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Record W2092180841 · doi:10.1021/ed081p1048

Chemical Modification of Papain and Subtilisin: An Active Site Comparison. An Undergraduate Biochemistry Experiment

2004· article· en· W2092180841 on OpenAlexaff
Mireille St-Vincent, Michael H. Dickman

Bibliographic record

VenueJournal of Chemical Education · 2004
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversité de Saint-Boniface
Fundersnot available
KeywordsSubtilisinPapainProteasesChemistrySerine proteaseCysteineSerinePhenylmethylsulfonyl FluorideActive siteResidue (chemistry)EnzymeStereochemistryBiochemistryProtease

Abstract

fetched live from OpenAlex

This experiment demonstrates the specific chemistry of cysteine and serine residues in the active sites of papain and subtilisin. While both protease enzymes catalyze the same reaction on similar substrates, papain uses cysteine to cleave peptide bonds and subtilisin employs serine for the same transformation. Treatment of both enzymes with methyl methanethiosulfonate and phenylmethylsulfonyl fluoride modifies papain and subtilisin, respectively, rendering them inactive. Methyl methanethiosulfonate reacts rapidly and exclusively with available thiols to form mixed disulfides. In the case of papain, the sole available thiol is cysteine-25 in the active site. Once this residue is modified, no further enzymatic activity is observed. Interestingly, the free thiol at cysteine-25 can be easily regenerated from the mixed disulfide, and the return of efficient catalysis can be observed. Phenylmethylsulfonyl fluoride reacts irreversibly with activated serines resulting in near complete inhibition of serine proteases. Subtilisin is modified at serine-221 to give the sulfonic ester rendering the enzyme ineffective at forming an acyl–enzyme complex with a substrate. The comparison of papain and subtilisin highlights the active site differences between the two proteases. The entire experiment can be completed in two, three-hour laboratory periods, or it could be shortened to a single, three-hour period.

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 score0.005
Threshold uncertainty score0.798

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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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