Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 Main Protease complexes reveal the role of a lateral pocket in enhancing the ligand affinity
Bibliographic record
Abstract
Abstract The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and a number of countries are already in the middle of the second wave of infection. There is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (M pro ) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 M pro that resulted in hundreds of X-ray crystal structures of ligand bound M pro complexes in the protein data bank (PDB) that describe structural details of different chemotypes of fragments binding within different sites in M pro . In this work, we perform rigorous molecular dynamics (MD) simulation of 62 reversible ligand-M pro complexes in the PDB to gain mechanistic insights about their interactions at atomic level. Using a total of ~2.25 μs long MD trajectories, we identified and characterized different pockets and their conformational dynamics in the apo M pro structure. Later, using the published PDB structures, we analyzed the dynamic interactions and binding affinity of small ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets in M pro . Our analyses unraveled the role of a lateral pocket in the catalytic site in M pro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in this lateral pocket towards ligand binding and affinity against M pro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and M pro dimerization in the ligand association with the target. Thus, comprehensive molecular level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 M pro .
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".