Literature DB >> 30968770

Recent Advances of Computational Methods for Identifying Bacteriophage Virion Proteins.

Wei Chen1,2,3, Fulei Nie3, Hui Ding2.   

Abstract

Phage Virion Proteins (PVP) are essential materials of bacteriophage, which participate in a series of biological processes. Accurate identification of phage virion proteins is helpful to understand the mechanism of interaction between the phage and its host bacteria. Since experimental method is labor intensive and time-consuming, in the past few years, many computational approaches have been proposed to identify phage virion proteins. In order to facilitate researchers to select appropriate methods, it is necessary to give a comprehensive review and comparison on existing computational methods on identifying phage virion proteins. In this review, we summarized the existing computational methods for identifying phage virion proteins and also assessed their performances on an independent dataset. Finally, challenges and future perspectives for identifying phage virion proteins were presented. Taken together, we hope that this review could provide clues to researches on the study of phage virion proteins. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Bacteriophage; feature selection; host bacteria; machine learning algorithm; phage virion protein; web-server.

Year:  2020        PMID: 30968770     DOI: 10.2174/0929866526666190410124642

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN: 0929-8665            Impact factor:   1.890


  3 in total

1.  Computational identification of N6-methyladenosine sites in multiple tissues of mammals.

Authors:  Fu-Ying Dao; Hao Lv; Yu-He Yang; Hasan Zulfiqar; Hui Gao; Hao Lin
Journal:  Comput Struct Biotechnol J       Date:  2020-04-30       Impact factor: 7.271

2.  Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model.

Authors:  Wenzheng Bao; Qingyu Cui; Baitong Chen; Bin Yang
Journal:  Comput Math Methods Med       Date:  2022-04-15       Impact factor: 2.809

3.  Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features.

Authors:  Feng-Min Li; Xiao-Wei Gao
Journal:  Biomed Res Int       Date:  2020-08-02       Impact factor: 3.411

  3 in total

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