计算毒力蛋白互作网络分析与侵袭性曲霉菌病相关的药物发现
In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery.
机构信息
Centre for Biotechnology, Maharshi Dayanand University, Rohtak-124001, Haryana, India.
Centre for Bioinformatics, Maharshi Dayanand University, Rohtak-124001, Haryana, India.
出版信息
Curr Top Med Chem. 2019;19(2):146-155. doi: 10.2174/1568026619666181120150633.
BACKGROUND
Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis.
MATERIALS & METHOD: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.
CONCLUSION
Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.
背景
烟曲霉毒力蛋白的蛋白质-蛋白质相互作用（PPI）网络分析是理解烟曲霉毒力背后机制的一种流行策略。鉴定主要枢纽蛋白并将枢纽蛋白作为新的抗真菌药物靶点，将有助于治疗侵袭性曲霉病。
材料与方法
本研究对 96 种烟曲霉毒力（药物靶点）蛋白的 PPI 网络进行了研究，结果得到 103 个节点和 430 个边。还使用 STRING 数据库和 Network analyzer a cytoscape 插件应用程序对 PPI 网络进行了拓扑富集分析。通过 STRING 分析了关键富集的 KEGG 途径和蛋白质结构域。
结论
通过人工 PPI 数据的验证，鉴定出了烟曲霉中具有最高相互作用伙伴的三种蛋白（PyrABCN-43、AroM-34 和 Glt1-34）。还使用 cytohubba 基于七种算法（介数、辐射度、接近度、度数、瓶颈、MCC 和 EPC）从网络中识别出了前 10%的枢纽蛋白。还预测了前三种枢纽蛋白的同源模型和活性口袋。
Zotero 插件