Prediction of complex super-secondary structure βαβ motifs based on combined features

Abstract EN

Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins.

The strand-loop-helix-loop-strand (bab) motif is an important complex super-secondary structure in proteins.

Many functional sites and active sites often occur in polypeptides of bab motifs.

Therefore, the accurate prediction of bab motifs is very important to recognizing protein tertiary structure and the study of protein function.

In this study, the bab motif dataset was first constructed using the DSSP package.

A statistical analysis was then performed on bab motifs and non-bab motifs.

The target motif was selected, and the length of the loop-a-loop varies from 10 to 26 amino acids.

The ideal fixed-length pattern comprised 32 amino acids.

A Support Vector Machine algorithm was developed for predicting bab motifs by using the sequence information, the predicted structure and function information to express the sequence feature.

The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively.

The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively.

Results demonstrate that the proposed method is an effective approach for predicting bab motifs and can be used for structure and function studies of proteins.

2015 The Authors.

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on behalf of King Saud University.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).