Evidence map›Paper›PMID 30857399›Full record

ReviewAnnual review of biophysics2019

Generalized Born Implicit Solvent Models for Biomolecules.

Alexey V Onufriev, David A Case

Abstract readReview
In one paragraph

Review in Annual review of biophysics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.

0numbers the graph read from it
0cells of the map it votes in
138citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

138 citing papers in PubMed.

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  14. Network analysis-guided drug repurposing: IGF1R as a novel melanoma target and therapeutic potential of dapagliflozin.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026
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78 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Alexey V OnufrievDepartments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA; email: alexey@cs.vt.edu.
David A CaseDepartment of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA; email: david.case@rutgers.edu.

Funding

Combining molecular dynamics simulations with crystallographic refinementR01GM122086 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI CASE, DAVID A · 2017 to 2020
$1.1M
NIGMS NIH HHS R01 GM122086
6 · The paper itself

Abstract

It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.

Indexed as

Computer SimulationNucleic AcidsProteinsSolventsThermodynamicsNucleic AcidsProteinsSolventsbiomolecular simulationsdielectricelectrostaticsgeneralized Bornimplicit solvation

Identifiers

PMID30857399
PMCPMC6645684

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.