在基于结构的蛋白质功能预测工具 ProMOL 中自动生成蛋白质主题。

Mikhail Osipovitch, Mitchell Lambrecht, Cameron Baker, Shariq Madha, Jeffrey L Mills, Paul A Craig, Herbert J Bernstein
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引用次数: 0

摘要

ProMOL 是 PyMOL 分子图形系统的一个插件,是一种基于结构的蛋白质功能预测工具。ProMOL 包括一套用于构建主题模板的例程,这些模板用于筛选酶活性位点的查询结构。以前,每个主题模板都是手动生成的,并且在优化灵敏度和选择性参数时需要监督。我们开发了一种算法和工作流程,用于在 ProMOL 中自动完成主题构建和测试程序。该算法使用一套根据经验得出的参数进行优化,几乎不需要用户干预。自动图案生成算法首先与一组基于相同 112 个 PDB 条目的相同活性位点手工生成的图案进行了性能比较测试。在识别同源排列和拒绝不相关结构的排列方面,这两组图案的效果相当。第二组 296 个活性位点图案是根据催化位点图谱条目和文献引文自动生成的,作为现有人工生成图案模板库的扩展。新的主题模板在与原生结构、具有相同 EC 和 Pfam 名称的同源物、随机选择的具有不同 EC 第一位数字的非相关结构的比对中的命中率,以及从主题和查询结构的局部结构比对中获得的 RMSD 值方面,表现出与现有模板相当的性能。这项研究得到了美国国立卫生研究院 GM078077 基金的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated protein motif generation in the structure-based protein function prediction tool ProMOL.

ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077.

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