Arumuganainar Deepavalli, Anegundi Raghavendra Vamsi, Ganesh P R, Yadalam Pradeep Kumar
Department of Periodontics, Saveetha Dental College, SIMATS, Saveetha University, Chennai, Tamil Nadu, India.
Department of Periodontics, Government Dental College, Chennai, Tamil Nadu, India.
J Indian Soc Periodontol. 2025 Mar-Apr;29(2):175-181. doi: 10.4103/jisp.jisp_311_24. Epub 2025 Aug 19.
Matrix metalloproteinases (MMPs) are essential endopeptidases involved in matrix degradation and remodeling, including periodontal tissues. They are classified into collagenases, gelatinases, stromelysin, matrilysin, and membrane types. MMPs, particularly MMP-2 and 9, contribute to gingival tissue breakdown in periodontitis. The study uses Graph Attention Network (GAT) to predict drug-gene associations for MMP-9 in host modulation, a crucial aspect of disease diagnosis, prognosis, targeted therapies, personalized medicine, and mechanistic studies. This approach can optimize treatment outcomes and minimize side effects, contributing to precision medicine.
Data on drugs and genes associated with MMP-9 were retrieved using probes and drugs, and 1898 drug-gene interactions were studied. Data were cleaned for missing values, and graph data were prepared using nodes, gene names, and edges. Edge weights represented biochemical activity, while node features provided additional details for training a GAT. Cytoscape was used to create a network graph for drug-gene associations, while Cytohubba applied the maximum clique centrality algorithm to a drug-gene interaction network. A GAT model, consisting of three layers, was applied using Google Colab in a Python environment.
The network graph has 742 nodes, 1897 edges, and an average number of neighbors of 5.049. It has a characteristic path length of 3.303, with low local connectivity, and sparseness. The top-ten hubs with drug-gene associations with MMP-9 include quercetin, luteolin, econazole, zinc chloride, curcumin, MMP-9, MMP2, MMP1, MMP13, and MMP3. The model faces issues due to a dataset imbalance, with 80% of positive cases overfitting the majority class. Despite this, it learns useful features from the graph structure and shows stable training. The GAT model achieved an accuracy of 0.7955, indicating 80% correct classification, and an F1 score of 0.8861.
This study explores the intricate relationship between drugs, genes, and MMP-9, using a GAT tool to identify potential drug targets. Addressing limitations can advance MMP-9 biology and develop new therapeutic strategies.
基质金属蛋白酶（MMPs）是参与基质降解和重塑的重要内肽酶，包括牙周组织。它们分为胶原酶、明胶酶、基质溶解素、基质溶素和膜型。MMPs，特别是MMP-2和9，在牙周炎中导致牙龈组织破坏。该研究使用图注意力网络（GAT）来预测宿主调节中MMP-9的药物-基因关联，这是疾病诊断、预后、靶向治疗、个性化医疗和机制研究的关键方面。这种方法可以优化治疗效果并最小化副作用，有助于精准医疗。
使用探针和药物检索与MMP-9相关的药物和基因数据，并研究了1898种药物-基因相互作用。对数据进行清理以去除缺失值，并使用节点、基因名称和边来准备图数据。边权重表示生化活性，而节点特征为训练GAT提供了额外细节。使用Cytoscape创建药物-基因关联的网络图，而Cytohubba将最大团中心性算法应用于药物-基因相互作用网络。在Python环境中使用Google Colab应用了由三层组成的GAT模型。
网络图有742个节点、1897条边，平均邻居数为5.049。其特征路径长度为3.303，局部连通性低且稀疏。与MMP-9有药物-基因关联的前十位枢纽包括槲皮素、木犀草素、益康唑、氯化锌、姜黄素、MMP-9、MMP2、MMP1、MMP13和MMP3。由于数据集不平衡，该模型面临问题，80%的阳性病例过度拟合多数类。尽管如此，它从图结构中学习到有用特征并显示出稳定的训练。GAT模型的准确率为0.7955，表明分类正确率为80%，F1分数为0.8861。
本研究使用GAT工具探索药物、基因和MMP-9之间复杂的关系，以识别潜在的药物靶点。解决局限性可以推动MMP-9生物学发展并开发新的治疗策略。
