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Record W4402742164 · doi:10.1021/acs.jpcb.4c05846

Mechanism of Nucleic Acid Phosphodiester Bond Cleavage by Human Endonuclease V: MD and QM/MM Calculations Reveal a Versatile Metal Dependence

2024· article· en· W4402742164 on OpenAlexafffund
Rajwinder Kaur, Dylan J. Nikkel, Stacey D. Wetmore

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta Innovates - Technology FuturesUniversity of Lethbridge
KeywordsPhosphodiester bondNucleic acidCleavage (geology)ChemistryEndonucleaseQM/MMMechanism (biology)DNAStereochemistryBiochemistryMaterials scienceCatalysisRNAPhysicsGene

Abstract

fetched live from OpenAlex

Human endonuclease V (EndoV) catalytically removes deaminated nucleobases by cleaving the phosphodiester bond as part of RNA metabolism. Despite being implicated in several diseases (cancers, cardiovascular diseases, and neurological disorders) and potentially being a useful tool in biotechnology, details of the human EndoV catalytic pathway remain unclear due to limited experimental information beyond a crystal structure of the apoenzyme and select mutational data. Since a mechanistic understanding is critical for further deciphering the central roles and expanding applications of human EndoV in medicine and biotechnology, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations were used to unveil the atomistic details of the catalytic pathway. Due to controversies surrounding the number of metals required for nuclease activity, enzyme-substrate models with different numbers of active site metals and various metal-substrate binding configurations were built based on structural data for other nucleases. Subsequent MD simulations revealed the structure and stability of the human EndoV-substrate complex for a range of active site metal binding architectures. Four unique pathways were then characterized using QM/MM that vary in metal number (one versus two) and modes of substrate coordination [direct versus indirect (water-mediated)], with several mechanisms being fully consistent with experimental structural, kinetic, and mutational data for related nucleases, including members of the EndoV family. Beyond uncovering key roles for several active site amino acids (D240 and K155), our calculations highlight that while one metal is essential for human EndoV activity, the enzyme can benefit from using two metals due to the presence of two suitable metal binding sites. By directly comparing one- versus two-metal-mediated P-O bond cleavage reactions within the confines of the same active site, our work brings a fresh perspective to the "number of metals" controversy.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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 designSimulation or modeling
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
Published2024
Admission routes2
Has abstractyes

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