Bioscience Evidence 2024, Vol.14, No.5, 227-237 http://bioscipublisher.com/index.php/be 232 Figure 2 FIG 4 Comparison of the structures of active center motifs of different trimeric dUTPases (Adopted from Li et al., 2019) Image caption: (A) The four enzyme active center motifs (I, II, III, and IV) of E165R. The atom names of the dUMP are indicated and side chains of the residues that form atom contacts with the dUMP are shown as sticks. (B). The RMSD values between motifs I, II, III, and IV from ASFV E165R and from other trimeric dUTPases. (C) Superimposition of the active center motifs (I, II, III, and IV) from ASFV E165R (blue) with those of M. tuberculosis dUTPase (yellow). The side chains of the residues that form hydrogen bonds with dUMP in ASFV E165R (please refer to Fig. S1B in the supplemental material) and those of their corresponding aligned residues in M. tuberculosis are shown as sticks. (D) Superimposition of the active center motifs (I, II, III, and IV) from ASFV E165R (blue) with those of P. falciparumdUTPase (pink). The side chains of the residues that form hydrogen bonds with dUMP in ASFV E165R (please refer to Fig. 2) and those of their corresponding aligned residues in P. falciparumare shown as sticks. ECOL, Escherichia coli; MABS, Mycobacterium abscessus; MTUB, Mycobacterium tuberculosis; ATHA, Arabidopsis thaliana; PFAL: Plasmodium falciparum; CBUR, Coxiella burnetii;VACV, Vaccinia virus; BHAL, Bacillus halodurans (Adopted from Li et al., 2019) 6 Genomic and Proteomic Integration 6.1 Integration of genomic and proteomic data for comprehensive target identification The integration of genomic and proteomic data is crucial for the comprehensive identification of drug targets in African swine fever virus (ASFV). By combining these two data types, researchers can gain a more holistic view of the virus's biology and its interaction with host cells. For instance, high-throughput proteomic analyses have been used to elucidate the interactome of ASFV proteins, identifying potential interacting partners and molecular pathways involved in the infection cycle (García-Dorival et al., 2023). This approach allows for the identification of proteins that are not only encoded by the virus but also those that are significantly altered in the host cells upon infection (Alfonso et al., 2004). Such integrative studies have revealed critical insights into the roles of various proteins in processes like membrane trafficking and lipid metabolism, which are essential for ASFV infection and replication (García-Dorival et al., 2023). 6.2 Use of bioinformatics tools to predict protein-drug interactions Bioinformatics tools play a pivotal role in predicting protein-drug interactions, especially when integrating genomic and proteomic data. These tools can analyze large datasets to identify potential drug targets and predict their interactions with small molecules. For example, proteogenomic mapping has been employed to create
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