Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein–protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.

中文翻译：

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从差异表达基因的蛋白质组网络中筛选动脉粥样硬化药物靶点

动脉粥样硬化样本分析的不同表达基因有助于对影响斑块形成和进展的突出基因进行分类。以科学证据为基础的蛋白质-蛋白质相互作用网络 (PPIN) 研究用于在复杂疾病条件下寻找中枢蛋白。成药能力是确认为成功药物靶点的重要参数之一。在动脉粥样硬化数据集上构建蛋白质相互作用网络和主节点分析（PNA）导致筛选中枢蛋白。此外，蛋白质口袋的可成药特性证实了易感靶标候选物的靶向性和靶标选择。通过 GEO2R 分析仪在各种动脉粥样硬化样本的数据集上鉴定差异表达的基因。使用 STRING 数据库和 Cytoscape 构建 PPIN。目标由 PNA 识别，例如中心性度量和聚类算法。基因本体富集也用作筛选参数之一，以过滤与动脉粥样硬化术语相关的候选者。ITASSER和GROMACS分别成功地完成了目标蛋白的拓扑评估。DoGSiteScorer 的基于网格的原理用于成药性分析。六种蛋白质，如整合素 α L (ITGAL)、金属硫蛋白 1F (MT1F)、金属硫蛋白 1X (MT1X)、P-选择蛋白糖蛋白配体-1 (SELPLG)、溶质载体家族 30 A、锌转运蛋白 (SLC30A1) 和 TNFSF13B通过基于网络的分析筛选为潜在的生物标志物。在这六个中，ITGAL、SELLPG、SLC30A1 和 TNSF13B 通过成药效率被确定为优先考虑的动脉粥样硬化靶点。