Homology modelling and docking studies on Neuraminidase enzyme as a natural product target for combating influenza
Bibliographic record
Abstract
Influenza remains to be dreadful with yearly epidemics and sudden pandemic outbreaks causing significant mortality, even in nations with the most advanced health care systems. Thus, there has been a long-standing interest to develop effective and safe antiviral agents to treat infected individuals. Attempt to identify suitable molecular targets as antiviral compounds have focused recently on the influenza virus neuraminidase (NA), a key enzyme in viral replication [1]. In this research, virtual screening was done on a total of 600 natural compounds from 22 ethno medicinal Indian herbs for activity against neuraminidase enzyme exploiting representative protein conformations selected from molecular dynamics simulations. Neuraminidase enzyme sequences from different existing strains available on National Center of Biotechnology Information [2] (NCBI) protein database were aligned using Clustal W [3] and CLC workbench 10 [4] to find the conserved residues. Neuraminidase protein sequence from H1N1 strain available on NCBI was used to structure 3D target model predicted against dataset from Protein data bank using modeller [5]. The target model was validated on different parameter at SAVES Server [6]. Using this target model a pharmacophore model was developed using ligand based strategy exploiting the three known inhibitors. The docking parameters were validated by redocking Zanamivir to its co-complex 2009 H1N1 NA crystal structure (PDB ID: 3TI5) generating best pose with a RMSD value of 0.7543 A°. This model was then used for in silico analysis of a library of natural compounds from 22 ethno medicinal Indian herbs known to have antiviral activity taken downloaded from PubChem database and selected on the basis of drug likeliness. All the compounds were docked in the binding pocket of neuraminidase. Top compounds having binding affinity better than or comparable to the control drug Zanamivir were selected and analyzed for their ADME and toxicity. Their binding pattern in the 150 loop was studied along with their interaction with the active site conserved residues. Electrostatic interactions were the main driving force in the binding affinity of the potential inhibitors. These hit compounds will provide direction for further in- vivo and in -vitro validation and may play an important role in finding novel Neuraminidase inhibitors against influenza.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".