Evidence map›Paper›PMID 19437047›Full record

ReviewJournal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry2009

Recent developments of the quantum chemical cluster approach for modeling enzyme reactions.

Per E M Siegbahn, Fahmi Himo

Abstract readReview
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In one paragraph

Review in Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

  1. Review
  2. Article
  3. Computer Simulations of the Temperature Dependence of Enzyme Reactions.Journal of chemical theory and computation · 2025
    Review
  4. Article
  5. Article
  6. Modeling onActa pharmaceutica Sinica. B · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Theoretical Modeling of Redox Potentials of Biomolecules.Molecules (Basel, Switzerland) · 2022
    Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

2 authors.

Per E M SiegbahnDepartment of Physics, ALBA NOVA, Arrhenius Laboratory, Stockholm University, Stockholm, Sweden. ps@physto.se
Fahmi Himo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quantum chemical cluster approach for modeling enzyme reactions is reviewed. Recent applications have used cluster models much larger than before which have given new modeling insights. One important and rather surprising feature is the fast convergence with cluster size of the energetics of the reactions. Even for reactions with significant charge separation it has in some cases been possible to obtain full convergence in the sense that dielectric cavity effects from outside the cluster do not contribute to any significant extent. Direct comparisons between quantum mechanics (QM)-only and QM/molecular mechanics (MM) calculations for quite large clusters in a case where the results differ significantly have shown that care has to be taken when using the QM/MM approach where there is strong charge polarization. Insights from the methods used, generally hybrid density functional methods, have also led to possibilities to give reasonable error limits for the results. Examples are finally given from the most extensive study using the cluster model, the one of oxygen formation at the oxygen-evolving complex in photosystem II.

Indexed as

Models, ChemicalQuantum TheoryAnimalsComputer SimulationEnzymesHumansModels, MolecularEnzymes

Identifiers

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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.