An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein

Abstract

The Ca2+ binding sites of proteins are critical for their function, particularly in processes such as signal transduction, enzyme regulation, and structural stability. In this study, the calcium-binding sites of NtEhCaBP1 (Entamoeba histolytica calcium-binding protein). This paper proposes Statistical Ranking Deep Learning (SR-ML) to estimate the binding affinities of ten protein variants, The proposed SR-ML model computes the features in the proteins with the detection of sequences in the bindings. The classification of binding sites evaluated with the optimization of the features. With each predicted variant’s binding affinity correlates well with its experimental value with Kendall Tau (τ) values ranging from 0.78 to 0.95 and Spearman rank correlation (ρ) ranging from 0.75 to 0.94. Specifically, the Root Mean Square, Deviation (RMSD) shows protein flexibility in values of 0.95 to 1.50 angstrom and Root Mean Fluctuation (RMSF) values of 0.30 angstrom to 0.50 angstrom. The binding energy falls from negative 4.90 kcal/mol to negative 7.20 kcal/mol proposing differing levels of protein stability. Secondly, considering calcium coordination geometry we describe how there are octahedral, tetrahedral and trigonal bipyramidal structures in various proteins, with Kd values of 0.3 uM to 5.0 uM. The anti-AIDS bioactive example of mutagenesis validation is at a 120-folds to 600-folds increase from binding affinity for several mutations involving dynamic correlation with values of between 0.88 to 0.97. These outcomes reveal that the SR-ML model has certain predictive preciseness in terms of the Ca-binding sites and protein motions, which is valuable for Drug designing involving the Ca signalling Pathway.