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A Practical Guide to Phylogenetics for Nonexperts
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MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random

Xuefeng Xia1, Song Zhang, Yu Su

  • 1Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.

Bioinformatics (Oxford, England)
|April 11, 2009
PubMed
Summary

MICAlign improves sequence-to-structure alignment for remote protein homologs by integrating diverse information into a conditional random field model. This novel tool demonstrates enhanced accuracy compared to existing methods in template-based protein structure modeling.

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Accurate sequence-to-structure alignment is crucial for template-based protein structure modeling, especially for remote homologs.
  • Identifying correct protein folds does not guarantee accurate sequence-structure alignments.

Purpose of the Study:

  • To develop and evaluate MICAlign, a novel tool for sequence-to-structure alignment.
  • To improve the accuracy of protein structure modeling for remote homologous proteins.

Main Methods:

  • MICAlign integrates multiple data sources: local structural contexts, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (residue-residue contacts, solvent exposures), and pre-aligned sequences/structures.
  • A position-specific gap scheme and conditional random fields (CRFs) are employed for optimal parameter learning.
  • Performance is evaluated using independent datasets and compared against state-of-the-art alignment tools.

Main Results:

  • MICAlign demonstrates superior alignment accuracy compared to several existing state-of-the-art alignment tools.
  • The integration of diverse information sources contributes to improved performance in sequence-to-structure alignment.
  • The tool effectively addresses the challenge of aligning remote homologs in protein structure modeling.

Conclusions:

  • MICAlign offers a significant advancement in sequence-to-structure alignment for template-based protein modeling.
  • The method's ability to incorporate multiple biological and structural features enhances alignment precision.
  • MICAlign provides a valuable resource for the structural biology and bioinformatics communities.