Swarm optimization-based neural network model for secondary structure prediction of proteins

Abstract

Proteins form the basis of all major life processes that sustain life. The functionality of a protein is a direct consequence of its underlying structure. Protein structure prediction thus serves to ascertain the function of similar or dissimilar proteins, accordingly. Secondary structure prediction paves way for 3D structures that eventually decides protein properties. It also aims to facilitate probable structures for proteins whose structures remain undiscovered. Although experimental approaches have been quite efficient in extracting protein secondary structure from its amino acid sequence, yet it is often cumbersome and time intensive to achieve it in vitro. Hence, computational approaches are required to predict secondary structures for the diverse amino acids constituting these proteins. However, the available computational models fail to register good prediction accuracy due to inadequate modelling of sequence-structure relationship. Also, the dearth of global exploration-based methods further makes them ineffective in catering to the evolving proteomic data. Accordingly, PSO (Particle swarm optimization) has been explored to propose a neural network model for protein secondary structure prediction (PSSP). Six standard datasets namely- PSS504, RS126, EVA6, CB396, Manesh and CB513 have been utilized for the training and testing of the neural network. The proposed model is evaluated on the basis of its Q3 accuracy, precision, and recall. The 10, 20, 30 and 40 fold cross validation in combination with sensitivity analysis and has been carried out for verification of results. The proposed model is found to outperform most of the existing models by demonstrating a better average Q3 accuracy lying above 81% for PSSP.