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RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Improved sequence-based prediction of strand residues.

Kanaka Durga Kedarisetti1, Marcin J Mizianty, Scott Dick

  • 1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. kanaka@ece.ualberta.ca

Journal of Bioinformatics and Computational Biology
|February 18, 2011
PubMed
Summary

BETArPRED enhances protein secondary structure prediction by accurately identifying strand residues and beta-strand segments. This novel sequence-based method improves upon existing predictors, discovering missed beta-strands.

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Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
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Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Accurate identification of protein strand residues is crucial for understanding protein structure and function.
  • Existing secondary structure prediction methods have limitations in precisely identifying beta-strand segments.

Purpose of the Study:

  • To develop and evaluate BETArPRED, a novel sequence-based predictor for improved identification of strand residues and beta-strand segments.
  • To enhance the accuracy of protein secondary structure prediction, specifically for beta-strands.

Main Methods:

  • BETArPRED integrates strand residue predictions from SSpro with a logistic regression classifier.
  • It utilizes nine custom features derived from primary sequence, predicted secondary structure (SSpro, PSIPRED, SPINE), and residue depth (RDpred).
  • Features incorporate local patterns in predicted SS and combine SS with residue depth information.

Main Results:

  • BETArPRED demonstrated statistically significant improvements over seven modern secondary structure predictors.
  • The predictor accurately identifies strand residues and beta-strand segments, finding previously missed beta-strands.
  • Compared to the ZHANG-server, BETArPRED improved strand segment prediction and identified more actual strand residues.

Conclusions:

  • BETArPRED offers a significant advancement in predicting strand residues and beta-strand segments.
  • The method effectively complements existing secondary structure prediction tools.
  • BETArPRED enhances the analysis of protein structural and functional aspects through improved strand prediction.