Peptidomics

化学 蛋白质组学 计算生物学 生物化学 鉴定(生物学) 生物 基因 植物
作者
Geert Baggerman,Peter Verleyen,Elke Clynen,Jurgen Huybrechts,Arnold DeLoof,Liliane Schoofs
出处
期刊:Journal of Chromatography B [Elsevier]
卷期号:803 (1): 3-16 被引量:127
标识
DOI:10.1016/j.jchromb.2003.07.019
摘要

Peptides occur in the whole animal kingdom, from the least evolved phyla with a very simple nervous system (coelenterates) to the highest vertebrates and are involved in most, if not all, physiological processes in animals. Knowing the amino acid sequence of peptide hormones or neurotransmitters is important since this allows for synthesis of large quantities of peptides to perform further functional analysis. Immunocytochemistry, radioimmunoassays (RIA), enzyme-linked immunosorbant assays (ELISA) and mass spectrometry can then provide information on the temporal and spatial distribution and quantification of the (neuro)peptide. Ever since the 1970s, a wealth of peptides has been discovered and investigated and this flow seems to be far from over. This is partially due to the use of new approaches mainly based on chromatographical purifications as well as molecular biological techniques. Surprisingly, peptides have so far been neglected in most proteomic studies. The finalization of the genome projects has opened new opportunities for rapid identification and functional analysis of (neuro)peptides as well. In analogy with the proteomics technology, where all proteins expressed in a cell or tissue are analyzed, the peptidomic approach aims at the simultaneous visualization and identification of the whole peptidome of a cell or tissue, i.e. all expressed peptides with their post-translational modifications (PTMs). This technology provides us with a fast and efficient tool to analyze the peptides from any tissue. This paper reviews the approaches that have been used so far to achieve this.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
量子星尘发布了新的文献求助10
1秒前
huay完成签到,获得积分10
1秒前
2秒前
yang发布了新的文献求助10
2秒前
2秒前
Mcdull完成签到,获得积分10
2秒前
2秒前
酷波er应助典雅的俊驰采纳,获得10
3秒前
3秒前
大意的帽子完成签到,获得积分10
4秒前
量子星尘发布了新的文献求助10
4秒前
科研笨猪发布了新的文献求助10
4秒前
不知道发布了新的文献求助10
5秒前
loathebm发布了新的文献求助10
5秒前
5秒前
6秒前
温暖凡灵完成签到,获得积分10
6秒前
孟德尔吃豌豆完成签到,获得积分10
6秒前
7秒前
7秒前
7秒前
7秒前
MWY发布了新的文献求助10
7秒前
8秒前
8秒前
开心书本发布了新的文献求助10
8秒前
8秒前
共享精神应助未雨绸缪采纳,获得10
8秒前
wjt发布了新的文献求助10
8秒前
8秒前
柯柯发布了新的文献求助10
8秒前
何妍发布了新的文献求助10
8秒前
8秒前
SJJ应助香香采纳,获得10
9秒前
9秒前
香蕉觅云应助liao采纳,获得10
10秒前
紧张的冷卉完成签到,获得积分10
10秒前
Lucas应助Djnsbj采纳,获得10
11秒前
雨落千年发布了新的文献求助10
11秒前
loathebm发布了新的文献求助10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Exploring Nostalgia 500
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
Advanced Memory Technology: Functional Materials and Devices 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5668670
求助须知:如何正确求助?哪些是违规求助? 4892290
关于积分的说明 15125387
捐赠科研通 4827622
什么是DOI,文献DOI怎么找? 2584752
邀请新用户注册赠送积分活动 1538503
关于科研通互助平台的介绍 1496841