COMPUTATIONAL APPROACHES FOR THE PREDICTION OF ANTIMICROBIAL POTENTIAL PEPTIDES FROM OCIMUM TENUIFLORUM.

Abstract

Objective: In this study, antimicrobial activity was predicted against novel antimicrobial target 1SDE receptor to understand the structural feature of predicted peptides using machine learning approach from Ocimum tenuiflorum. Methods: Protein sequences from O. tenuiflorum were digested using peptide cutter and further processed for the prediction of antimicrobial peptide (AMP) through AMP predictor tool of CAMP which have multidimensional algorithms such as support vector machine, artificial neural network, random forest, and discriminant analysis. After analyzing various peptides, only four peptides were predicted as antimicrobial in nature. Furthermore, the three-dimensional structure of different potential peptides was generated with the help of Pepfold-3.0 server followed by protein-peptide docking studies with novel target receptor with the help of PatchDock, FireDock webserver, and Hex 8.0 software. Interactions were further visualized using Discovery Studio Client 2.5 software tool.Results: This study revealed that peptide 2 resulted higher score in PatchDock and FireDock and also Hex 8.0 provides E total value of −430.18 which is higher than that of peptide 1 with −381.07, peptide 3 with −416.86, and peptide 4 with −407.94.Conclusion: The peptide predicted in this study has potential to act as effective AMP against target receptor and also utilize other antimicrobial target.