Beta-sheet prediction using inter-strand residue pairs and refinement with hopfield neural network


Over the last 20 years, many secondary prediction methods have been studied. Almost all methods use sequences with 7 to 21 consecutive residues, and guess the secondary structure of the center residue [Rost 93]. These methods work well for-helices, because one turn of-helix consists of 3.5 residues, thus 7 consecutive residues su ces to guess the secondary structure of the center residue. On the contrary, prediction for-sheets is di cult. In a-sheet, residues which are connected with hydrogen bonds are usually separated by more than 10 residues and the distance between them is not constant. For this reason, predictions based on consecutive residues are not for a-sheet itself, but for a strand, which is a piece of stretched sub-sequence in the-sheet. A-sheet consists of a pair of strands, which are connected by hydrogen bonds. So a strand is a necessary for a-sheet, but is not su cient. In this research I used a protein tertiary structure database (PDB) to gather statistics of pairs of three residue sub-sequences (will be abbreviated as TRS) in-sheets, and calculated the propensities of TRS pairs (will be abbreviated as pTRSP) as described in [Hubbard 94]. 1 These propensities are used to guess whether two sub-sequences of a test sequence compose a TRS pair or not

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