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Record W4281801898 · doi:10.3389/fnut.2022.874259

Identification of Di/Tripeptide(s) With Osteoblasts Proliferation Stimulation Abilities of Yak Bone Collagen by in silico Screening and Molecular Docking

2022· article· en· W4281801898 on OpenAlexaff
Yongkai Chen, Yujie Guo, Yusi Liu, Chunhui Zhang, Feng Huang, Lingyun Chen

Bibliographic record

VenueFrontiers in Nutrition · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsChemistryOsteoblastStimulationCell growthAlkaline phosphataseIn silicoTripeptideReceptorEndocrinologyOsteoporosisEstrogen receptorInternal medicinePharmacologyCell biologyBiochemistryPeptideBiologyMedicineIn vitro

Abstract

fetched live from OpenAlex

Endothelial protein C receptor (EPCR), cannabinoid receptor 2 (CBR2), and estrogen receptor α (ERα) play vital roles in osteoblasts proliferation. Also, collagen peptides have osteoblasts proliferation stimulation abilities, and di/tri-peptides could be absorbed by the intestine more easily. This study obtained three di/tripeptides with potential osteoblasts proliferation stimulation abilities of yak bone collagen, namely, MGF, CF, and MF, by in silico screening. Results suggested that these three peptides exhibited good absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. They also had strong affinities with EPCR, CBR2, and ERα, and the total -CDOCKER energy (-CE) values were 150.9469, 113.1835, and 115.3714 kcal/mol, respectively. However, further Cell Counting Kit-8 (CCK-8) assays indicated that only MGF could significantly ( P < 0.05) stimulate osteoblasts proliferation at 0.3 mg/ml. At the same time, the proliferating index (PI) of the osteoblasts treated with MGF increased significantly ( P < 0.05), and the alkaline phosphatase (ALP) activity decreased highly significantly ( P < 0.01). In summary, MGF exhibited the potential to be an effective treatment for osteoporosis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.205
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2022
Admission routes1
Has abstractyes

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