Kiyoshi ASAI (asai@Detl.go.jp)
Institute for New Generation Computer Technology
In this paper, it is shown that we can efficiently use continuous speech recognition techniques for prediction of protein structures. We propose a general framework to treat the local structures and the global structures of protein together by using the continuous speech recognition techniques for protein structure prediction. This framework enables us to express the statistic information from the protein database and biological knowledge by stochastic models and grammar-like rules, and to summarize them by parsing techniques. The objects, the human voice and the protein, are not similar. However, they have similar hierarchies. In the case of speech, they are phonemes, words, phrases, sentences, meanings. In the case of protein, they are primary structures, secondary structures, super-secondary structures, tertiary structures, functions. We introduce a structure prediction system which constructs the structures of protein by considering such hierarchies.