MétaCan
Menu
Back to cohort
Record W4387735905 · doi:10.1021/acsomega.3c02835

Assay Development for Metal-Dependent Enzymes─Influence of Reaction Buffers on Activities and Kinetic Characteristics

2023· article· en· W4387735905 on OpenAlexafffund
Natalia Forero, Chengsong Liu, Sami G. Sabbah, Michèle C. Loewen, Trent Chunzhong Yang

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Catalyzed Oxygenation Mechanisms
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council Canada
KeywordsTrisHEPESChemistryEnzyme kineticsHydroxymethylChelationMetal ions in aqueous solutionPhosphateMetalInorganic chemistryNuclear chemistryEnzymeStereochemistryActive siteOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Buffers are often thought of as innocuous components of a reaction, with the sole task of maintaining the pH of a system. However, studies had shown that this is not always the case. Common buffers used in biochemical research, such as Tris (hydroxymethyl) aminomethane hydrochloride (Tris-HCl), can chelate metal ions and may thus affect the activity of metalloenzymes, which are enzymes that require metal ions for enhanced catalysis. To determine whether enzyme activity is influenced by buffer identity, the activity of three enzymes (BLC23O, Ro 1,2-CTD, and trypsin) was comparatively characterized in N -2- hydroxyethylpiperazine- N ′-2-ethanesulfonic acid (HEPES), Tris-HCl, and sodium phosphate buffer. The pH and temperature optima of BLC23O, a Mn 2+ -dependent dioxygenase, were first identified, and then the metal ion dissociation constant ( K d ) was determined in the three buffer systems. It was observed that BLC23O exhibited different K d values depending on the buffer, with the lowest (1.49 ± 0.05 μM) recorded in HEPES under the optimal set of conditions (pH 7.6 and 32.5 °C). Likewise, the kinetic parameters obtained varied depending on the buffer, with HEPES (pH 7.6) yielding overall the greatest catalytic efficiency and turnover number ( k cat = 0.45 ± 0.01 s –1; k cat / K m = 0.84 ± 0.02 mM –1 s –1 ). To corroborate findings, the characterization of Fe 3+ -dependent Ro 1,2-CTD was performed, resulting in different kinetic constants depending on the buffer ( K m (HEPES, Tris-HCl, and Na-phosphate) = 1.80, 6.93, and 3.64 μM; k cat (HEPES, Tris-HCl, and Na-phosphate) = 0.64, 1.14, and 1.01 s –1; k cat / K m (HEPES, Tris-HCl, and Na-phosphate) = 0.36, 0.17, and 0.28 μM –1 s –1 ). In order to determine whether buffer identity influenced the enzymatic activity of nonmetalloenzymes alike, the characterization of trypsin was also carried out. Contrary to the previous results, trypsin yielded comparable kinetic parameters independent of the buffer (K m (HEPES, Tris-HCl, and Na-Phosphate) = 3.14, 3.07, and 2.91 mM; k cat (HEPES, Tris-HCl, and Na-phosphate) = 1.51, 1.47, and 1.53 s –1; kcat/ K m (HEPES, Tris-HCl, and Na-phosphate) = 0.48, 0.48, and 0.52 mM –1 s –1 ). These results showed that the activity of tested metalloenzymes was impacted by different buffers. While selected buffers did not influence the tested nonmetalloenzyme activity, other research had shown impacts of buffers on other enzyme activities. As a result, we suggest that buffer selection be optimized for any new enzymes such that the results from one lab to another can be accurately compared.

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 score0.007
Threshold uncertainty score0.686

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 head0.256
Teacher spread0.233 · 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

Citations22
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueACS OmegaSame topicMetal-Catalyzed Oxygenation MechanismsFrench-language works237,207