Prediction Of SARS-CoV-2 Virus Host Protein And Protein Complexes Based On Core-attachment

With the rapid development of high-throughput technology,more and more reliable biological resource information has been produced.It has become one of the research hotspots in the field of bioinformatics to comprehensively analyze the structure of protein-protein interaction network and the connection pattern of nodes by combining with biological big data.In particular,the prediction of protein complexes or functional modules by PPI network plays a fundamental role in the in-depth analysis of the functional mechanisms of proteins in different time and space in the cell environment,and it is also of great significance for human beings to explore new models of drug research and development.At present,many graph clustering algorithms used to find the community structure in complex networks have been applied in the field of protein complex recognition.However,most of the existing methods usually ignore the biological structure of the complex.Secondly,many methods only consider the complex as a dense subgraph,and then ignore the biological function of the complex itself.Aiming at the existing problems,this thesis starts from the topological structure of known complexes,and makes an indepth research and Analysis on the recognition framework of protein complexes based on core appendage.The main work of this paper is as follows:1)In this thesis,a covering clustering core appendage protein complex recognition algorithm CCA-SE(Seed extended protein complex recognition algorithm based on coverage clustering)is proposed.PPI network has the characteristics of community structure,and the topological properties of PPI network and known complexes are analyzed.Protein complexes are mainly composed of highly interconnected core structures and edge protein sets with auxiliary functions.Therefore,on the basis of introducing the core subsidiary framework,this thesis combines the covering clustering algorithm to mine the core structure of the complex,so as to cluster the PPI network.The algorithm is tested on several common protein relationship network datasets of yeast and human species.The recognition results show that CCA-SE has better recognition rate than MCODE,MCL and COACH.In addition,the cases identified by the algorithm also show that the algorithm can obtain compounds with biological significance.2)This thesis presents a new method for virus host protein prediction based on complex and Node2 Vec algorithm.In this thesis,the SARS-CoV-2 virus host protein interaction map and human protein network were integrated,and the integrated network was analyzed by cluster analysis.Based on the clustering results,the topological characteristics of PPI network were analyzed,and the prediction results of host protein were obtained.The analysis on GO and KEGG shows that the algorithm is practical and plays a fundamental role in the detection of viral drug targets.