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首页> 外文期刊>Journal of Molecular Biology >PREDICTION OF PROTEIN THREE-DIMENSIONAL STRUCTURES IN INSERTION AND DELETION REGIONS - A PROCEDURE FOR SEARCHING DATA BASES OF REPRESENTATIVE PROTEIN FRAGMENTS USING GEOMETRIC SCORING CRITERIA
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PREDICTION OF PROTEIN THREE-DIMENSIONAL STRUCTURES IN INSERTION AND DELETION REGIONS - A PROCEDURE FOR SEARCHING DATA BASES OF REPRESENTATIVE PROTEIN FRAGMENTS USING GEOMETRIC SCORING CRITERIA

机译：插入和缺失区域中蛋白质三维结构的预测-使用几何评分标准搜索代表性蛋白质片段数据库的程序

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The prediction of protein structure in insertion/deletion regions (referred to as indels) is an important part of protein model building by homology. Here we combine cluster analysis with data base search procedures. initially, data bases of representative protein fragments are constructed using two different clustering algorithms. In the HCAPD (hierarchical clustering after preliminary division) approach, all protein fragments are divided into classes with similar anchor region structures (a protein fragment consists of two anchoring regions and a central region). Within these classes the fragments are further clustered using a hierarchical cluster algorithm. The DCANN (deterministic clustering by assignment of all nearest neighbours) approach is a variant of the k-nearest neighbours cluster algorithm. Only geometric scoring criteria are used for data base searching. The main advantage of a non-redundant data base is the ability to provide structurally different fragments during the search process, which leads to an improvement in structure prediction. Both methods have been tested on 71 insertions and 74 deletions with lengths between one and eight residues. (C) 1995 Academic Press Limited [References: 44]
机译：插入/缺失区中蛋白质结构的预测（称为插入/缺失）是通过同源性建立蛋白质模型的重要部分。在这里，我们将聚类分析与数据库搜索过程结合在一起。首先，使用两种不同的聚类算法构建代表性蛋白质片段的数据库。在HCAPD（初步划分后的层次聚类）方法中，所有蛋白质片段都分为具有相似锚定区域结构的类别（一个蛋白质片段由两个锚定区域和一个中央区域组成）。在这些类别中，使用层次聚类算法将片段进一步聚类。 DCANN（通过分配所有最近邻居确定性聚类）方法是k最近邻居聚类算法的一种变体。仅几何评分标准用于数据库搜索。非冗余数据库的主要优点是能够在搜索过程中提供结构上不同的片段，从而改善了结构预测。两种方法都经过了71个插入和74个缺失的测试，长度在1至8个残基之间。 （C）1995 Academic Press Limited [参考号：44]

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