Murakami Yoichi, Mizuguchi Kenji
Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-Ku, Chiba, 265-8501 Japan.
Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita-Shi, Osaka, 565-0871 Japan.
Biophys Rev. 2022 Dec 24;14(6):1393-1411. doi: 10.1007/s12551-022-01038-1. eCollection 2022 Dec.
The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.
蛋白质-蛋白质相互作用(PPI)的鉴定有助于更好地理解蛋白质的细胞功能和生物学过程,并有助于设计针对致病PPI的药物。此外,靶向宿主-病原体PPI对于阐明感染机制很有用。尽管已经使用了几种实验方法来鉴定PPI,但这些方法尚未绘制出完整的PPI网络。因此,越来越需要计算技术来预测潜在的PPI,这些PPI从未在实验中被观察到。最近基于序列的高性能方法有助于构建PPI网络并阐明特定疾病的发病机制。然而,这些方法的有效性取决于PPI训练数据的质量和数量。在这篇简短的综述中,我们介绍了目前可用的PPI数据库以及最近基于序列的PPI预测方法。此外,我们讨论了该领域的关键问题,并展示了基于序列的PPI预测的未来前景。