聚类破碎蛋白键角分析

Justin S. Diamond
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引用次数: 0

摘要

对精确蛋白质预测算法的渴望一直是计算生物学成就的一个标志。尽管如此,更好的算法和方法可以在多种生物和医学领域取得更大的成功,例如蛋白质功能推断。准确的预测方法在很大程度上依赖于序列相似性,然而结构更具有进化保守性,即结构是蛋白质之间祖先关系的替代特征。本工作的前提是相似的结构特征将聚集在一起,这可能会显示出独特的氨基酸和二级结构(SS)分布,可以将其纳入hmm中用于SS预测和蛋白质功能推断算法。考虑到结构-进化关系,我提出了一种基于“结构”的SS预测方法,使用HMM和k-mean和模糊k -means碎片化蛋白质簇。当片段分布被纳入hmm时,平均准确度提高了1%,但对特定序列的准确度提高了13%。HMM的结果并不是那么有希望,但是通过c - α键角对蛋白质结构片段的聚类显示出一种有用的与长度无关的度量来推断蛋白质之间的功能关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Clustering Fragmented Protein Bond Angles
The desire for accurate protein prediction algorithms has been a hallmark of computational biology achievements. Still, better algorithms and methodologies can achieve even greater success with implication across a diverse range of biological and medicinal fields such as protein function inference. Accurate prediction methods rely heavily on sequence similarity, however structure is more evolutionary conserved, i.e. structure is an alternate characteristic for ancestral relationships between proteins. The premise of this work is that similar structural features will be clustered together, which may show a unique amino acid and secondary structure (SS) distribution, which can be, incorporated into HMMs for SS prediction and protein function inference algorithms. With structural-evolutionary relationship in mind, I propose a methodology for ‘structure’ based SS prediction methods using HMM and k-mean and fuzzy k -means fragmented protein clusters. When fragment distributions were incorporated into HMMs, the average accuracy increased by 1 percent but showed an increase in accuracy of up to 13 percent for particular sequences. The HMM results were not so promising, however the clustering of protein structure fragments by C-alphas bond angles shows to be a useful length-independent metric for inferring functional relationships between proteins.
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