ALGORITHMS FOR THE OPTIMAL IDENTIFICATION OF SEGMENT NEIGHBORHOODS
ALGORITHMS FOR THE OPTIMAL IDENTIFICATION OF SEGMENT NEIGHBORHOODS
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DOI：
10.1016/s0092-8240(89)80047-3
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发表时间：
1989-01-01
影响因子：
3.5
通讯作者：
LAWRENCE, CE
中科院分区：
文献类型：
--
作者：
AUGER, IE;LAWRENCE, CE
提出了两种有效识别分段邻域的算法。区段邻域是共享共同特征的一组连续残基。两个程序的开发，以有效地找到描述这些功能的模型的参数和残留物，定义每个片段的邻域的边界的估计。该算法可以接受几乎任何模型的段邻域，并可以应用于广泛的最佳拟合函数，包括最小二乘和最大似然。该算法依次识别序列的最重要的特征。这些方法之一的影响病毒的血红蛋白的蛋白质的应用程序揭示了一个可能的机制，通过发现一个强大的七肽重复结构的断裂构象变化。
Two algorithms for the efficient identification of segment neighborhoods are presented. A segment neighborhood is a set of contiguous residues that share common features. Two procedures are developed to efficiently find estimates for the parameters of the model that describe these features and for the residues that define the boundaries of each segment neighborhood. The algorithms can accept nearly any model of segment neighborhood, and can be applied with a broad class of best fit functions including least squares and maximum likelihood. The algorithms successively identify the most important features of the sequence. The application of one of these methods to the haemogglutinin protein of influence virus reveals a possible mechanism for conformational change through the finding of a break in a strong heptad repeat structure.