Protein secondary structure prediction using two-level case-based reasoning.
We have developed a two-level case-based reasoning architecture for predicting protein secondary structure. The central idea is to break the problem into two levels: (i) reasoning at the object (protein) level and using the global information from this level to focus on a more restricted problem space; (ii) decomposing objects into pieces (segments) and reasoning at the level of internal structures. As a last step to the procedure, inferences from the parts of the internal structure are synthesized into predictions about global structure. The architecture has been developed and tested on a commonly used data set with 69.5% predictive accuracy. It was then tested on a new data set with 68.2% accuracy. With additional tuning, over 70% accuracy was achieved. In addition, a series of experiments were conducted to test various aspects of the method and the results are informative.
Leng
B
B
Department of Computer Science, University of Pittsburgh, PA 15260, USA.
Buchanan
B G
BG
Nicholas
H B
HB
D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D002645:Chickens; D053818:Chymases; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010191:Pancreatic Polypeptide / Q000737:chemistry; D017433:Protein Structure, Secondary; D051381:Rats; D012697:Serine Endopeptidases / Q000737:chemistry; D012984:Software
D010191:Pancreatic Polypeptide; D012697:Serine Endopeptidases; D053818:Chymases