Prediction of protein folding kinetics states using hybrid brainstorm optimization

Abstract

ABSTRACT Protein folding procedure is tremendously vital in determining the molecular role. The protein folding kinetics states select if the molecule influences the natural structure of intermediates or not. The fold can be complete with stable intermediates (3-State/3S) or without stable intermediates (2-State/2S). Protein folding is regularly determined, using experiments and often takes time and dreary. Furthermore, there are significant numbers of unfolding mechanisms that are available in the PDB to originate unidentified. Henceforth, it made curiosity and we focused on classifying and envisaging the folding mechanism as three-state or two-state. The authors established the clustering models by modified brainstorm optimization (MBSO) using the Fuzzy C-Means (FCM) algorithm through known parameters (hydrophobicity, length, hydrophilicity, and secondary structural components) to predict the protein folding kinetic states. The prototypes implemented well enough for envisaging three-state and two-state folding using the recognized features. MBSO using FCM shows a better model when compared with MBSO. The MBSO using FCM algorithm produced good accuracy in Davies-Bouldin index and Dunn index for clustering. The result depicts the better performance of MBSO using FCM techniques with the best prediction when compared with MBSO. In future, we can further extend to predict protein fold recognition.