iLytic-PseAAC: Prediction of Cell wall lytic and non-lytic enzymes with highest accuracy of the predictor by integrating statistical moments and position relative features into Chou’s PseAAC
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Date
2019
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UMT, Lahore
Abstract
Bacteriophage producing lytic enzymes are anti-infective molecules that are capable of digesting cell wall of targeted bacteria, specifically of gram-positive bacterium, causing effective bacterial cell wall lysis and consequences in ultimate death of the targeted bacterium. This lysin driven phenomenon of bacterial death is an operative tool to operate against antibiotic resistance of pathogenic bacteria due to their specificities for the pathogen and low bacterial resistance towards lysin. Thus, for controlling antibiotic resistance and infection diseases, the discrimination of lytic from non-lytic enzymes is somewhat extremely crucial that requires the reliable and comprehensive computational method that can precisely predict and discriminate the two groups of lysins. This study comprehends the construction of novel prediction model to serve the proposed purpose. We developed the prediction model based on artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through cross-validation with the Jackknife testing that was computed to be 93.60%,93.06 % sensitivity, and 94.06 % specificity. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection.