Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
Embedded Devices & Intelligent Systems, TCS Research & Innovation, Kolkata, India.
Sci Rep. 2024 Aug 12;14(1):18736. doi: 10.1038/s41598-024-69617-8.
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 .
猴痘（Mpox）是一种由猴痘病毒（MPXV）引发的人畜共患病，具有传播风险且目前尚无治愈方法，因此构成了严重威胁。本研究提出了一种预测 MPXV 感染过程中蛋白-蛋白相互作用（PPIs）的计算方法，旨在发现潜在的药物靶点并将现有潜在的美国食品和药物管理局（FDA）药物重新用于治疗目的。在这项工作中，使用包含 2-5 个节点图节点属性和基于蛋白质组成的特征的集成特征来为深度学习（DL）模型预测 PPIs。该技术在人类综合蛋白-蛋白相互作用参考（HIPPIE）和 MPXV-人类 PPI 数据集上均对 PPI 进行了出色的预测。此外，还准确地鉴定了 MPXV 的人类蛋白靶标，并检测到可能的治疗靶标。此外，通过对重新利用的药物进行研究并对 DrugBank 中识别的潜在 FDA 药物（如烟酰胺腺嘌呤二核苷酸和氢（NADH）、福他替尼、谷氨酸、大麻二酚、铜和锌）进行药物共识评分（DCS）研究，对潜在 FDA 药物进行了对接研究。这是通过使用现在可用的 MPXV 的主要晶体结构来实现的。对接研究还得到了分子动力学（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 预测来理解病毒感染涉及的分子过程，并为重新利用药物治疗新兴传染病（如但不限于猴痘）提供帮助的有效性。本研究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-Suggest-Therapies-For-Emerging-Threats-like-Mpox 。
