Mainali Nirjal, Balasubramaniam Meenakshisundaram, Johnson Jay, Ayyadevara Srinivas, Shmookler Reis Robert J
Bioinformatics Program, University of Arkansas for Medical Sciences and University of Arkansas at Little Rock, Little Rock, AR, 72205, USA.
Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
Bioinformation. 2024 Jan 31;20(1):4-10. doi: 10.6026/973206300200004. eCollection 2024.
Many age-progressive diseases are accompanied by (and likely caused by) the presence of protein aggregation in affected tissues. Protein aggregates are conjoined by complex protein-protein interactions, which remain poorly understood. Knowledge of the proteins that comprise aggregates, and their adherent interfaces, can be useful to identify therapeutic targets to treat or prevent pathology, and to discover small molecules for disease interventions. We present web-based software to evaluate and rank influential proteins and protein-protein interactions based on graph modelling of the cross linked aggregate interactome. We have used two network-graph-based techniques: Leave-One-Vertex-Out (LOVO) and Leave-One-Edge-Out (LOEO), each followed by dimension reduction and calculation of influential vertices and edges using Principal Components Analysis (PCA) implemented as an R program. This method enables researchers to quickly and accurately determine influential proteins and protein-protein interactions present in their aggregate interactome data.
许多随年龄发展的疾病都伴随着（并且可能是由）受影响组织中蛋白质聚集的存在而引起的。蛋白质聚集体通过复杂的蛋白质-蛋白质相互作用结合在一起，而人们对这些相互作用仍知之甚少。了解构成聚集体的蛋白质及其附着界面，有助于识别治疗或预防病理的治疗靶点，并发现用于疾病干预的小分子。我们展示了基于网络的软件，用于基于交联聚集体相互作用组的图模型评估和排名有影响力的蛋白质和蛋白质-蛋白质相互作用。我们使用了两种基于网络-图的技术：留一顶点法（LOVO）和留一边法（LOEO），每种技术之后都进行降维和使用作为R程序实现的主成分分析（PCA）计算有影响力的顶点和边。这种方法使研究人员能够快速准确地确定其聚集体相互作用组数据中存在的有影响力的蛋白质和蛋白质-蛋白质相互作用。
