BACKGROUND Over the last two decades, many approaches have been developed in bioinformatics that aim at one of the most promising, yet unsolved problems in modern life sciences--prediction of structural features of a protein. Such tasks addressed to transmembrane protein structures provide valuable knowledge about their three-dimensional structure. For this reason, the analysis of membrane proteins is essential in genomic and proteomic-wide investigations. Thus, many in-silico approaches have been utilized extensively to gain crucial advances in understanding membrane protein structures and functions. RESULTS It turned out that amino acid covariation within interacting sequence parts, extracted from a evolutionary sequence record of α-helical membrane proteins, can be used for structure prediction. In a recent study we discussed the significance of short membrane sequence motifs widely present in nature that act as stabilizing 'building blocks' during protein folding and in retaining the three-dimensional fold. In this work, we used motif data to define evolutionary interaction pattern pairs. These were obtained from different pattern alignments and were used to evaluate which coupling mechanisms the evolution provides. It can be shown that short interaction patterns of homologous sequence records are membrane protein family-specific signatures. These signatures can provide valuable information for structure prediction and protein classification. The results indicate a good agreement with recent studies. CONCLUSIONS Generally, it can be shown how the evolution contributes to realize covariation within discriminative interaction patterns to maintain structure and function. This points to their general importance for α-helical membrane protein structure formation and interaction mediation. In the process, no fundamentally energetic approaches of previous published works are considered. The low-cost rapid computational methods postulated in this work provides valuable information to classify unknown α-helical transmembrane proteins and to determine their structural similarity.

中文翻译：

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膜蛋白中进化相互作用模式对的观察。

背景技术在过去的二十年中，生物信息学中已经开发出许多方法，这些方法的目标是现代生命科学中最有希望但尚未解决的问题之一-蛋白质结构特征的预测。针对跨膜蛋白结构的此类任务提供了有关其三维结构的宝贵知识。因此，在基因组和蛋白质组学研究中，膜蛋白的分析是必不可少的。因此，许多计算机内方法已被广泛使用，以在理解膜蛋白结构和功能方面获得重要进展。结果发现，从α-螺旋膜蛋白的进化序列记录中提取的相互作用序列部分中的氨基酸共变可用于结构预测。在最近的研究中，我们讨论了自然界中广泛存在的短膜序列基序的重要性，这些基序在蛋白质折叠过程中起到稳定“构件”的作用，并保持三维折叠。在这项工作中，我们使用主题数据来定义进化相互作用模式对。这些是从不同的模式比对中获得的，并用于评估进化提供的耦合机制。可以证明，同源序列记录的短相互作用模式是膜蛋白家族特有的特征。这些签名可以为结构预测和蛋白质分类提供有价值的信息。结果表明与最近的研究有很好的一致性。结论一般来说，可以证明进化是如何在区分性交互模式内实现协变以维持结构和功能的。这表明了它们对于α-螺旋膜蛋白结构形成和相互作用介导的普遍重要性。在此过程中，没有考虑先前已发表作品的从根本上充满活力的方法。这项工作中假定的低成本快速计算方法提供了有价值的信息，可以对未知的α-螺旋跨膜蛋白进行分类并确定其结构相似性。