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A Protocol for Computer-Based Protein Structure and Function Prediction
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Protein multiple alignments: sequence-based versus structure-based programs.

Mathilde Carpentier1, Jacques Chomilier2

  • 1Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, Paris, France.

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Summary
This summary is machine-generated.

Structure-based multiple sequence alignment methods offer superior reliability compared to sequence-based approaches. This evaluation highlights structure-aware programs as key for accurate protein alignment, especially with low sequence identity.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding protein evolution and function.
  • Existing evaluations primarily focus on sequence-based alignment methods.
  • The added value of incorporating structural information into MSA remains underexplored.

Purpose of the Study:

  • To evaluate the performance of structure-based and sequence-structure based multiple sequence alignment programs.
  • To compare these methods against established sequence-based programs using reference alignments.
  • To identify the benefits of considering protein structure in multiple sequence alignment.

Main Methods:

  • Compared alignments from 25 programs (sequence-based, structure-based, and hybrid) against five benchmark databases (BALIBASE 2 & 3, HOMSTRAD, OXBENCH, SISYPHUS).
  • Assessed alignment reliability and performance across varying sequence identities and residue types (secondary structures, buried residues).
  • Analyzed gap management differences between sequence and structure-based methods.

Main Results:

  • Structure-based methods consistently produced more reliable alignments than sequence-based methods.
  • Hybrid sequence-structure methods also outperformed purely sequence-based approaches.
  • MAMMOTH and MATRAS showed leading performance, with other programs like MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN, and TCOFFEE+SAP demonstrating competitive results.
  • The advantage of structure-based methods was more pronounced at lower sequence identities and for residues in regular secondary structures or buried regions.
  • Sequence-based programs introduced fewer gaps than structure-based programs.
  • Manually curated databases presented greater challenges for all tested programs.

Conclusions:

  • Incorporating structural information significantly enhances the reliability of multiple sequence alignments.
  • Structure-based alignment tools are recommended, particularly for datasets with low sequence identity.
  • Further research into optimizing hybrid approaches and understanding database-specific performance is warranted.