Genetic Information Processing by Stochastic Model

HMM for Secondary Structure Prediction of Protein

Abstract

It is maintained that active use of a stochastic model is essential for analyzing the amino acid sequence of protein or the base sequence of DNA. Here we shall discuss the amino acid sequence of protein. Using HMM, which is a type of stochastic model, an attempt was made to predict the secondary structure of protein. In this process, secondary structures such as helix, sheet and turn are each learned by descrete HMM, and the output probabilities from the stochastic model is used to determine which part of the sequence (whose structure is unknown) corresponds to which structure. With a model in which one amino acid is taken as the output symbol, favorable results could not be obtained, but when two consecutive amino acids were used as the output symbol, good results could be obtained in estimating helix and sheet.