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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Discovery of Spatially Cohesive Itemsets in Three-Dimensional Protein Structures
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Discovery of Spatially Cohesive Itemsets in Three-Dimensional Protein Structures

机译：三维蛋白质结构中空间内聚项目集的发现

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摘要

In this paper we present a cohesive structural itemset miner aiming to discover interesting patterns in a set of data objects within a multidimensional spatial structure by combining the cohesion and the support of the pattern. We propose two ways to build the itemset miner, VertexOne and VertexAll, in an attempt to find a balance between accuracy and run-times. The experiments show that VertexOne performs better, and finds almost the same itemsets as VertexAll in a much shorter time. The usefulness of the method is demonstrated by applying it to find interesting patterns of amino acids in spatial proximity within a set of proteins based on their atomic coordinates in the protein molecular structure. Several patterns found by the cohesive structural itemset miner contain amino acids that frequently co-occur in the spatial structure, even if they are distant in the primary protein sequence and only brought together by protein folding. Further various indications were found that some of the discovered patterns seem to represent common underlying support structures within the proteins.
机译：在本文中，我们提出了一种具有凝聚力的结构项挖掘器，旨在通过结合模式的凝聚力和支持力，在多维空间结构内的一组数据对象中发现有趣的模式。为了找到准确性和运行时间之间的平衡，我们提出了两种构建项目集挖掘器的方法VertexOne和VertexAll。实验表明，VertexOne的性能更好，并且可以在更短的时间内找到与VertexAll几乎相同的项目集。该方法的实用性通过将其应用于在一组蛋白质中基于蛋白质分子结构中原子的原子坐标在空间上邻近区域找到有趣的氨基酸模式而得到证明。内聚性结构元素挖掘者发现的几种模式包含经常在空间结构中同时出现的氨基酸，即使它们在一级蛋白质序列中相距甚远并且仅通过蛋白质折叠而聚集在一起。进一步的各种迹象表明，一些发现的模式似乎代表了蛋白质中常见的潜在支持结构。

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