Deep learning-based Drug discovery of Mac domain of SARS-CoV-2 (WT) Spike inhibitors: using experimental ACE2 Inhibition TR-FRET Assay Screening and Molecular Dynamic Simulations
Bibliographic record
Abstract
Abstract SARS-CoV-2 exploits the homotrimer transmembrane Spike glycoproteins (S protein) during host cell invasion. Omicron, delta, and prototype SARS-CoV-2 receptor-binding domain show similar binding strength to hACE2 (Angiotensin-Converting Enzyme 2). Here we utilized multi-ligand virtual screening to identify small molecule inhibitors for their efficacy against SARS-CoV-2 virus using quantum Docking, pseudovirus ACE2 Inhibition TR-FRET Assay Screening, and Molecular Dynamic simulations (MDS). 350-thousand compounds were screened against the macrodomain of non-structural protein 3 of SARS-CoV-2. Using TR-FRET Assay, we filtered out two of 10 compounds that had no reported activity in in-vitro screen against Spike S1: ACE2 binding assay. Percentage Inhibition at 30 µM was found to be 79% for “Compound F1877-0839” and 69% for “Compound F0470-0003”. This first of its kind study identified “FILLY” pocket in macrodomains. Our 200 ns MDS revealed stable binding poses of both leads. They can be used for further development of preclinical candidates. Abstract Image In Brief Iqbal et al., described a deep learning guided drug discovery, efficacy against SARS-CoV-2 Spike inhibitors: using experimental pseudovirus ACE2 Inhibition TR-FRET Assay. Our molecular dynamic simulation results were next validated a posteriori against the corresponding experimental data of identified leads with 80 percent inhibition. Moreover, this study is first of kind to identify “FILLY” pocket in macrodomains. Highlights Experimental pseudovirus ACE2 Inhibition TR-FRET Assay and HTS lead to identification of two potential clinical leads. Conformational Dynamics analysis reveal the structural stability of complexes throughout 200 ns molecular dynamic simulations. Unveiling of the impact surface charge on the Variant of Concerns Detection of conformational changes within ACE2/RBD complex We identified the FILLY pocket in the SARS viruses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".