Evidence map›Paper›PMID 22184480›Full record

ArticleJournal of statistical physics2011

Quantifying density fluctuations in volumes of all shapes and sizes using indirect umbrella sampling.

Amish J Patel, Patrick Varilly, David Chandler, Shekhar Garde

Abstract read
In one paragraph

Article in Journal of statistical physics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. A water-pinning hot spot drives templated tau aggregation.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. A water-pinning hotspot drives templated tau aggregation.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Context-Aware Hydrophobicity Modeling: HydroMap and FastHydroMap.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Water-directed pinning is key to tau prion formation.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Learning the relationship between nanoscale chemical patterning and hydrophobicity.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  18. Article
  19. Article
  20. Identifying hydrophobic protein patches to inform protein interaction interfaces.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
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

4 authors.

Amish J PatelHoward P. Isermann Department of Chemical & Biological Engineering, and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180.
Patrick Varilly
David Chandler
Shekhar Garde

Funding

Hydrophobic Effects in Biophysical Systems and Protein-Protein InteractionsR01GM078102 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI CHANDLER, DAVID · 2007 to 2010
$897k
NIGMS NIH HHS R01 GM078102
6 · The paper itself

Abstract

Water density fluctuations are an important statistical mechanical observable that is related to many-body correlations, as well as hydrophobic hydration and interactions. Local water density fluctuations at a solid-water surface have also been proposed as a measure of it's hydrophobicity. These fluctuations can be quantified by calculating the probability, P(v)(N), of observing N waters in a probe volume of interest v. When v is large, calculating P(v)(N) using molecular dynamics simulations is challenging, as the probability of observing very few waters is exponentially small, and the standard procedure for overcoming this problem (umbrella sampling in N) leads to undesirable impulsive forces. Patel et al. [J. Phys. Chem. B, 114, 1632 (2010)] have recently developed an indirect umbrella sampling (INDUS) method, that samples a coarse-grained particle number to obtain P(v)(N) in cuboidal volumes. Here, we present and demonstrate an extension of that approach to volumes of other basic shapes, like spheres and cylinders, as well as to collections of such volumes. We further describe the implementation of INDUS in the NPT ensemble and calculate P(v)(N) distributions over a broad range of pressures. Our method may be of particular interest in characterizing the hydrophobicity of interfaces of proteins, nanotubes and related systems.

Identifiers

PMID22184480
PMCPMC3241221

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Registered trials

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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.