Toxicoproteomics approach and computational modeling for study of dracunculus medinensis

Abstract

This research is based on the conceptual model that is system architecture development of nematodes (Helminths), application of the protein structural analysis computational software and tools and annotation for bioinformatics based synthetic peptide designing. newlineThe objective of the following study includes: newlineand#61623; High throughput screening of Dracunculus medinensis proteins. newlineand#61623; Sequence analysis, computational modeling and patterns comparison of active protein. newlineand#61623; Functional annotation and target confirmation for peptide epitopes newlineIn order to address the above issue, the research contribution is: newlineand#61623; The high throughput screening, classification, and identification of the nematode protein sequences have been performed through an advanced computational approach to complete analysis of the desired protein sequences. The advanced Database Management System (DBMS) has been used for data mining and data manipulation. newlineand#61623; The nematode protein sequences were characterized and identified to study the protein antigenicity, solvent accessible regions, which could be essential for finding the active sites against allergic reaction and drug development. newlineand#61623; The Artificial Neural network has been used for the MHC binding peptides prediction of the protein sequences. newlineii newlineand#61623; The Support Vector Machine (SVM) was applied for the detection of promiscuous MHC class II binding peptides. The combinational method of the SVM (Support Vector Machine) and ANN (artificial neuron network) prediction approach were used to establish the upper limit of sensitivity, accuracy, and specificity to achieve the prediction accuracy. newlineand#61623; An automated neural network bases protein modeling server which reveals various important aspects of protein structure and its function like protein interaction expression pattern, surface activity, binding sites, and electrostatic potentials. newlineand#61623; The antigenicity prediction methods predict the specific segments from protein that are likely to be antigenic and that capable to elicit an antibody response. The prediction of the