2018
DOI: 10.22159/ajpcr.2018.v11i1.23008
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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, … Show more

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Cited by 4 publications
(1 citation statement)
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“…The default clustering RMSD 2.0 Å was used, and the complex type was chosen to be protein–small ligand. Connolly dot surface representations of the molecules as different components such as convex, concave, and flat patches were generated using the PATCH Dock algorithm [ 57 , 58 ]. Then, the PATCH Dock solutions were optimized for small-size molecules, provides a list of refined complexes, reshuffled the molecule’s relative orientation, and the side-chains interface of the top 10 candidate solutions were rescored.…”
Section: Methodsmentioning
confidence: 99%
“…The default clustering RMSD 2.0 Å was used, and the complex type was chosen to be protein–small ligand. Connolly dot surface representations of the molecules as different components such as convex, concave, and flat patches were generated using the PATCH Dock algorithm [ 57 , 58 ]. Then, the PATCH Dock solutions were optimized for small-size molecules, provides a list of refined complexes, reshuffled the molecule’s relative orientation, and the side-chains interface of the top 10 candidate solutions were rescored.…”
Section: Methodsmentioning
confidence: 99%