在蛋白质中紧凑结构基序的完整枚举

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722047
Bhadrachalam Chitturi, D. Bein, N. Grishin
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引用次数: 10

摘要

在比较蛋白质结构时,寻找指定多肽片段空间排列的结构基序优于其他方法,如共同亚结构发现和结构叠加。三维蛋白质结构可以建模为图形,其最大程度由常数限定。结构图案也可以建模为图形,其中很大一部分是树。因此,蛋白质中的基序搜索可以建模为同构子图的枚举,其中具有m个节点的查询树Q在具有n个节点的稀疏图G中搜索,并且G中任何节点的最大度以常数ε为界。我们设计了一个有效的分治算法,该算法通过使用最小支配集划分Q来找到G中Q的所有副本。该策略可以扩展到稀疏查询图,通过删除少量边可以将其简化为树。
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Complete enumeration of compact structural motifs in proteins
The search of structural motifs that specify the spatial arrangement of polypeptide segments is preferred over other methods such as common substructure discovery and structural superposition in comparing protein structures. 3D protein structures can be modeled as graphs whose maximum degree is bounded by a constant. Structural motifs can also be modeled as graphs and a significant percentage of them are trees. Thus, motif search in proteins can be modeled as an enumeration of isomorphic subgraphs where a query tree Q with m nodes is searched in a sparse graph G with n nodes and the maximum degree of any node in G is bounded by a constant ε. We design an efficient divide-and-conquer algorithm that finds all copies of Q in G by partitioning Q using a minimum dominating set. This strategy can be extended to sparse query graphs that can be reduced to trees by deleting a small number of edges.
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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