Recognition of beta-alpha-beta motifs in proteins by using Random Forest algorithm

Author

Lixia Sun ; Xiuzhen Hu

Author_Institution

Coll. of Sci., Inner Mongolia Univ. of Technol., Hohhot, China

fYear

2013

fDate

16-18 Dec. 2013

Firstpage

546

Lastpage

551

Abstract

A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A Random Forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew´s correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.

Keywords

bioinformatics; molecular biophysics; molecular configurations; proteins; statistical analysis; DSSP; Definition of Secondary Structure of Proteins software; Matthew correlation coefficient; PROMOTIF software; Random Forest algorithm; amino acid composition; amino acids; beta-alpha-beta motif recognition; hydropathy component; loop-helix-loop length; nonbeta-alpha-beta motifs; protein coordinate file; proteins; sequence characteristics; statistical analysis; structural motifs; Amino acids; Decision support systems; Prediction algorithms; Proteins; Radio frequency; Tin; Vectors; Complex super-secondary structure; Hydropathy component of position; Random Forest algorithm; Structure prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-2760-9

Type

conf

DOI

10.1109/BMEI.2013.6747001

Filename

6747001