兼职蛋白质-一种系统化概念的方法。

Q2 Medicine In Silico Biology Pub Date : 2020-01-01 DOI:10.3233/ISB-190473
Maria Krantz, Edda Klipp
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引用次数: 5

摘要

“兼职蛋白”指的是一种蛋白质至少具有两种不相关的、机械上不同的功能。作为一个概念,兼职描述了在众多生物体中发现的大量多样的蛋白质群。到目前为止,对这些蛋白质的系统化观点还没有形成。在这里,我们提出了一个由两个分类器的兼职蛋白分类。我们使用蛋白质的功能作为第一分类器:激活-激活(I型),激活-抑制(II型),抑制-激活(III型)和抑制-抑制(IV型)。为了进一步明确兼职蛋白质的类型,我们使用了基于改变蛋白质功能的因子的特征的第二分类器:影响蛋白质的外部因素(A型),第一途径的变化(B型),第二途径的变化(C型),两种途径之间的平等竞争(D型)。使用一个小型的双途径模型,我们模拟了这些类型的兼职蛋白,以阐明这些类型的兼职蛋白的可能行为。我们发现,利用我们的模拟结果,我们可以将兼职类型的行为分为Blinker, Splitter和switch。
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Moonlighting proteins - an approach to systematize the concept.

Moonlighting refers to a protein with at least two unrelated, mechanistically different functions. As a concept, moonlighting describes a large and diverse group of proteins which have been discovered in a multitude of organisms. As of today, a systematized view on these proteins is missing. Here, we propose a classification of moonlighting proteins by two classifiers. We use the function of the protein as a first classifier: activating - activating (Type I), activating - inhibiting (Type II), inhibiting - activating (Type III) and inhibiting - inhibiting (Type IV). To further specify the type of moonlighting protein, we used a second classifier based on the character of the factor that switches the function of the protein: external factor affecting the protein (Type A), change in the first pathway (Type B), change in the second pathway (Type C), equal competition between both pathways (Type D). Using a small two-pathway model we simulated these types of moonlighting proteins to elucidate possible behaviors of the types of moonlighting proteins. We find that, using the results of our simulations, we can classify the behavior of the moonlighting types into Blinker, Splitter andSwitch.

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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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