Hunan Institute of Science and Technology, Yueyang, 414006, China.
Hunan Engineering Research Center of Multimodal Health Sensing and Intelligent Analysis, Yueyang, 414006, China.
BMC Bioinformatics. 2024 Apr 20;25(1):157. doi: 10.1186/s12859-024-05772-z.
The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.
Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.
To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.
鉴定必需蛋白质有助于理解细胞生存和发育的最低要求，以发现药物靶点并预防疾病。如今，节点排序方法是鉴定必需蛋白质的常用方法，但底层 PIN 数据质量较差，在某种程度上阻碍了这些方法在 PIN 中鉴定必需蛋白质的准确性。因此，研究人员通过考虑相互作用蛋白质对的某些生物学特性来构建细化网络，以提高节点排序方法在 PIN 中的性能。研究表明，与不在复合物中的蛋白质相比，复合物中的蛋白质更有可能是必需的。然而，对于 PIN 的细化方法，通常会忽略模块性。
基于此，我们提出了一种基于模块发现和生物信息的网络细化方法。该方法的思路是，首先从 PIN 中提取最大连通子图，并使用快速展开算法将其划分为不同的模块；然后根据同源信息、亚细胞定位信息和每个模块内的拓扑信息检测关键模块；最后，使用识别出的关键模块构建更细化的网络（CM-PIN）。
为了评估所提出方法的有效性，我们使用了 12 种典型的节点排序方法（LAC、DC、DMNC、NC、TP、LID、CC、BC、PR、LR、PeC、WDC），比较了 CM-PIN 与 S-PIN、D-PIN 和 RD-PIN 在整体性能方面的差异。实验结果表明，CM-PIN 在鉴定必需蛋白质的数量、精度-召回曲线、Jackknifing 方法等方面表现最佳，能够更准确地识别必需蛋白质。
