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Cited by 650

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The molecular machinery for secretion is conserved from yeast to neurons.

Open access
Bennett, M K, Scheller, R H·1993·Proceedings of the National Academy of Sciences

Abstract

A variety of approaches have been utilized to identify and characterize the molecules that mediate vesicular trafficking along the secretory pathway. Two approaches that have been particularly fruitful include the genetic dissection of the yeast secretory pathway and the biochemical characterization of proteins involved in the synaptic vesicle membrane trafficking in the mammalian nerve terminal. The recent convergence of these approaches suggests that common mechanisms may underlie a wide variety of vesicle-mediated transport steps. We discuss the results that support this possibility and propose a model for synaptic vesicle docking and fusion that incorporates evolutionarily conserved elements that may be part of a constitutive fusion machinery and specialized elements that may mediate regulatory events that are specific to the process of neurotransmitter release.

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Crossref · 10.1073/pnas.90.7.2559

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Metadata

Authors
Bennett, M. K., & Scheller, R. H.
Venue
Proceedings of the National Academy of Sciences
Published
1993-04
Volume
90
Issue
7
Pages
2559-2563
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1999744850
Cited by
650
References
2
FWCI
37.32
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The molecular machinery for secretion is conserved from yeast to neurons. https://app.cochat.ai/papers/10.1073/pnas.90.7.2559
The paper
Bennett, M. K., & Scheller, R. H. (1993). The molecular machinery for secretion is conserved from yeast to neurons. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.90.7.2559

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.