Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

Abstract

It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.

An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

“…In addition, as shown in Table 1 , we downloaded eight types of heterogeneous associations from nine other databases, 8374 pairs of miRNA-lncRNA association provided by lncRNASNP2 database, 16,427 pairs of miRNA-disease association provided by HMDD database [ 31 ], 4944 pairs of miRNA-protein association provided by miRTarBase database [ 32 ], and 1264 pairs of lncRNA-disease association provided by LncRNADisease [ 33 ] and lncRNASNP2 [ 34 ] databases. LncRNA2Target [ 35 ], DisGeNET [ 36 ], DrugBank, and STRING [ 37 ] provided 690 pairs of lncRNA-protein associations, 25,087 pairs of protein-disease associations, 11,107 pairs of drug-protein associations, and 19,237 pairs of protein–protein interactions [ 38 – 40 ]. After unifying identifiers, eliminating redundancy, simplify, and deleting irrelevant items, the downloaded experimental data are sorted out and obtained in Table 2 .…”

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

“…In a detailed and exact way, we employed PSI-BLAST to obtain the PSSM from each protein sequence for detecting SIPs. To achieve a better score and a large scale of homologous sequences, the E -value parameter of PSI-BLAST was set to be 0.001 which reported for a given result represents the quantity of two sequences’ alignments and selected three iterations in this experiment [39, 40]. Afterwards we can achieve a 20-dimensional matrix which consists of M × 20 elements based on PSSM, where M represents the count of residues of a protein, and 20 denote the 20 types of amino acids.…”

ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation

“…According to the cognition of the short length of ACPs, it’s difficult to exploit the efficient features of many mature feature representation methods, which are widely used on protein sequences 23 . With the rapid growth of the number of ACPs that has been identified experimentally, by machine learning, and by bioinformatics research,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 the computational prediction methods of ACPs still need further development.…”