Abstract

Saint Louis encephalitis virus, a member of the $aviviridae subgroup, is a culex mosquito-borne pathogen. Despite severe epidemic outbreaks on several occasions, not much progress has been made with regard to an epitope-based vaccine designed for Saint Louis encephalitis virus. Covalent binding is an important mechanism for many drugs to gain its function. Computational algorithms to model this chemical event and extended it to a web server have been previously generated. The CovalentDock Cloud provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis.These novel hyperstructures were generated by Using the BiogenetoligandorolTM AND the CovalentDock Cloud: a web server for automated covalent docking. An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. Here, in Biogenea we have discovered for the first time an Improved Computational Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization to Design an Epitope-Based Mimo-Peptidic hyper agonist consisting of linked active Pharmacophoric chemo-Scaffolds comprising in silico demonstrated vaccine-like potential properties against Saint Louis Encephalitis Virus conserved binding domains.

Keywords

Improved Quantum-Behaved; Particle Swarm; Optimization Algorithm; Elitist Breeding; Unconstrained Optimization; Computational Assay; Design an Epitope-Based; Mimo-Peptidic; hyper agonist; inked active Pharmacophoric; chemo-Scaffolds; in silico; vaccine-like; potential properties; Saint Louis Encephalitis ;Virus conserved; binding domains.