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Subdivision of the MDR superfamily of medium-chain dehydrogenases/reductases through iterative hidden Markov model refinement

机译:通过迭代隐马尔可夫模型细化对中链脱氢酶/还原酶的MDR超家族进行细分

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摘要

BackgroundThe Medium-chain Dehydrogenases/Reductases (MDR) form a protein superfamily whose size and complexity defeats traditional means of subclassification; it currently has over 15000 members in the databases, the pairwise sequence identity is typically around 25%, there are members from all kingdoms of life, the chain-lengths vary as does the oligomericity, and the members are partaking in a multitude of biological processes. There are profile hidden Markov models (HMMs) available for detecting MDR superfamily members, but none for determining which MDR family each protein belongs to. The current torrential influx of new sequence data enables elucidation of more and more protein families, and at an increasingly fine granularity. However, gathering good quality training data usually requires manual attention by experts and has therefore been the rate limiting step for expanding the number of available models.
机译:背景中链脱氢酶/还原酶(MDR)形成了一个蛋白质超家族,其大小和复杂性击败了传统的亚分类方法;它目前在数据库中拥有超过15000个成员,成对序列同一性通常为25%左右,有来自所有生命王国的成员,链长随寡聚度而异,并且这些成员参与了许多生物学过程。有可用于检测MDR超家族成员的配置文件隐式马尔可夫模型(HMM),但没有一种可用于确定每种蛋白质所属的MDR家族。当前大量涌入的新序列数据可以阐明越来越多的蛋白质家族,并且粒度越来越细。但是,收集高质量的训练数据通常需要专家的手动注意,因此已成为限制可用模型数量的速率限制步骤。

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