the number of solute atoms only.…”

Section: Introductionmentioning

confidence: 99%

AnnlognGeneralized Born Approximation

Anandakrishnan

Daga

Onufriev

2011

J. Chem. Theory Comput.

View full text Add to dashboard Cite

Molecular dynamics (MD) simulations based on the generalized Born (GB) model of implicit solvation offer a number of important advantages over the traditional explicit solvent based simulations. Yet, in MD simulations, the GB model has not been able to reach its full potential partly due to its computational cost, which scales as ∼n(2), where n is the number of solute atoms. We present here an ∼n log n approximation for the generalized Born (GB) implicit solvent model. The approximation is based on the hierarchical charge partitioning (HCP) method (Anandakrishnan and Onufriev J. Comput. Chem. 2010 , 31 , 691 - 706 ) previously developed and tested for electrostatic computations in gas-phase and distant dependent dielectric models. The HCP uses the natural organization of biomolecular structures to partition the structures into multiple hierarchical levels of components. The charge distribution for each of these components is approximated by a much smaller number of charges. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. To apply the HCP concept to the GB model, we define the equivalent of the effective Born radius for components. The component effective Born radius is then used in GB computations for points that are distant from the component. This HCP approximation for GB (HCP-GB) is implemented in the open source MD software, NAB in AmberTools, and tested on a set of representative biomolecular structures ranging in size from 632 atoms to ∼3 million atoms. For this set of test structures, the HCP-GB method is 1.1-390 times faster than the GB computation without additional approximations (the reference GB computation), depending on the size of the structure. Similar to the spherical cutoff method with GB (cutoff-GB), which also scales as ∼n log n, the HCP-GB is relatively simple. However, for the structures considered here, we show that the HCP-GB method is more accurate than the cutoff-GB method as measured by relative RMS error in electrostatic force compared to the reference (no cutoff) GB computation. MD simulations of four biomolecular structures on 50 ns time scales show that the backbone RMS deviation for the HCP-GB method is in reasonable agreement with the reference GB simulation. A critical difference between the cutoff-GB and HCP-GB methods is that the cutoff-GB method completely ignores interactions due to atoms beyond the cutoff distance, whereas the HCP-GB method uses an approximation for interactions due to distant atoms. Our testing suggests that completely ignoring distant interactions, as the cutoff-GB does, can lead to qualitatively incorrect results. In general, we found that the HCP-GB method reproduces key characteristics of dynamics, such as residue fluctuation, χ1/χ2 flips, and DNA flexibility, more accurately than the cutoff-GB method. As a practical demonstration, the HCP-GB simulation of a 348 000 atom chromatin fiber was used to refine the starting structure. O...

show abstract

Continuum Electrostatics Solvent Modeling with the Generalized Born Model

Cited by 47 publications

References 121 publications

Partially Assembled Nucleosome Structures at Atomic Detail

Partially Assembled Nucleosome Structures at Atomic Detail

Speed of Conformational Change: Comparing Explicit and Implicit Solvent Molecular Dynamics Simulations

AnnlognGeneralized Born Approximation

Contact Info

Product

Resources

About