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Record W7116705329 · doi:10.1002/pro.70412

Structural basis of product recognition by <i>Mycobacterium tuberculosis</i> fatty acid synthase

2025· article· en· W7116705329 on OpenAlexafffund
Elnaz Khalili Samani, S M Naimul Hasan, Alexander F. A. Keszei, Mahtab Heydari, Mohammad T. Mazhab‐Jafari

Bibliographic record

VenueProtein Science · 2025
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPrincess Margaret Cancer Foundation
KeywordsFatty acid synthaseFatty acidAcyl carrier proteinSaccharomyces cerevisiaeYeastCofactorTransferaseCoenzyme AMycobacterium tuberculosis

Abstract

fetched live from OpenAlex

Microbial iterative fatty acid synthases (FAS) are versatile multienzymes under scrutiny for their potential as anti-infectious targets and their biotechnological applications. They produce saturated fatty acids with defined chain length and release them as coenzyme A-conjugates. How they recognize appropriate acyl length to initiate the process of product release is unknown. Here, we resolved two intermediate state structures of FAS, one from each of the two organisms: bacterium Mycobacterium tuberculosis and yeast Saccharomyces cerevisiae. These structures reveal how acyl carrier protein (ACP) domain and nascent fatty acids interact with the substrate-promiscuous malonyl-palmitoyl transferase (MPT) domain that is involved in product cleavage from the enzyme. MPT adopts a transient channel necessary for the accommodation of long-chain fatty acids. This channel is formed by the transient retraction of a conserved arginine side chain involved in malonate binding. These insights uncover structural determinants that enable M. tuberculosis type I FAS to produce very long-chain fatty acids used for evading host immunity in tuberculosis (TB).

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 score0.020
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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