Pathway-Specific Protein Domains (PSPD) Discrimination by Using a Hybrid Feature Space Based on Deep Neural Network(DNN)

Abstract

The Pathway-specific protein domains (PSPDs) are important tools in examining drug growth as they provide a fast, reliable, and inexpensive way of estimating complex new molecular targets in specific diseases. The protein architecture prevents the formation of a direct correlation between signal transduction behavior and cellular structure. Accordingly, protein–tissue factor pathway inhibitor 2 isotypes 1 precursors have been used to encode peptide sequence information into specific feature structures. The measurable structure-activity classification model obtained by machine learning technology can predict pathway-specific protein interactions and new signaling peptides. We introduce deep neural network (DNN)-based PSPDs, abbreviated as DNNPSPDs, as the first pathway-specific protein domain that is built based on five extant models, namely, the AAindex, pseudo-amino acid composition, amino acid composition, composition mood of pseudoamino acids, and dipeptide composition. A total of 900 proteins with undetermined roles collected from the PDB data base are tested to evaluate the predictive power of this model. Various combinations of the available feature selection technologies are also combined to process a hybrid function space. DNNPSPDs predicts PSPDs by using features that are automatically learned from primary protein sequences. The sequences of pathway-associated proteins are sequentially fed into and decoded in neural network layers. Several classifications are also employed. DNNPSPDs achieves a prediction accuracy of 0.957 at a Matthew’s correlation coefficient (MCC) of 91.86%, with DPC, and 2nd achieve high prediction score 0.936 at Matthew’s correlation coefficient (MCC) of 88.02%, accuracy which is probably better. In terms of ROC–AUC, DNNPSPDs achieves a ROC–AUC curve of 0.982, which is larger than that of the other machine learning classifiers. A study using an alternative dataset reveals that our primary pathways, as pathway-specific protein domains, have accurate and reliable associations, thereby proving the viability of the proposed DNNPSPDs.