five

Anticancer peptide

收藏
DataCite Commons2025-05-01 更新2024-08-18 收录
下载链接:
https://figshare.com/articles/dataset/Anticancer_peptide/24746712/1
下载链接
链接失效反馈
官方服务:
资源简介:
Currently, anticancer peptides (ACPs) are categorized into two distinct types. The first type exhibits cytotoxic effects against bacteria and cancer cells while being non-toxic to normal cells, with notable examples including cecropins and magainins. The second type, represented by insect defensins and tachyplesin II, demonstrates destructive effects on bacteria, cancer cells, and normal cells alike. The non-toxic nature of the first type to normal cells endows them with considerable research significance and prospective applications.In our recent collection, we have focused exclusively on the first type of anticancer peptides. These peptides exhibit diversity in length and sequence, yet most share two common characteristics: cationic and amphipathic properties. Typically composed of 5 to 40 amino acids, these peptides contain arginine, lysine, and histidine, imparting them with a strong cationic nature, as evidenced by their surface net charges ranging from +2 to +9. The structural composition of these peptides, including hydrophilic and hydrophobic side chains, confers them with both hydrophilicity and lipophilicity. Most anticancer peptides possess either α-helical or β-sheet structures, wherein the amphipathic side chains are distributed on either side of the α-helix or concentrated at the extremities, resulting in distinct hydrophilic and hydrophobic faces or ends.This dataset of anticancer peptides, primarily focusing on the first type, is of paramount clinical research and application value. Therefore, we have decided to make this dataset publicly available for the prediction of broad-spectrum pharmacological anticancer peptides. This initiative not only facilitates the exploration of novel anticancer therapeutics but also contributes significantly to the advancement of cancer treatment research.
提供机构:
figshare
创建时间:
2023-12-05
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作