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DGPD:一种 Apicomplexa 致密颗粒蛋白的知识库。

DGPD: a knowledge database of dense granule proteins of the Apicomplexa.

机构信息

School of Information and Computer, College of Animal Science and Technology, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, 130 Changjiangxilu, Hefei, Anhui 230036, P. R. China.

出版信息

Database (Oxford). 2022 Sep 27;2022. doi: 10.1093/database/baac085.

DOI:10.1093/database/baac085
PMID:36164976
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9513560/
Abstract

Apicomplexan parasites cause severe diseases in human and livestock. Dense granule proteins (GRAs), specific to the Apicomplexa, participate in the maintenance of intracellular parasitism of host cells. GRAs have better immunogenicity and they can be emerged as important players in vaccine development. Although studies on GRAs have increased gradually in recent years, due to incompleteness and complexity of data collection, biologists have difficulty in the comprehensive utilization of information. Thus, there is a desperate need of user-friendly resource to integrate with existing GRAs. In this paper, we developed the Dense Granule Protein Database (DGPD), the first knowledge database dedicated to the integration and analysis of typical GRAs properties. The current version of DGPD includes annotated GRAs metadata of 245 samples derived from multiple web repositories and literature mining, involving five species that cause common diseases (Plasmodium falciparum, Toxoplasma gondii, Hammondia hammondi, Neospora caninum and Cystoisospora suis). We explored the baseline characteristics of GRAs and found that the number of introns and transmembrane domains in GRAs are markedly different from those of non-GRAs. Furthermore, we utilized the data in DGPD to explore the prediction algorithms for GRAs. We hope DGPD will be a good database for researchers to study GRAs. Database URL: http://dgpd.tlds.cc/DGPD/index/.

摘要

疟原虫等顶复门寄生虫可引起人类和家畜的严重疾病。致密颗粒蛋白(GRAs)是顶复门所特有的,参与宿主细胞内寄生的维持。GRAs 具有更好的免疫原性,它们可以成为疫苗开发的重要参与者。尽管近年来对 GRAs 的研究逐渐增加,但由于数据收集的不完整性和复杂性,生物学家难以全面利用信息。因此,非常需要一个用户友好的资源来整合现有的 GRAs。在本文中,我们开发了致密颗粒蛋白数据库(DGPD),这是第一个专门用于整合和分析典型 GRAs 特性的知识库。目前的 DGPD 版本包括来自多个网络存储库和文献挖掘的 245 个样本的注释 GRAs 元数据,涉及五种引起常见疾病的物种(恶性疟原虫、刚地弓形虫、哈氏巴贝斯虫、犬新孢子虫和猪等孢球虫)。我们探索了 GRAs 的基线特征,发现 GRAs 的内含子和跨膜结构域数量与非 GRAs 明显不同。此外,我们利用 DGPD 中的数据来探索 GRAs 的预测算法。我们希望 DGPD 将成为研究人员研究 GRAs 的一个很好的数据库。数据库网址:http://dgpd.tlds.cc/DGPD/index/。

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StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides.StackDPPIV:一种用于准确预测二肽基肽酶 IV(DPP-IV)抑制肽的新型计算方法。
Methods. 2022 Aug;204:189-198. doi: 10.1016/j.ymeth.2021.12.001. Epub 2021 Dec 6.
3
Biotinylation of the Neospora caninum parasitophorous vacuole reveals novel dense granule proteins.
刚地弓形虫寄生泡生物素化揭示新的致密颗粒蛋白。
Parasit Vectors. 2021 Oct 9;14(1):521. doi: 10.1186/s13071-021-05023-7.
4
Proximity-dependent biotinylation approaches to study apicomplexan biology.用于研究顶复门生物学的邻近依赖性生物素化方法。
Mol Microbiol. 2022 Mar;117(3):553-568. doi: 10.1111/mmi.14815. Epub 2021 Oct 22.
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Plasmodium falciparum is evolving to escape malaria rapid diagnostic tests in Ethiopia.恶性疟原虫正在进化以逃避在埃塞俄比亚的疟疾快速诊断检测。
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