Identification of Synthetic Peptides that Inhibit Lipopolysaccharide (LPS) Binding to Myeloid Differentiation Protein-2 (MD-2)
Bibliographic record
Abstract
Many studies have suggested that the synergic effect of myeloid differential protein-2 (MD-2) on bacterial lipopolysaccharide (LPS) stimulation of toll-like receptor 4 (TLR4) may be a critical step during the LPS-TLR4 response signaling pathway. We performed a bioinformatic analysis on the MD-2 protein and identified the amino acid sequence NH2-FSKGKYKCV-COOH (K128-132) as a possible key sequence involved in the binding between MD-2 and LPS. We then screened a random phage display peptide library using this sequence as bait in order to identify antagonistic peptides. After 3 rounds of selection, 3 positive clones were identified. All 3 peptides were shown to inhibit, in a dose-dependent manner the production of tumor necrosis factor-α and interleukin-6 in human U937 and THP-1 cell lines as well as human peripheral blood monocytes stimulated by LPS. Only 2 of the 3 peptides were able to bind MD-2 directly as shown by sulfo-SBED biotin label transfer experiments. BALB/C mice were used to estimate the protection of these peptides from LPS challenge, and 2 of the 3 peptides (Lys-Thr-Val-Pro-Asp-Asn-His and Ile-Gly-Lys-Phe-Leu-Tyr-Arg) reduced mortality of the challenged mice from 100% to 53.8%. This study has demonstrated that interfering with the binding between MD-2 and LPS might be a potential therapeutic strategy for treating LPS-induced sepsis, and in doing so has identified 2 potential peptide candidates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".