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A survey of current trends in computational predictions of protein-protein interactions

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Abstract

Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the state-of-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.

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Acknowledgements

This work was supported in part by Awardee of the NSFC Excellent Young Scholars Program in 2017, in part by the National Natural Science Foundation of China (Grant Nos. 61902342, 61722212 and 61572506). The authors would like to thank the editors and anonymous reviewers for their constructive advices.

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Authors and Affiliations

  1. Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China

    Yanbin Wang, Zhuhong You, Liping Li & Zhanheng Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yanbin Wang & Zhanheng Chen

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  1. Yanbin Wang
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  2. Zhuhong You
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  3. Liping Li
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  4. Zhanheng Chen
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Correspondence to Zhuhong You.

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Yanbin Wang received his BE degree in Computer Science and Technology from Zhengzhou University, China in 2015. He obtained his MS degree in Computer Science from University of Chinese Academy of Sciences (UCAS), China in 2018. He is currently a research assistant with the Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, China. His current research interests include deep neural networks, big data, signal processing, and its applications in bioinformatics.

Zhuhong You received his BE degree in Electronic Information Science and Engineering from Hunan Normal University, China in 2005. He obtained his PhD degree in control science and engineering from University of Science & Technology of China (USTC), China in 2010. From June 2008 to November 2009, he was a visiting research fellow at the Center of Biotechnology and Information, Cornell University, USA. He is currently a professor with the Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, China. His current research interests include neural networks, big data, intelligent information processing, sparse representation, and their applications in bioinformatics.

Liping Li received his BE degree in architectural engineering from Gansu Agricultural University, China in 2006. She received his Master degree in School of Computer Science from Shenzhen University, China in 2016. She is currently an association professor with the Xijing University, China. Her current research interests include data mining algorithms, neural networks, pattern recognition, and its applications in bioinformatics.

Zhanheng Chen is currently pursuing the PhD degree with the University of Chinese Academy of Sciences, China. His current research interests include data mining, natural language processing, and pattern identification. He has several publications in journals (Published in Molecular Therapy-Nucleic Acids, BMC Genomics, BMC Systems Biology, Frontiers in Genetics, International Journal of Molecular Sciences, and so on), and international conferences (such as RECOMB, ISBRA, ICIC, ICIBM, and so on).

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Wang, Y., You, Z., Li, L. et al. A survey of current trends in computational predictions of protein-protein interactions. Front. Comput. Sci. 14, 144901 (2020). https://doi.org/10.1007/s11704-019-8232-z

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