Evidence map›Paper›PMID 23197255›Full record

ArticlePhysical biology2012

A method for computing association rate constants of atomistically represented proteins under macromolecular crowding.

Sanbo Qin, Lu Cai, Huan-Xiang Zhou

Abstract read
In one paragraph

Article in Physical biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Modeling Electrostatic Force in Protein-Protein Recognition.Frontiers in molecular biosciences · 2019
    Article
  5. Review
  6. Article
  7. Review
  8. Challenges in structural approaches to cell modeling.Journal of molecular biology · 2016
    Review
  9. Theory of Crowding Effects on Bimolecular Reaction Rates.The journal of physical chemistry. B · 2016
    Article
  10. Article
  11. Article
  12. Modeling protein association mechanisms and kinetics.Current opinion in structural biology · 2013
    Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Looking for a promoter in 3D.Nature structural & molecular biology · 2013
    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

3 authors.

Sanbo QinDepartment of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA.
Lu Cai
Huan-Xiang Zhou

Funding

Modeling Crowding and Confinement of Cellular EnvironmentsR01GM088187 · NIGMS · FLORIDA STATE UNIVERSITY · PI ZHOU, HUAN-XIANG · 2010 to 2015
$1.6M
NIGMS NIH HHS GM88187NIGMS NIH HHS R01 GM088187
6 · The paper itself

Abstract

In cellular environments, two protein molecules on their way to form a specific complex encounter many bystander macromolecules. The latter molecules, or crowders, affect both the energetics of the interaction between the test molecules and the dynamics of their relative motion. In earlier work (Zhou and Szabo 1991 J. Chem. Phys. 95 5948-52), it has been shown that, in modeling the association kinetics of the test molecules, the presence of crowders can be accounted for by their energetic and dynamic effects. The recent development of the transient-complex theory for protein association in dilute solutions makes it possible to easily incorporate the energetic and dynamic effects of crowders. The transient complex refers to a late on-pathway intermediate, in which the two protein molecules have near-native relative separation and orientation, but have yet to form the many short-range specific interactions of the native complex. The transient-complex theory predicts the association rate constant as k(a) = k(a0)exp(-ΔG*(el)/k(B)T), where k(a0) is the 'basal' rate constant for reaching the transient complex by unbiased diffusion, and the Boltzmann factors captures the influence of long-range electrostatic interactions between the protein molecules. Crowders slow down the diffusion, therefore reducing the basal rate constant (to k(ac0)), and induce an effective interaction energy ΔG(c). We show that the latter interaction energy for atomistic proteins in the presence of spherical crowders is 'long'-ranged, allowing the association rate constant under crowding to be computed as k(ac) = k(ac0)exp[-(ΔG*(el) + ΔG*(c))/k(B)T]. Applications demonstrate that this computational method allows for realistic modeling of protein association kinetics under crowding.

Indexed as

Computational BiologyDiffusionModels, BiologicalModels, MolecularProtein BindingProteinsSolutionsThermodynamicsProteinsSolutions

Identifiers

PMID23197255
PMCPMC3521150

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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.