Recent Advances in Computational Methods for Identifying Anticancer Peptides

被引:6
|
作者
Feng, Pengmian [1 ]
Wang, Zhenyi [2 ]
机构
[1] North China Univ Sci & Technol, Sch Publ Hlth, Tangshan 063000, Peoples R China
[2] North China Univ Sci & Technol, Sch Life Sci, Ctr Genom & Computat Biol, Tangshan 063000, Peoples R China
关键词
Anticancer peptides; disease; cancer; drug target; machine learning methods; sequence encoding scheme; AMINO-ACID-COMPOSITION; MODEL QUALITY ASSESSMENT; S-NITROSYLATION SITES; FEATURE-SELECTION; GENERAL-FORM; SUBCELLULAR-LOCALIZATION; THERAPEUTIC PEPTIDES; PSEUDO; PROTEINS; IDENTIFICATION;
D O I
10.2174/1389450119666180801121548
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.
引用
收藏
页码:481 / 487
页数:7
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