Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM
Joint Authors
Liang, Yunyun
Liu, Sanyang
Zhang, Shengli
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins.
It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM).
Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy.
In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM.
Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper.
Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA).
To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets.
Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance.
This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.
American Psychological Association (APA)
Liang, Yunyun& Liu, Sanyang& Zhang, Shengli. 2015. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057880
Modern Language Association (MLA)
Liang, Yunyun…[et al.]. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1057880
American Medical Association (AMA)
Liang, Yunyun& Liu, Sanyang& Zhang, Shengli. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057880
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057880