A Dihedral Angle Database of Short Sub-sequences for Protein Structure Prediction

Dayalan, S., Bevinakoppa, S. and Schroder, H.

    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 exact matches. 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.
Cite as: Dayalan, S., Bevinakoppa, S. and Schroder, H. (2004). A Dihedral Angle Database of Short Sub-sequences for Protein Structure Prediction. In Proc. Second Asia-Pacific Bioinformatics Conference (APBC2004), Dunedin, New Zealand. CRPIT, 29. Chen, Y.-P. P., Ed. ACS. 131-137.
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