TITLE:
Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins
AUTHORS:
Xiaobo Shi, Xiuzhen Hu
KEYWORDS:
Support Vector Machine Algorithm; Increment of Diversity Value; Position Conservation Scoring Function Value; Secondary Structure Information
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
December
17,
2013
ABSTRACT: The structure and function of proteins are closely related,
and protein structure decides its function, therefore protein structure prediction
is quite important.β-turns are important
components of protein secondary structure. So development of an accurate prediction
method ofβ-turn types is very necessary.
In this paper, we used the composite vector with position conservation scoring function,
increment of diversity and predictive secondary structure information as the input
parameter of support vector machine algorithm for predicting theβ-turn types in the database of 426 protein
chains, obtained the overall prediction accuracy of 95.6%, 97.8%, 97.0%, 98.9%,
99.2%, 91.8%, 99.4% and 83.9% with the Matthews Correlation Coefficient values of
0.74, 0.68, 0.20, 0.49, 0.23, 0.47, 0.49 and 0.53 for types I, II, VIII, I’, II’, IV, VI
and nonturn respectively, which is better than other prediction.