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A dihedral angle database of short sub-sequences for protein structure prediction

机译:短子序列的二面角数据库,用于蛋白质结构预测

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Protein structure prediction is considered to be the holy grail of bioinformatics. Ab initio and homology modelling are two important groups of methods used in protein structure prediction. Amongst these, ab initio methods assume that no previous knowledge about protein structures is required. On the other hand homology modelling is based on sequence similarity and uses information such as classification, structure, sequence and dihedral angles for prediction.Even though there are many databases for structural and sequence information, there are not many databases for dihedral angles that store all occurring dihedral values of sub-sequences. The existing ones have limitations like not being able to retrieve dihedral values for amino acids of a specific sub-sequence or being designed only for a specific set of proteins based on sequence identity (proteins with 20% sequence identity). They hence have disadvantages when used in protein structure prediction based on short sub-sequences and exactmatches. This paper presents a dihedral angle database for short sub-sequences up to length five. In this database dihedral angles of all proteins were extracted from the Protein Data Bank (PDB) regardless of the percent of sequence similarity. This paper also shows how the database can be used for protein structure prediction using exact matches.
机译:蛋白质结构预测被认为是生物信息学的圣杯。从头算和同源性建模是蛋白质结构预测中使用的两种重要方法。在这些方法中,从头算方法假设不需要有关蛋白质结构的先前知识。另一方面,同源性建模基于序列相似性,并使用分类,结构,序列和二面角等信息进行预测,尽管结构和序列信息的数据库很多,但存储所有元素的二面角数据库却不多子序列的二面角值。现有的蛋白质具有局限性,例如无法检索特定子序列氨基酸的二面值,或仅基于序列同一性(<20%序列同一性的蛋白质)仅针对特定的一组蛋白质进行设计。因此,当将它们用于基于短子序列和精确匹配的蛋白质结构预测时,它们具有缺点。本文提出了一个二面角数据库,用于短子序列,长度不超过5。在此数据库中,无论序列相似性百分比如何,都从蛋白质数据库(PDB)中提取所有蛋白质的二面角。本文还展示了如何使用精确匹配将数据库用于蛋白质结构预测。

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