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Prediction of 3 State Secondary Structure of Bacterial Protein (Quorum-Sensing Control Repressor-Pseudomonas Aeruginosa) Using Optimized an Topology

Sangeetha Balashanmugam1*, Saravanan K2, Guru Prasad L3, Sivakumar S4

The regulation of gene expression in response to changes in cell population density is known as Quorum Sensing (QS). The bacterium called Pseudomonas aeruginosa utilizes QS to control gene transcription in humans. So it is essential to analyse the structural characteristics of Pseudomonas aeruginosa. Secondary structures of proteins have been identified as physical processes of primary sequences, folding into functional tertiary structures that allow proteins to participate in biological events of life science. Prediction of protein secondary structure from the associated amino acids sequence is importance in bioinformatics and it is a challenging assignment for machine learning based algorithms. Even though the utilization of NN for predicting secondary structure of protein is an innovative approach, it is complex at the time of network formulation. In order to overcome this problem, learning algorithm can be utilised to train the synaptic weights. Hence, in this work, the secondary structure analysis of QscR protein (Pseudomonas aeruginosa (PDB ID: 3 szT) is obtained by adopting PSO tuned neural network. It predicts the 3 state secondary structure of QscR protein. This proposed algorithm has resulted prediction of single protein domain with higher accuracy.