Algorithms for protein structural motif recognition.
Algorithms for protein structural motif recognition.
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DOI：
10.1089/cmb.1995.2.125
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发表时间：
1995-01-01
期刊：
影响因子：
--
通讯作者：
Berger, B
中科院分区：
文献类型：
--
作者：
Berger, B
折叠成某些已知的三维（3D）结构或基序的蛋白质序列的鉴定通过其一维（1D）序列的概率分析来评估。我们提出了一种相关性的方法，在线性时间内运行，并结合在多个距离的氨基酸残基之间的成对依赖关系，以评估一个给定的残基是一个给定的三维结构的一部分的条件概率。这种方法被推广到多个图案，其中的动态规划方法导致一个有效的算法，在线性时间运行的实际问题。通过这种方法，我们能够区分（2-链）卷曲螺旋结构域和非卷曲螺旋结构域以及球蛋白和非球蛋白。当在Brookhaven X射线晶体结构数据库上进行测试时，该方法不会产生卷曲螺旋的任何假阳性或假阴性预测。
The identification of protein sequences that fold into certain known three-dimensional (3D) structures, or motifs, is evaluated through a probabilistic analysis of their one-dimensional (1D) sequences. We present a correlation method that runs in linear time and incorporates pairwise dependencies between amino acid residues at multiple distances to assess the conditional probability that a given residue is part of a given 3D structure. This method is generalized to multiple motifs, where a dynamic programming approach leads to an efficient algorithm that runs in linear time for practical problems. By this approach, we were able to distinguish (2-stranded) coiled-coil from non-coiled-coil domains and globins from nonglobins. When tested on the Brookhaven X-ray crystal structure database, the method does not produce any false-positive or false-negative predictions of coiled coils.