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MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information.

Jimin Pei1, Nick V Grishin

  • 1Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA. jpei@chop.swmed.edu

Nucleic Acids Research
|August 29, 2006
PubMed
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We developed MUMMALS, a new program for multiple protein sequence alignment. It uses probabilistic consistency and hidden Markov models (HMMs) to improve alignment accuracy, especially for remote homologs.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Accurate multiple protein sequence alignment is crucial for understanding protein function and evolution.
  • Existing alignment methods face challenges with distant protein homologs.

Purpose of the Study:

  • To develop a novel program, MUMMALS, for enhanced multiple protein sequence alignment.
  • To improve alignment accuracy by incorporating probabilistic consistency and local structural information.

Main Methods:

  • Developed MUMMALS, a program utilizing probabilistic consistency for multiple sequence alignment.
  • Employed pairwise alignment hidden Markov models (HMMs) with multiple match states.
  • Estimated model parameters using a large library of structure-based alignments.

Related Experiment Videos

Main Results:

  • MUMMALS demonstrated statistically superior accuracy compared to leading aligners (ProbCons, MAFFT, MUSCLE) on remote homologs.
  • A large dataset of automatically computed pairwise structure alignments proved more effective for parameter estimation and testing than smaller curated datasets.
  • Reference-independent evaluation methods showed strong correlation with reference-dependent evaluations.

Conclusions:

  • MUMMALS offers improved accuracy for multiple protein sequence alignment, particularly for distantly related proteins.
  • Large-scale, automatically generated datasets are valuable for training and evaluating alignment algorithms.
  • Validated a reliable method for reference-independent assessment of alignment quality.