  The GOR program for predicting protein secondary structure is extended to
include triple correlation. A score system for a residue pair to be at certain
conformation state is derived from the conditional weight matrix describing
amino acid frequencies at each position of a window flanking the pair under the
condition for the pair to be at the fixed state. A program using this score
system to predict protein secondary structure is established. After training
the model with a learning set created from PDB\_SELECT, the program is tested
with two test sets. As a method using single sequence for predicting secondary
structures, the approach achieves a high accuracy near 70%.
