Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

Abstract

BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks

“…We utilized the PSSM matrix to transform the protein evolution information in alphabetic form into a matrix in numerical form in the experiment. PSSM [ 32 ] is able to translate protein sequences into numerical matrices and depict their biological evolutionary information [ 33 , 34 , 35 , 36 , 37 ]. In the PSSM matrix, each protein can generate a matrix , which is mathematically described below: here, means the quantity of protein residues, 20 means the quantity of amino acid types, and the matrix element denotes the probability of mutation of the ith residue to the j th amino acid.…”

“…We utilized the PSSM matrix to transform the protein evolution information in alphabetic form into a matrix in numerical form in the experiment. PSSM [32] is able to translate protein sequences into numerical matrices and depict their biological evolutionary information [33][34][35][36][37]. In the PSSM matrix, each protein can generate a N × 20 matrix PM(i, j), which is mathematically described below:…”