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Tailoring the Variational Implicit Solvent Method for New Challenges: Biomolecular Recognition and Assembly

Overview of attention for article published in Frontiers in Molecular Biosciences, February 2018
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Title
Tailoring the Variational Implicit Solvent Method for New Challenges: Biomolecular Recognition and Assembly
Published in
Frontiers in Molecular Biosciences, February 2018
DOI 10.3389/fmolb.2018.00013
Pubmed ID
Authors

Clarisse Gravina Ricci, Bo Li, Li-Tien Cheng, Joachim Dzubiella, J. Andrew McCammon

Abstract

Predicting solvation free energies and describing the complex water behavior that plays an important role in essentially all biological processes is a major challenge from the computational standpoint. While an atomistic, explicit description of the solvent can turn out to be too expensive in large biomolecular systems, most implicit solvent methods fail to capture "dewetting" effects and heterogeneous hydration by relying on a pre-established (i.e.,guessed) solvation interface. Here we focus on the Variational Implicit Solvent Method, an implicit solvent method that adds water "plasticity" back to the picture by formulating the solvation free energy as a functional of all possible solvation interfaces. We survey VISM's applications to the problem of molecular recognition and report some of the most recent efforts to tailor VISM for more challenging scenarios, with the ultimate goal of including thermal fluctuations into the framework. The advances reported herein pave the way to make VISM a uniquely successful approach to characterize complex solvation properties in the recognition and binding of large-scale biomolecular complexes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Professor 2 14%
Student > Master 1 7%
Researcher 1 7%
Professor > Associate Professor 1 7%
Other 0 0%
Unknown 5 36%
Readers by discipline Count As %
Physics and Astronomy 2 14%
Chemistry 2 14%
Computer Science 1 7%
Chemical Engineering 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Other 1 7%
Unknown 6 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2018.
All research outputs
#18,587,406
of 23,023,224 outputs
Outputs from Frontiers in Molecular Biosciences
#1,987
of 3,871 outputs
Outputs of similar age
#334,195
of 445,207 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#27
of 38 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,871 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 445,207 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.