结蛋白的疏水性

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722034
P. Anto, S. N. Achuthsankar
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引用次数: 0

摘要

蛋白质结构在其天然折叠中具有打结构型,对其功能有很大影响。蛋白结定位虽然在结构水平上得到了鉴定,但在单分子实验中已成为可能。信号处理方法在基因组和蛋白质组学序列分析中发挥了重要作用,也可用于结蛋白分析。氨基酸疏水性指数有助于了解蛋白质的稳定性。发现水的捕获和释放是由与该指数有关的结的拧紧力控制的。通过傅里叶分析、功率谱密度估计和相互关系分析发现,结蛋白具有疏水性。利用proteinKNOT web server(pKNOT)上的结蛋白集进行实验,结果证明93%的结蛋白在其结核中具有疏水性。
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Hydrophobic tint of knot proteins
Protein structures having knotted configurations in their native fold, have great impact in their function. Protein knot localization has become possible in single molecule experiments though they are identified in their structure level. Signal processing methods which have played an important role to analyse genomic and proteomic sequences are also useful for knot protein analysis. The amino acid index hydrophobicity contributes the knowledge of stability of proteins. Water capture and release is found to be controllable by the tightening force in knots which are related to this index. It is observed that, the knot proteins are of hydrophobic in nature by Fourier analysis, Power spectral density estimation and Cross correlation method. The set of knot proteins from proteinKNOT web server(pKNOT) has been used for the experimentation and proved 93% of them are of hydrophobic nature in their knotted core.
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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