Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern
Bibliographic record
Abstract
The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein’s receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro . Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4–31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 μM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD’s surface.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".