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Record W2887672362 · doi:10.1002/pro.3484

Bile salt hydrolases: Structure and function, substrate preference, and inhibitor development

2018· review· en· W2887672362 on OpenAlexaff
Zixing Dong, Byong H. Lee

Bibliographic record

VenueProtein Science · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMcGill University
FundersTianjin University
KeywordsHydrolaseComputational biologyLimitingBiochemical engineeringDocking (animal)BiochemistryChemistryEnzymeBiotechnologyBiologyMedicineEngineering

Abstract

fetched live from OpenAlex

The worldwide trend of limiting the use of antibiotic growth promoters (AGPs) in animal production creates challenges for the animal feed industry, thus necessitating the development of effective non-antibiotic alternatives to improve animal performance. Increasing evidence has shown that the growth-promoting effect of AGPs is highly correlated with the reduced activity of bile salt hydrolase (BSH, EC 3.5.1.24), an intestinal bacteria-producing enzyme that has a negative impact on host fat digestion and energy harvest. Therefore, BSH inhibitors may become novel, attractive alternatives to AGPs. Detailed knowledge of BSH substrate preferences and the wealth of structural data on BSHs provide a solid foundation for rationally tailored BSH inhibitor design. This review focuses on the relationship between structure and function of BSHs based on the crystal structure, kinetic data, molecular docking and comparative structural analyses. The molecular basis for BSH substrate recognition is also discussed. Finally, recent advances and future prospectives in the development of potent, safe, and cost-effective BSH inhibitors are described.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.297
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations88
Published2018
Admission routes1
Has abstractyes

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