The Structure-Based Design of SARS-CoV-2 Nsp14 Methyltransferase Ligands Yields Nanomolar Inhibitors
Bibliographic record
Abstract
COVID-19, caused by the SARS-CoV-2 virus, is responsible for a global pandemic that has paralyzed the normal life in many countries around the globe. Therefore, the preparation of both effective vaccines and potential therapeutics has become a major research priority in the biotechnology sector. Both viral proteins and selected host factors are important targets for the treatment of this disease. Suitable targets for antiviral therapy include i.a. viral methyltransferases, which allow the viral mRNA to be efficiently translated and protect the viral RNA from the innate immune system. In this study, we have focused on the structure-based design of the inhibitors of one of the two SARS-CoV-2 methyltransferases, nsp14. This methyltransferase catalyzes the transfer of the methyl group from S -adenosyl- L -methionine (SAM) to cap the guanosine triphosphate moiety of the newly synthesized viral RNA, yielding the methylated capped RNA and S -adenosyl- L -homocysteine (SAH). The crystal structure of SARS-CoV-2 nsp14 is unknown; we have taken advantage of its high homology to SARS-CoV nsp14 and prepared its homology model, which has allowed us to identify novel SAH derivatives modified at the adenine nucleobase as inhibitors of this important viral target. We have synthesized and tested the designed compounds in vitro and shown that these derivatives exert unprecedented inhibitory activity against this crucial enzyme. The docking studies nicely explain the contribution of an aromatic part attached by a linker to the position 7 of the 7-deaza analogues of SAH. Our results will serve as an important source of information for the subsequent development of new antivirals to combat COVID-19.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".