Gibbs entropy simulated annealing based Edman firefly optimization for big data protein sequencing

Abstract

SummaryProtein sequencing is a significant problem to be solved to better understand its structure and function. Few research works have been designed for protein sequencing using different techniques. The performance of conventional protein sequencing methods was not effectual as sequencing accuracy of proteins was lower when considering a big protein dataset. In order to overcome this limitation, a Gibbs Entropy Simulated Annealing based Edman FireFly Optimization (GESA‐EFFO) technique is proposed. The GESA‐EFFO technique is designed for sequencing the amino acid residue in peptides with higher accuracy and minimal time. The GESA‐EFFO technique develops Gibbs Entropy Simulated Annealing (GESA) with the objective of selecting the relevant amino acid features from the big dataset with lower time complexity. The GESA applied Gibbs Entropy in order to accurately determine cost (ie, changes in heat energy) for each amino acid feature and thereby chooses the related amino acid features in a big protein dataset for efficient protein sequencing. After identifying the relevant features, the GESA‐EFFO technique designs the Edman FireFly Optimization (EFFO) where amino acid features are ranked based on the light intensity of the firefly to form protein sequences. The experimental evaluation of the GESA‐EFFO technique is carried out on factors such as precision.