Pathogen-Associated Molecular Patterns: The Synthesis of Heptose Phosphates and Derivatives
Bibliographic record
Abstract
Abstract Lipopolysaccharide biosynthesis metabolites, such as d-glycero-β-d-manno-heptopyranosyl 1,7-diphosphate, d-glycero-β-d-manno-heptopyranosyl phosphate, and adenosine 5′-(l-glycero-β-d-manno-heptopyranosyl)diphosphate, have been found to activate NF-κB via alpha-kinase 1 and TRAF-interacting protein with forkhead associated domain. This axis has been determined as a novel pathway of innate immunity yet to be targeted for immunomodulatory treatment approaches. Key in understanding this new axis has been the ability to synthesize these metabolites. The design of synthetic analogues and probes have also been published not only to design new drugs, but also to gain insight into the mechanism of action for these compounds. The focus of the present review is the synthesis of heptose phosphate metabolites as well as synthetic analogues and probes. 1 Introduction 2 Synthesis of d-glycero-d-manno-Heptose 2.1 Using d-Mannose as Starting Material 2.2 Using d-Ribose as Starting Material 2.3 Using 2,2-Dimethyl-1,3-dioxan-5-one as Starting Material 3 Synthesis of l-glycero-d-manno-Heptose 3.1 Using d-Mannose as Starting Material 3.2 Using 2,2-Dimethyl-1,3-dioxan-5-one as Starting Material 3.3 Using l-Lyxose as Starting Material 4 Synthesis of Heptose Phosphates 4.1 Synthesis of d-glycero-β-d-manno-Heptose 1,7-Diphosphate 4.2 Synthesis of Heptose Phosphate Derivatives 4.2.1 Development of Scaffolds for Conjugation 4.2.2 Development of Heptose Phosphates Derivatives for Cell Intake and Metabolic Stability 5 Conclusion and Outlook
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".