PDF(Pubmed)
Abstract：
Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein-Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2-5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein-Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox .
摘要：
猴痘(Mpox),由猴痘病毒（MPXV）引发的人畜共患疾病，构成重大威胁，因为它可能会传播，无法治愈。这项工作介绍了一种计算方法来预测MPXV感染期间的蛋白质-蛋白质相互作用（PPI）。目的是发现预期的药物靶标，并将当前潜在的食品和药物管理局（FDA）药物用于治疗目的。在这项工作中,合奏功能，包括2-5个节点的graphlet属性和基于蛋白质组成的特征用于深度学习(DL)模型来预测PPI。此处使用的技术在人类整合蛋白质-蛋白质相互作用参考(HIPPIE)和MPXV-HumanPPI数据集上都证明了PPI的优异预测性能。此外,MPXV的人蛋白靶标已被准确鉴定,同时检测可能的治疗靶标.此外，验证过程包括对潜在的FDA药物进行对接研究，如烟酰胺腺嘌呤二核苷酸和氢（NADH），福司替尼,谷氨酸,大麻二酚,铜,通过对药物再利用和MPXV药物共识评分（DCS）的研究，确定了药店中的锌。这是通过采用MPXV的主要晶体结构来实现的，现在可以访问。对接研究也得到了分子动力学（MD）模拟的支持。我们的研究结果强调了使用基于集成特征的PPI预测的有效性，以了解病毒感染中涉及的分子过程，并帮助开发用于新出现的传染病的再利用药物，例如，但不限于,水痘.此工作中使用的源代码和数据链接可在以下网址获得：https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-to-suggestest-Therapies-For-Emerging-Threats-like-Mpox。
