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Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.

Mst Shamima Khatun1, Watshara Shoombuatong1, Md Mehedi Hasan1

  • 11Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka820-8502, Japan; 2Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok10700, Thailand; 3Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo102-0083, Japan; 4Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka820-8502, Japan.

Current Genomics
|October 23, 2020
PubMed
Summary

This review explores sequence-based computational methods for predicting protein-protein interactions (PPIs). It covers current databases, state-of-the-art bioinformatics approaches, and future directions for PPI prediction.

Keywords:
PPIs databaseProtein-protein interactionsbioinformaticsfeature selectionmachine learningsequence features

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for biological processes, disease development, and drug discovery.
  • Experimental PPI identification methods are costly and time-consuming.
  • Computational approaches offer a faster, more economical alternative for PPI prediction.

Purpose of the Study:

  • To provide a comprehensive overview of sequence-based computational methods for predicting protein-protein interactions (PPIs).
  • To review existing PPI databases and state-of-the-art bioinformatics approaches.
  • To discuss limitations and future prospects for PPI prediction algorithms.

Main Methods:

  • Review of current literature on sequence-based computational methods for PPI prediction.
  • Analysis of existing protein-protein interaction databases.
  • Evaluation of the principles and performance of various bioinformatics approaches.

Main Results:

  • Identified and summarized various sequence-based computational methods for PPI prediction.
  • Highlighted the importance of PPI databases in computational approaches.
  • Discussed the performance metrics and working principles of different algorithms.

Conclusions:

  • Sequence-based computational methods are essential for efficient PPI prediction.
  • Further development of advanced algorithms is needed to overcome current limitations.
  • Future research should focus on improving the accuracy and scope of PPI prediction.