Improved method for predicting beta-turn using support vector machine.
BACKGROUND
Numerous methods for predicting beta-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of beta-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance.


RESULTS
The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting beta-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index.
Zhang
Qidong
Q
Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School and Informatics Institute of UMDNJ, 675 Hoes Lane, Piscataway, NJ 08854, USA.
Yoon
Sukjoon
S
Welsh
William J
WJ
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013329:Structure-Activity Relationship
D011506:Proteins