Abstract:
: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 (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-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 βαβ 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 βαβ motifs and can be used for structure and function studies of proteins.
journal_name
Saudi J Biol Scijournal_title
Saudi journal of biological sciencesauthors
Sun L,Hu X,Li S,Jiang Z,Li Kdoi
10.1016/j.sjbs.2015.10.005subject
Has Abstractpub_date
2016-01-01 00:00:00pages
66-71issue
1eissn
1319-562Xissn
2213-7106pii
S1319-562X(15)00246-6journal_volume
23pub_type
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journal_title:Saudi journal of biological sciences
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journal_title:Saudi journal of biological sciences
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pub_type: 杂志文章,评审
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journal_title:Saudi journal of biological sciences
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pub_type: 杂志文章
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journal_title:Saudi journal of biological sciences
pub_type: 杂志文章
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journal_title:Saudi journal of biological sciences
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journal_title:Saudi journal of biological sciences
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journal_title:Saudi journal of biological sciences
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