Evidence map›Paper›PMID 30850521›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

Mechanisms for achieving high speed and efficiency in biomolecular machines.

Jason A Wagoner, Ken A Dill

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  6. Multistate Kinetic Model of the Sodium-Potassium ATPase.The journal of physical chemistry. B · 2025
    Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. FFrontiers in microbiology · 2022
    Review
  15. Evolution of mechanical cooperativity among myosin II motors.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  16. Reversible catalysis.Nature reviews. Chemistry · 2021
    Review
  17. How Do Cells Adapt? Stories Told in Landscapes.Annual review of chemical and biomolecular engineering · 2020
    Review
  18. Article
  19. 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.

Jason A WagonerLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794.
Ken A DillLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794; dill@laufercenter.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How does a biomolecular machine achieve high speed at high efficiency? We explore optimization principles using a simple two-state dynamical model. With this model, we establish physical principles-such as the optimal way to distribute free-energy changes and barriers across the machine cycle-and connect them to biological mechanisms. We find that a machine can achieve high speed without sacrificing efficiency by varying its conformational free energy to directly link the downhill, chemical energy to the uphill, mechanical work and by splitting a large work step into more numerous, smaller substeps. Experimental evidence suggests that these mechanisms are commonly used by biomolecular machines. This model is useful for exploring questions of evolution and optimization in molecular machines.

Indexed as

Energy MetabolismEnergy TransferModels, TheoreticalMolecular ConformationMolecular Motor ProteinsMolecular StructureMolecular Motor Proteinsevolutionfree-energy landscapekinetic optimizationmolecular machinesnonequilibrium steady state

Identifiers

PMID30850521
PMCPMC6442566

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.