title: Protein secondary structure prediction using dynamic programming.
abstract: In the present paper, we describe how a directed graph was constructed and then searched for the optimum path using a dynamic programming approach, based on the secondary structure propensity of the protein short sequence derived from a training data set. The protein secondary structure was thus predicted in this way. The average three-state accuracy of the algorithm used was 76.70%.
authors.affiliation: School of Life Science and Technology, Shanghai Jiaotong University, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhao
authors.forename: Pei-Ming
authors.initials: PM
authors.lastname: Song
authors.forename: Qing
authors.initials: Q
authors.lastname: Fang
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D011382:Programming, Linear; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins