A virus–target host proteins recognition method based on integrated complexes data and seed extension

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 1 - 256
• Main Authors: Xia, Shengrong, Xia, Yingchun, Xiang, Chulei, Wang, Hui, Wang, Chao, He, Jin, Shi, Guolong, Gu, Lichuan
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 28.06.2022; BioMed Central; BMC
• Subjects: Algorithms; Clustering; Coronaviruses; COVID-19; Disease resistance; Disease transmission; Drug resistance; Drug targeting; Drugs; Evaluation; Health services; Host protein; Identification and classification; Machine learning; Medical treatment; Methods; Network topologies; Physiological aspects; Protein complexes recognition; Protein-protein interactions; Proteins; Protein–protein interaction network; Recognition; Respiratory diseases; Standard scores; Topology; Viruses; Virus–target protein; West Nile virus; China
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04792-x

Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus-target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus-target host proteins and reduce the cost of disease treatment. Since protein complexes contain virus-target host proteins, it is reasonable to predict virus-target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus-target host proteins, the human protein-protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus-target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments. Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus-target host proteins.