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

Yanbin Wang, Zhuhong You, Liping Li, Zhanheng Chen

Research output: Contribution to journalReview articlepeer-review

26 Scopus citations

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.

Original languageEnglish
Article number144901
JournalFrontiers of Computer Science
Volume14
Issue number4
DOIs
StatePublished - 1 Aug 2020
Externally publishedYes

Keywords

  • computational proteomics
  • protein feature extraction
  • protein-protein interactions
  • proteomics

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