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Record W7132889260

Characterization and Design of Nickel-binding Proteins towards Metal Recovery Applications

2023· dissertation· W7132889260 on OpenAlexaff
Patrick Diep

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeriplasmic spaceProtein engineeringFluorescenceQuenching (fluorescence)MutagenesisTransmembrane proteinCharacterization (materials science)
DOInot available

Abstract

fetched live from OpenAlex

Nickel is an important metal in the adoption of greener technologies needed to transition to cleaner economies, and mines face pressure to source it more sustainably. Nature has evolved nickel-specific ATP-binding cassette (ABC) importers that can transport nickel across a cell’s membrane from the environment into the cytoplasm. Specifically, their solute-binding component known as the nickel-binding protein (NiBP) scavenges the cell’s periplasmic space to deliver metals to the cognate permease complex for transport. Existing methods for metal-binding characterization are not amenable to protein engineering efforts that rely on screening libraries of variants with different substrates. Using natural changes in protein fluorescence during a metal-binding event, we first optimized an assay previously reported in literature to measure the binding affinity (K¬D) of NiBP complexes with nickel and other metals. This assay was validated by determining the KD of the well-characterized NiBP CjNikZ from Campylobacter jejuni, which was comparable to reported values, so it was then used to demonstrate the presence of Ni(II)-binding activity in the uncharacterized NiBP called CcNikZ-II from Clostridium carboxidivorans. Next, we determined the crystal structure of CcNikZ-II, which revealed the potential Ni(II)-binding site located close to a short variable loop. Mutagenesis identified the CcNikZ-II residues involved in Ni(II) binding, but the role of the variable (v-)loop of this protein (TEDKYT) remained unclear. We purified nine CcNikZ-II homologues and determined the nickel-binding affinity of these proteins using an intrinsic fluorescence quenching assay, which showed all these proteins have higher KD for Ni(II) than CcNikZ-II. Furthermore, we replaced the CcNikZ-II v-loop sequence (TEDKYT) with those from the other homologues with higher affinity and found that the engineered CcNikZ-II variants have a higher binding affinity to Ni(II). Metal promiscuity screening further demonstrated the importance of the secondary coordination sphere in controlling affinity and specificity. Finally, an engineered E. coli strain (Ni_v.1) was created and tested in 10 ppm NiCl2 solution matching environmental conditions and demonstrated 7-fold improvement in nickel bioaccumulation performance compared to controls. Thus, both wildtype and engineering microbial NiBPs can be engineered for improved metal binding and selectivity and used for developing bio-based technologies for metal recovery applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.373
Teacher spread0.319 · 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
Published2023
Admission routes1
Has abstractyes

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