Predicting Location and Structure Of beta-Sheet Regions Using Stochastic Tree Grammars

We describe and demonstrate the effectiveness of a method of predicting protein secondary structures, sheet regions in particular, using a class of stochastic tree grammars as representational language for their amino acid sequence patterns. The family of stochastic tree grammars we use, the Stochastic Ranked Node Rewriting Grammars (SRNRG), is one of the rare families of stochastic grammars that are expressive enough to capture the kind of long-distance dependencies exhibited by the sequences of -sheet regions, and at the same time enjoy relatively efficient processing. We applied our method on real data obtained from the HSSP database and the results obtained are encouraging: Using an SRNRG trained by data of a particular protein, our method was actually able to predict the location and structure of -sheet regions in a number of different proteins, whose sequences are less than 25 per cent homologous to the training sequences. The learning algorithm we use is an extension of the `In...

• Type: Conference
• Year: 1994
• Where: ISMB
• Authors: Hiroshi Mamitsuka, Naoki Abe
• Keywords: -sheet Regions, Computational Biology, ISMB 1994, Stochastic Grammars, Stochastic Tree Grammars