Integrative homology, AI-based modelling of PfSET3 and virtual screening of microbial-derived inhibitors as potential anti-malarial agents

整合同源性分析、基于人工智能的PfSET3建模以及微生物来源抑制剂作为潜在抗疟药物的虚拟筛选

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Abstract

Malaria continues to be a major global health crisis, affecting millions and causing significant mortality each year, especially as resistance to existing treatments grows. In response to this urgent problem, this study focused on the epigenetic regulator Plasmodium falciparum Su (var) Enhancer of Zeste and Trithorax-3 (PfSET3), a histone methyltransferase in Plasmodium falciparum that is crucial for silencing virulence genes that contribute to the parasite's persistence and severity. Using computational approaches, PfSET3 models were generated with ColabFold (an AI method) and MODELLER. These models were carefully validated for structural quality using tools such as the Ramachandran plot, z-score plots, and residual energy plots from the ProSA-web server. To ensure their reliability, both PfSET3 models underwent molecular dynamics simulations, which confirmed their stability. Building on this foundation, the pharmacophoric features were derived from known inhibitors of Plasmodium falciparum Histone Lysine Methyl Transferase (PfHKMT), a related epigenetic target, and used to virtually screen compounds from the Microbial Metabolite Database (MiMeDB). This process led to the identification of four promising compounds that merit further experimental validation. These findings highlight the potential of targeting PfSET3 in the development of new antimalarial therapies. The computational work and the identified lead compounds offer a valuable starting point for designing novel inhibitors, potentially leading to more effective drugs that can overcome resistance and improve malaria control. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-026-00606-7.

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