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基于二面角的生物聚合物分段识别与分类 II:多核苷酸。

Dihedral-based segment identification and classification of biopolymers II: polynucleotides.

机构信息

University of Natural Resources and Life Sciences , Institute for Molecular Modeling and Simulation , Muthgasse 18, 1190 Vienna, Austria.

出版信息

J Chem Inf Model. 2014 Jan 27;54(1):278-88. doi: 10.1021/ci400542n. Epub 2014 Jan 10.

Abstract

In an accompanying paper (Nagy, G.; Oostenbrink, C. Dihedral-based segment identification and classification of biopolymers I: Proteins. J. Chem. Inf. Model. 2013, DOI: 10.1021/ci400541d), we introduce a new algorithm for structure classification of biopolymeric structures based on main-chain dihedral angles. The DISICL algorithm (short for DIhedral-based Segment Identification and CLassification) classifies segments of structures containing two central residues. Here, we introduce the DISICL library for polynucleotides, which is based on the dihedral angles ε, ζ, and χ for the two central residues of a three-nucleotide segment of a single strand. Seventeen distinct structural classes are defined for nucleotide structures, some of which--to our knowledge--were not described previously in other structure classification algorithms. In particular, DISICL also classifies noncanonical single-stranded structural elements. DISICL is applied to databases of DNA and RNA structures containing 80,000 and 180,000 segments, respectively. The classifications according to DISICL are compared to those of another popular classification scheme in terms of the amount of classified nucleotides, average occurrence and length of structural elements, and pairwise matches of the classifications. While the detailed classification of DISICL adds sensitivity to a structure analysis, it can be readily reduced to eight simplified classes providing a more general overview of the secondary structure in polynucleotides.

摘要

在一篇相关论文中(Nagy,G.;Oostenbrink,C. 基于二面角的生物聚合物片段识别和分类 I:蛋白质。J. Chem. Inf. Model. 2013,DOI:10.1021/ci400541d),我们引入了一种新的算法,用于基于主链二面角对生物聚合物结构进行分类。DISICL 算法(全称 Dihedral-based Segment Identification and CLassification)对包含两个中心残基的结构片段进行分类。在这里,我们引入了基于二面角 ε、ζ 和 χ 的 DISICL 多核苷酸库,用于单链的三个核苷酸片段的两个中心残基。我们定义了 17 种独特的核苷酸结构类,其中一些——据我们所知——在其他结构分类算法中没有被描述过。特别是,DISICL 还可以对非规范的单链结构元素进行分类。我们将 DISICL 应用于包含 80000 个和 180000 个片段的 DNA 和 RNA 结构数据库。根据 DISICL 的分类与另一种流行的分类方案的分类进行了比较,比较的方面包括分类的核苷酸数量、结构元素的平均出现和长度,以及分类的两两匹配。虽然 DISICL 的详细分类增加了对结构分析的灵敏度,但它可以很容易地简化为 8 个简化的类,为多核苷酸中的二级结构提供更全面的概述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2abf/3904765/1521bb67659c/ci-2013-00542n_0002.jpg

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