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Efficient similarity search in protein structure databases by k-clique hashing.

机译:通过k-clique哈希在蛋白质结构数据库中进行有效的相似性搜索。

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MOTIVATION: Graph-based clique-detection techniques are widely used for the recognition of common substructures in proteins. They permit the detection of resemblances that are independent of sequence or fold homologies and are also able to handle conformational flexibility. Their high computational complexity is often a limiting factor and prevents a detailed and fine-grained modeling of the protein structure. RESULTS: We present an efficient two-step method that significantly speeds up the detection of common substructures, especially when used to screen larger databases. It combines the advantages from both clique-detection and geometric hashing. The method is applied to an established approach for the comparison of protein binding-pockets, and some empirical results are presented. AVAILABILITY: Upon request from the authors.
机译:动机:基于图的群体检测技术已广泛用于识别蛋白质中常见的亚结构。它们允许检测与序列或折叠同源性无关的相似性,并且还能够处理构象灵活性。它们的高计算复杂度通常是一个限制因素,并阻止了蛋白质结构的详细细粒度建模。结果:我们提出了一种有效的两步法,该方法可大大加快常见子结构的检测速度,尤其是在用于筛选大型数据库时。它结合了集团检测和几何哈希的优点。该方法被应用于比较蛋白质结合口袋的既定方法,并给出了一些实验结果。可用性:应作者要求。

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