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

机译:蛋白质结构预测短子序列的二见角度库

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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。 AB Initio和同源性建模是蛋白质结构预测中使用的两个重要方法。其中,AB Initio方法假设不需要对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,并使用诸如分类,结构,序列和二面角的信息,虽然有许多用于结构和序列信息的数据库,但Dihedral角度的数据库并不多发生的子序列的二相值。现有的局限性类似于不能检索特定子序列的氨基酸的二相值,或者仅基于序列同一性的特定蛋白质(具有<20%序列同一性的蛋白质)设计。因此,当基于短子序列和精确级的蛋白质结构预测中使用时,它们具有缺点。本文介绍了长度为5的短子序列的二见角度库。在这种数据库中,无论序列相似度的百分比如何,从蛋白质数据库(PDB)中提取所有蛋白质的二相角度。本文还展示了使用精确匹配的数据库如何用于蛋白质结构预测。

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