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Record W2545978226 · doi:10.17918/etd-6781

Kinetics of oxygen transport in monomeric sarcosine oxidase

2016· dissertation· en· W2545978226 on OpenAlexfundno aff
Anthony Bucci

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
FundersYork UniversityNational Institutes of HealthNational Science Foundation
KeywordsSarcosineKineticsChemistryOxygenOxygen transportBiochemistryOrganic chemistryPhysicsGlycine

Abstract

fetched live from OpenAlex

Flavin-containing oxidases are a class of proteins which use oxygen to regenerate the oxidized state of the isoalloxazine ring in flavin adenine dinucleotide (FAD) or flavin mononucleotide (FMN) after it has been reduced by substrate oxidation. Though well characterized experimentally, many questions linger regarding how small molecules access the active site as well as where oxygen activation occurs in flavin-containing oxidases. A prototypical member of this family is monomeric sarcosine oxidase (MSOX), and it is perhaps the most well studied. Despite knowing which features are essential for catalysis as well as the location of the substrate binding site, it is unclear how oxygen accesses the site since the only clear entryway can be partially blocked by the larger substrate sarcosine. As such, two competing mechanisms have gained attention centering around the order by which ligands enter the binding site. In this thesis, we detail the use of all atom molecular dynamics (MD) studies to identify how oxygen accesses the MSOX active site, as well as characterize the resulting kinetic network. We use the single sweep method to identify four potential routes for oxygen to travel from the surface of MSOX to the active site. Then, using Markovian milestoning with Voronoi tessellations (MMVT), we refine the pathways identified in single sweep and develop a Markov state model describing the kinetics of oxygen entry and exit. We calculate entry and exit mean first passage times (MFPT) for oxygen from this model, which are used to compute second order rate constants for entry and first order rate constants for exit. Our calculated rate constants and mechanisms show that the presence of a substrate-mimicking inhibitor markedly influences the kinetics of oxygen entry and exit. The bound competitive inhibitor changes the protein structure sufficiently to shut down almost all major oxygen channels save one, which it opens, speeding entry but greatly slowing down oxygen exit, relative to the substrate-free enzyme. This means that our kinetic analysis predicts oxygen exhibits a longer residence time within MSOX when a substrate-like ligand is present. This supports the so-called "modified ping-pong" mechanism, in agreement with previous experimental results, thus lending validity to our approach. Furthermore, our computed second-order entry rate constants are larger by about an order of magnitude than are experimentally determined oxygen consumption rate constants. Since oxygen consumption combines the processes of entry and electron transfer, we conclude that of these two, entry is not rate-limiting in the overall catalytic cycle, regardless of whether or not a substrate-like ligand is bound. Finally, because this work represents the first test of the MMVT approach for comparing kinetics of ligand entry for an enzyme in two distinct states, we not surprisingly uncovered inefficiencies in the approach. We tested one idea for gaining efficiency based on the "finite-temperature" string method, in which transport channels can be determined and kinetically characterized on-the-fly, rather than sequentially. Our results indicate that more research in that area is needed.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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