Proposing a highly accurate protein structural class predictor using segmentation-based features.
Abdollah Dehzangi,Abdollah Dehzangi,Kuldip K. Paliwal,James Lyons,Alok Sharma,Alok Sharma,Abdul Sattar,Abdul Sattar +7 more
TLDR
By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, this study is able to enhance the protein structural class prediction performance significantly.Abstract:
Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks. By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.read more
Citations
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Systems biology study of transcriptional and post-transcriptional co-regulatory network sheds light on key regulators involved in important biological processes in Citrus sinensis
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A Simplified Complex Network-Based Approach to mRNA and ncRNA Transcript Classification
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A Review on Saponin Biosynthesis and its Transcriptomic Resources in Medicinal Plants
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Computational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction
Vineet Singh,Alok Sharma,Abel Avitesh Chandra,Abdollah Dehzangi,Daichi Shigemizu,Tatsuhiko Tsunoda +5 more
TL;DR: A new predictor, PSSM-PUP, is proposed that uses evolutionary information of amino acids to predict pupylated lysine residues and has demonstrated improvement in performance compared to other existing predictors using the benchmark dataset from Pupdb Database.
References
More filters
Journal ArticleDOI
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
Stephen F. Altschul,Thomas L. Madden,Alejandro A. Schäffer,Jinghui Zhang,Zheng Zhang,Webb Miller,David J. Lipman +6 more
TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
SCOP: a structural classification of proteins database for the investigation of sequences and structures.
TL;DR: This database provides a detailed and comprehensive description of the structural and evolutionary relationships of the proteins of known structure and provides for each entry links to co-ordinates, images of the structure, interactive viewers, sequence data and literature references.