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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Improved multiple sequence alignments using coupled pattern mining.

K S M Tozammel Hossain1, Debprakash Patnaik2, Srivatsan Laxman3

  • 1Virginia Tech, Blacksburg.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|January 4, 2014
PubMed
Summary
This summary is machine-generated.

We introduce ARMiCoRe, a new method for multiple sequence alignment (MSA) that mines coupled residues. This approach improves alignments by identifying correlated mutations, outperforming traditional conservation-based methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding evolutionary relationships and biological functions.
  • Classical MSA algorithms focus on sequence conservation, often overlooking correlated mutations (couplings).
  • Couplings provide additional insights into sequence evolution and are sometimes manually incorporated into alignments.

Purpose of the Study:

  • To develop a novel approach, ARMiCoRe, for refining multiple sequence alignments.
  • To improve the identification and representation of coupled residues within biological sequences.
  • To enhance MSA quality beyond traditional conservation-based metrics.

Main Methods:

  • ARMiCoRe utilizes a pattern mining approach, specifically frequent episode mining.
  • It defines and discovers 'coupled patterns' within sequences.
  • A max-flow algorithm is employed to tile these coupled patterns for alignment refinement.

Main Results:

  • ARMiCoRe generates multiple sequence alignments that better expose sequence couplings.
  • The method demonstrates improved performance compared to standard conservation-based alignment algorithms.
  • Effectiveness was validated across a large dataset of biological sequences.

Conclusions:

  • ARMiCoRe offers a novel, pattern mining-based strategy for enhancing multiple sequence alignments.
  • The approach successfully integrates the analysis of correlated mutations into MSA.
  • ARMiCoRe provides superior alignments for both coupling discovery and traditional assessment metrics.