Molecular Insights into the Engagement of High-Affinity Sialylated Ligands to Human CD22
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide CD22 is a sialic acid-binding immunoglobulin-like lectin (Siglec) that maintains a baseline level of B cell inhibition. Its function and restricted expression in B cells make CD22 a validated target in therapies against dysregulated B cells, which cause cancer and autoimmune diseases. High-affinity sialic acid-based ligands capable of competing with natural ligands to bind CD22 represent a promising therapeutic opportunity. Here, we describe the design and synthesis of a sialoside library constructed by chemical modifications on carbon C2 of 9- N -biphenylcarboxamide Neu5Ac ( BPC Neu5Ac) or 9- N -m-phenoxybenzamide Neu5FAc ( MPB Neu5FAc) scaffold using a copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction. Subsequent analysis of binding to human CD22 using competitive binding assays and isothermal titration calorimetry reveals that addition of noncarbohydrate substituents at C2 and C9 can improve the affinity toward CD22 from high micromolar to submicromolar K D values. We describe the molecular basis of this affinity improvement for three of the newly synthesized compounds by solving cocrystal structures in complex with CD22. These findings contribute to our understanding of the affinity increase of chemically modified Neu5Ac toward CD22, providing the molecular basis for further compound design of sialic acid-based molecules with potential therapeutic relevance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".