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Combinatorial model for sequence and spatial motif discovery in short sequence fragments: Examples from 脽-barrel membrane proteins

机译:短序列片段中序列和空间基序发现的组合模型:脽-桶状膜蛋白的实例

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摘要

Motifs are over-represented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein functions. When motifs are located in short sequence fragments, as in transmembrane domains that are only 10-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we develop combinatorial models for assessing statistically significant sequence and spatial patterns. We show our method can uncover previously unknown sequence and spatial motifs in beta-barrel membrane proteins
机译:母题是蛋白质中出现的过度表达的序列或空间模式。它们通常在维持蛋白质稳定性和促进蛋白质功能中起重要作用。当基序位于短序列片段中时,例如在长度仅为10-20个残基的跨膜结构域中,并且当数据非常有限时,很难识别基序。在这项研究中,我们开发了组合模型来评估具有统计学意义的序列和空间模式。我们证明了我们的方法可以揭示β-桶状膜蛋白中先前未知的序列和空间基序

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