Abstract

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Title:: Drug target prediction approach for SARS-CoV-2 based on metabolic analysis

Author(s):: ZHAO Yanlong; ZHENG Haoran; School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China

Keywords:: SARS-CoV-2 drug target metabolic network model highly expressed gene

Abstract:: A metabolic profiling-based method for predicting the drug targets that inhibit SARS-CoV-2 replication is presented. Five sets of the transcriptomic data of human lung histiocytes from the Gene Expression Omnibus (GEO) database are used in the study. The highly expressed genes after SARS-CoV-2 invaded the host cells are extracted, and a metabolic network model after the virus invasion is reconstructed. Some systems biology approaches such as gene knockout and cytotoxicity test are adopted to discover the drug targets. The analysis on the samples from 5 datasets in GEO shows that the target genes predicted in each dataset has certain consistency. PLPBP is the common target gene predicted in the 5 datasets, indicating that it plays an important role in the metabolic activities of SARS-CoV-2 and can be served as a potential drug target. In addition, BCAT1, BCAT2, ADI1 are also worth further exploring. The proposed method provides a new idea for predicting the drug targets for SARS-CoV-2, and the predicted drug targets are potential for further clinical research.