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Cited by 31Bacterial Genetics and BiotechnologyMachine Learning in BioinformaticsProtein Structure and Dynamics

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli

Open access
Nagar, Natan, Ecker, Noa, Loewenthal, Gil, Avram, Oren, Ben-Meir, Daniella, Biran, Dvora, Ron, Eliora, Pupko, Tal·2021·mSystems
DOI 10.1128/msystems.01296-20PMC PMC7857536

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Abstract

Bacteria use protein degradation to control proliferation, dispose of misfolded proteins, and adapt to physiological and environmental shifts, but the factors that dictate which proteins are prone to degradation are mostly unknown. In this study, we have used a combined computational-experimental approach to explore protein degradation in E. coli .

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  • Cultures in Boe et al. were inoculated with a varying number (order of magnitude 10^1-10^4) of cells.

    “Cultures were inoculated with a varying number (order of magnitude 10 1 -10 4 ) of cells, but this parameter was deemed of little importance.”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [61]

    The Materials and Methods section ('Bacterial cell culture and pulsed-SILAC labeling'; page unavailable) describes reseeding two E. coli cultures at low optical densities, not inoculating cultures with 10¹–10⁴ cells. Searches for Boe, the stated cell range, and the importance of inoculum size found no passage addressing the claim. (LLM, conf=0.74)

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  • Boe et al. performed 23 experiments, each comprising 48 parallel tubes.

    “23 experiments, each comprising 48 parallel tubes, were performed.”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [61]

    The Materials and Methods section describes isotope-labeled E. coli cultures and sampling at five time points, but does not state that 23 experiments used 48 parallel tubes each. Searches for “23 experiments, each comprising 48 parallel tubes” and “Boe 48 parallel tubes experiments” found no passage addressing those counts. Page numbers are unavailable in our copy. (LLM, conf=0.75)

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  • The possible appearance of phenotypic delay was raised in two big fluctuation experiments reported by Newcombe in 1948 and by Boe et al. in 1994.

    “The possible appearance of phenotypic delay was raised in two big fluctuation experiments reported by Newcombe in 1948 and by Boe et al. in 1994 [20,61].”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [61]

    The Results and Discussion passages concern protein degradation in E. coli, not phenotypic delay or fluctuation experiments. Searches for Newcombe, Boe, and phenotypic delay found no passage in our copy addressing the claim. (LLM, conf=0.76)

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Metadata

Authors
Nagar, N., Ecker, N., Loewenthal, G., Avram, O., Ben-Meir, D., Biran, D., Ron, E., & Pupko, T.
Affiliations
  • Tel Aviv University (IL)
  • Shmunis School for Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
Corresponding author
Pupko, Tal
ORCID
Venue
mSystems
Publisher
American Society for Microbiology
Published
2021-02-23
Volume
6
Issue
1
ISSN
2379-5077 (ISSN-L)
DOI
10.1128/msystems.01296-20
PMC
PMC7857536
OpenAlex
W3128046355
Cited by
31
References
8
Citations by year
2026: 4 · 2025: 2 · 2024: 1 · 2023: 12 · 2022: 10 · 2021: 2
FWCI
3.53
Type
journal article
Language
en
Open access
Licence
Locations
  • mSystems (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Israel Science Foundation (802/16)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
protein degradation, Escherichia coli, protein stability, protein properties, machine learning prediction, protein engineering, antibiotic targets, bacterial pathogenesis

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