Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient,Computational and Structural Biotechnology Journal

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Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2021-09-20 , DOI: 10.1016/j.csbj.2021.09.014
Sara Omranian _{1,

2} , Angela Angeleska ₃ , Zoran Nikoloski _{1,

2}

Affiliation

Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from , , and , we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in , with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes.

中文翻译：

基于聚类系数从蛋白质-蛋白质相互作用网络中高效准确地识别蛋白质复合物

从蛋白质-蛋白质相互作用 (PPI) 网络中识别蛋白质复合物是 PPI 挖掘中的一个关键问题，该问题通过召回率较低的参数依赖方法来解决。在这里，我们介绍 GCC-v，这是一系列高效、无参数的算法，可使用 PPI 网络中蛋白质的（加权）聚类系数准确预测蛋白质复合物。通过与来自、、和的黄金标准和 PPI 网络的比较分析，我们证明 GCC-v 在至少 85.71% 的场景中，在 12 项性能指标方面优于 12 种最先进的蛋白质复合物识别方法。我们还表明，GCC-v 可以精确回收泛植物 PPI 网络中约 35% 的蛋白质复合物，并在 GO 语义相似性的高度支持下发现了 144 个新的蛋白质复合物。我们的结果表明，GCC-v 的研究结果对网络扰动具有稳健性，这对于评估 PPI 网络质量对预测蛋白质复合物的影响具有直接意义。

更新日期：2021-09-20

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