Singh M, Berger B, Kim P S, Berger J M, Cochran A G
Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
Proc Natl Acad Sci U S A. 1998 Mar 17;95(6):2738-43. doi: 10.1073/pnas.95.6.2738.
The recent rapid growth of protein sequence databases is outpacing the capacity of researchers to biochemically and structurally characterize new proteins. Accordingly, new methods for recognition of motifs and homologies in protein primary sequences may be useful in determining how these proteins might function. We have applied such a method, an iterative learning algorithm, to analyze possible coiled coil domains in histidine kinase receptors. The potential coiled coils have not yet been structurally characterized in any histidine kinase, and they appear outside previously noted kinase homology regions. The learning algorithm uses a combination of established sequence patterns in known coiled coil proteins and histidine kinase sequence data to learn to recognize efficiently this coiled coil-like motif in the histidine kinases. The common appearance of the structural motif in a functionally important part of the receptors suggests hypotheses for kinase regulation and signal transduction.
近期蛋白质序列数据库的快速增长，使得研究人员在生物化学和结构层面表征新蛋白质的能力相形见绌。因此，识别蛋白质一级序列中基序和同源性的新方法，可能有助于确定这些蛋白质的功能方式。我们应用了一种方法，即迭代学习算法，来分析组氨酸激酶受体中可能存在的卷曲螺旋结构域。在任何组氨酸激酶中，潜在的卷曲螺旋结构域尚未得到结构表征，并且它们出现在先前已知的激酶同源区域之外。该学习算法结合了已知卷曲螺旋蛋白中已确立的序列模式和组氨酸激酶序列数据，以便学会高效识别组氨酸激酶中这种类似卷曲螺旋的基序。这种结构基序在受体功能重要部分的普遍出现，为激酶调节和信号转导提出了假设。
