Integrated Transcriptome Guided Identification and in Silico Validation of Venom Nerve Growth Factor NGF from Naja Naja as a Structure based Anti-Venom Target

Abstract

Snakebite continues to pose a major challenge across many tropical regions [1-3], Causing thousands of mortality and morbidity annually. Antivenom that saves life do not detect all the toxin but sometimes trigger bad immune responses [4-6]. With the help of advanced technology like transcriptomics and computational biology [7-9] Researchers can now zoom in various venom protein in the complex venom cocktail and study them down to their molecular detail for better treatment and targets you see structure-based method.

In this study, a transcriptome guided bioinformatics workflow was used to identify and validate a reliable venom Target from Naja Naja. Venom gland RNA-seq data Were assembled De novo. Followed by protein prediction and functional annotation. Among the detected toxin families Venom nav growth factor was identified as structurally complete and computationally tractable candidate for structure-based analysis. Structure based virtual screening of selected phytochemicals was performed using molecular docking and the top ranked complexes were analysed through 100 ns molecular dynamic simulations under NPT conditions. Stability analysis including RMSD, RMSF, interaction persistence and secondary structure evaluation confirmed sustained ligand binding during the simulation. Both Leg Binded to the targets attacker site Where diosmin settled faster while Hesperidin showed fluctuations towards the end of the simulation. The ADMET prediction for the selected candidates Showed promising pharmacokinetic and safety results. This study shows that combining transcriptomics with structure-based modelling is a solid way to pinpoint venom target and spot new helper molecules It’s a smart move to push antivenom therapy forward.