Abstract

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Title:: General target prediction method for inhibiting viral replication based on metabolic differential analysis

Author(s):: FENG Ziye; QI Yupeng; ZHENG Haoran; School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China

Keywords:: Keywords: viral disease differential metabolic network metabolic differential analysis target prediction

Abstract:: Abstract: A general target prediction method for inhibiting viral replication based on metabolic differential analysis is proposed. This method employs a differential metabolic network model, referred to as the DELTA network, which utilizes metabolic differences in host cells before and after infection to focus on virus-induced metabolic subsystems, thereby effectively eliminating tissue specificity associated with infections in different hosts. The model which is reconstructed by integrating host gene expression differences pre- and post-infection with the viral biomass objective function is used to identify candidate targets through single-gene knockout simulations and cytotoxicity assessments. The results show that CTH and DCTD are present, either alone or in combination, in the predicted target gene sets across all 15 viruses, demonstrating a certain degree of broad-spectrum potential in suppressing viral replication. The proposed method exhibits strong generalizability in predicting antiviral targets, enabling effective identification of potential broad-spectrum candidates and providing new insights into viral disease research, thereby enhancing the efficiency of targeted antiviral therapies.