Bioactive peptide design using the Resonant Recognition Model.
Bioactive peptide design using the Resonant Recognition Model.
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DOI：
10.1186/1753-4631-1-7
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发表时间：
2007-07-19
期刊：
影响因子：
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通讯作者：
Pirogova, Elena
中科院分区：
文献类型：
--
作者：
Cosic, Irena;Pirogova, Elena
在已知大量DNA和蛋白质序列的情况下，关键问题是找出这些大分子的生物学功能是如何“写入”核苷酸或氨基酸序列的。任何生物体中的生物过程都是基于特定生物分子（主要是蛋白质）之间的选择性相互作用。控制蛋白质生物功能编码的规则，即它选择性地与其他分子相互作用的能力，仍然没有阐明。此外，随着蛋白质一级结构数据库的快速积累，迫切需要能够分析蛋白质结构-功能关系的理论方法。共振识别模型（RRM）12是识别氨基酸序列内蛋白质相互作用的选择性的一种尝试。RRM 12是一种物理-数学方法，使用数字信号处理方法解释蛋白质序列线性信息。在RRM中，蛋白质一级结构通过为序列中的每个氨基酸分配与蛋白质生物活性相关的物理参数值来表示为数字序列。RRM的概念是基于这样的发现，即氨基酸的数字表示的光谱与其生物活性之间存在显著的相关性。一旦鉴定了特定蛋白质功能/相互作用的特征频率，则可以利用RRM方法来预测蛋白质序列中的氨基酸，其主要贡献于该频率并因此贡献于观察到的功能，以及设计具有所需周期性的从头肽。如我们先前对成纤维细胞生长因子（FGF）肽拮抗剂23和人免疫缺陷病毒（HIV）包膜激动剂24的研究所示，这种从头设计的肽表达所需的生物学功能。本研究利用RRM计算方法分析癌基因和原癌基因蛋白。所获得的结果表明，RRM能够识别致癌蛋白和原癌蛋白之间的差异，并有可能识别其蛋白质一级结构中的“致癌”特征。此外，合理设计的生物活性肽类似物显示致癌或原癌样活性在这里。
With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) 12 is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM 12 is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists 23 and human immunodeficiency virus (HIV) envelope agonists 24, such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here.