State Key Laboratory of Non-food Biomass Energy and Enzyme Technology, National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Nanning, Guangxi, China.
PLoS One. 2011;6(12):e28206. doi: 10.1371/journal.pone.0028206. Epub 2011 Dec 5.
The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family.
A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P(0) is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P(0) and individual proteins P(i) of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P(i) to the standard protein P(0). Correlation analysis is used to analyze the communication network among segments.
The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.
具有相似生物学功能的蛋白质家族中的蛋白质，其氨基酸组成可能有很大差异，但它们必须具有相似的 3D 结构，并保持稳定的中心区域。在保守结构区域，两个或三个催化残基与几个关键位置的功能残基协同作用，执行相似的生物学功能。在蛋白质家族中，通信信号通过位置网络进行传递，调节蛋白质的生物学功能。
开发了一种计算方法，即结构位置相关分析（SPCA），用于分析结构片段（或位置）之间的相关性。SPCA 的基本假设是，在蛋白质家族中，结构保守性比序列保守性更重要，局部结构变化可能包含生物学功能进化的信息。在蛋白质家族中定义了一个标准蛋白质 P(0)，它由最常见的氨基酸组成，并采用家族中所有蛋白质的平均结构。SPCA 的基本变量是标准蛋白质 P(0)和家族中各个蛋白质 P(i)之间的结构位置位移。结构位置被组织成片段，这些片段是蛋白质家族结构位移的稳定单元。蛋白质成员的生物学功能差异由个体蛋白质 P(i)的位置结构位移到标准蛋白质 P(0)来确定。相关性分析用于分析片段之间的通信网络。
结构位置相关分析（SPCA）能够发现蛋白质家族中结构片段（或位置）之间的相关性，而这些相关性无法通过氨基酸序列和基于频率的方法检测到。SPCA 方法揭示了蛋白质家族中结构片段（或位置）之间的功能通信网络，很好地说明了远程变构相互作用，并为蛋白质工程研究提供了有价值的信息。
