对于非专业的生物信息学家来说,这是一个简单的方法,可以为没有已知同源物的蛋白质建立可实验验证的假设。

Alexander Zawaira, Youtaro Shibayama
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引用次数: 0

摘要

研究独特orf的蛋白质-蛋白质相互作用(PPIs)是一种破译独特orf生物学作用的策略。为统一参考,我们将unique orf定义为使用默认参数进行PDB-BLAST搜索后未找到匹配蛋白的orf。orf的独特性通常排除了在实验设计中直接使用基于结构的方法来探索ppi。许多开源的生物信息学工具,从常用的到相对深奥的,已经建立并验证了对蛋白质进行各种分析和/或预测。如何将这些可用的工具组合成一个协议,帮助非专业的生物信息学家研究人员设计实验,以探索其独特的ORF的ppi ?在这里,我们定义了一个基于软件可访问性的实用协议来实现这一目标,并通过将其应用于结核分枝杆菌的两种蛋白质- ImuB和ImuA'蛋白质使其具体化。该协议是实用的,因为决策主要基于易于使用的免费软件的可用性。我们定义了以下基本和用户友好的软件路径来构建可测试的PPI假设,以查询蛋白质序列:PSI-PRED→MUSTER→metaPPISP→ASAView和ConSurf。在可能的情况下,可以包括其他分析和/或预测工具。我们的方案将软件预测和分析与一般生物信息学原理相结合,以达成共识,优先和可测试的PPI假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simple recipe for the non-expert bioinformaticist for building experimentally-testable hypotheses for proteins with no known homologs.

The study of the protein-protein interactions (PPIs) of unique ORFs is a strategy for deciphering the biological roles of unique ORFs of interest. For uniform reference, we define unique ORFs as those for which no matching protein is found after PDB-BLAST search with default parameters. The uniqueness of the ORFs generally precludes the straightforward use of structure-based approaches in the design of experiments to explore PPIs. Many open-source bioinformatics tools, from the commonly-used to the relatively esoteric, have been built and validated to perform analyses and/or predictions of sorts on proteins. How can these available tools be combined into a protocol that helps the non-expert bioinformaticist researcher to design experiments to explore the PPIs of their unique ORF? Here we define a pragmatic protocol based on accessibility of software to achieve this and we make it concrete by applying it on two proteins-the ImuB and ImuA' proteins from Mycobacterium tuberculosis. The protocol is pragmatic in that decisions are made largely based on the availability of easy-to-use freeware. We define the following basic and user-friendly software pathway to build testable PPI hypotheses for a query protein sequence: PSI-PRED → MUSTER → metaPPISP → ASAView and ConSurf. Where possible, other analytical and/or predictive tools may be included. Our protocol combines the software predictions and analyses with general bioinformatics principles to arrive at consensus, prioritised and testable PPI hypotheses.

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