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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote

Bin Liu1, Deyuan Zhang, Ruifeng Xu

  • 1School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China, Gordon Life Science Institute, Belmont, MA 02478, USA, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning, China, School of Computer Science, Fudan University, Shanghai 200433, China and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Bioinformatics (Oxford, England)
|December 10, 2013
PubMed
Summary

This study introduces a novel profile-based protein representation method to enhance remote homology detection. The approach improves the performance of existing sequence-based kernels, offering insights into protein family features.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein remote homology detection is crucial for basic research and drug development.
  • Profile-based approaches show promise but require optimal evolutionary information extraction.
  • Improving evolutionary information extraction is key to advancing protein homology detection.

Purpose of the Study:

  • To propose a novel profile-based protein representation for improved remote homology detection.
  • To extract evolutionary information effectively using frequency profiles derived from multiple sequence alignments.
  • To enhance the performance of existing sequence-based kernels in protein homology detection.

Main Methods:

  • Developed a profile-based protein representation using frequency profiles from PSI-BLAST multiple sequence alignments.
  • Integrated the profile-based representation with three established sequence-based kernels (SVM-Ngram, SVM-pairwise, SVM-LA).
  • Evaluated the approach on a SCOP benchmark dataset comprising 54 families and 23 superfamilies.

Main Results:

  • The novel profile-based protein representation significantly improved the performance of the tested sequence-based kernels.
  • The approach demonstrated its potential for enhancing protein remote homology detection.
  • The method provided valuable insights into the characteristics of proteins across different families.

Conclusions:

  • The proposed profile-based protein representation is a promising method for improving remote homology detection.
  • This approach can be readily combined with existing sequence-based methods to boost their efficacy.
  • The developed technique offers a valuable tool for both fundamental research and practical applications in protein science.