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3DCoffee: combining protein sequences and structures within multiple sequence alignments.

Orla O'Sullivan1, Karsten Suhre, Chantal Abergel

  • 1Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.

Journal of Molecular Biology
|June 18, 2004
PubMed
Summary
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This study introduces 3DCoffee, a new method for creating high-quality multiple sequence alignments by integrating protein sequences and structures. Incorporating structural data significantly enhances alignment accuracy, especially for distantly related sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Multiple sequence alignments (MSAs) are crucial for bioinformatics analyses.
  • Previous studies suggested structural information could improve MSA quality, but this remained unevaluated.

Purpose of the Study:

  • To develop and evaluate 3DCoffee, a novel method for generating high-quality MSAs by combining protein sequence and structure data.
  • To systematically assess the impact of structural information on MSA accuracy.

Main Methods:

  • 3DCoffee, based on TCoffee v2.00, integrates pairwise sequence alignments with pairwise structure comparisons.
  • Benchmarking was performed on a HOMSTRAD dataset subset, including HOM39 for distantly related sequences.
  • The method utilized the Fugue threading program.

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Main Results:

  • 3DCoffee improved HOMSTRAD dataset accuracy by 4% with one structure and 10% with two structures.
  • A linear correlation was observed between MSA accuracy and the ratio of structures to sequences for distantly related proteins.
  • Accuracy gains were more pronounced with increased structural information.

Conclusions:

  • Combining sequence and structural data with 3DCoffee significantly enhances MSA quality.
  • For distantly related protein sequences, multiple structures are beneficial for accurate MSA generation.
  • 3DCoffee offers a valuable tool for improving bioinformatics analyses reliant on MSAs.