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Better prediction of the location of α‐turns in proteins with support vector machine
Author(s) -
Wang Yan,
Xue Zhidong,
Xu Jin
Publication year - 2006
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21062
Subject(s) - support vector machine , computer science , sequence (biology) , matthews correlation coefficient , set (abstract data type) , data mining , artificial intelligence , data set , correlation coefficient , alpha (finance) , pattern recognition (psychology) , machine learning , algorithm , mathematics , biology , statistics , genetics , programming language , construct validity , psychometrics
We have developed a novel method named AlphaTurn to predict alpha-turns in proteins based on the support vector machine (SVM). The prediction was done on a data set of 469 nonhomologous proteins containing 967 alpha-turns. A great improvement in prediction performance was achieved by using multiple sequence alignment generated by PSI-BLAST as input instead of the single amino acid sequence. The introduction of secondary structure information predicted by PSIPRED also improved the prediction performance. Moreover, we handled the very uneven data set by combining the cost factor j with the "state-shifting" rule. This further promoted the prediction quality of our method. The final SVM model yielded a Matthews correlation coefficient (MCC) of 0.25 by a 10-fold cross-validation. To our knowledge, this MCC value is the highest obtained so far for predicting alpha-turns. An online Web server based on this method has been developed and can be freely accessed at http://bmc.hust.edu.cn/bioinformatics/ or http://210.42.106.80/.