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Related Concept Videos

Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Bayesian Top-Down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties.

Andrew F Neuwald1, Stephen F Altschul2

  • 1Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.

Plos Computational Biology
|May 19, 2016
PubMed
Summary
This summary is machine-generated.

GISMO, a new Bayesian Markov chain Monte Carlo (MCMC) sampler, offers more accurate protein multiple sequence alignment (MSA) than existing popular tools. It excels at identifying conserved domains within large, diverse protein sequence sets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Accurate protein multiple sequence alignment (MSA) is crucial for understanding protein function and evolution.
  • Existing MSA tools face challenges with large, diverse sequence datasets and conserved domain identification.

Purpose of the Study:

  • To introduce GISMO, a novel Bayesian MCMC sampler for protein MSA.
  • To demonstrate GISMO's superior accuracy and efficiency compared to popular MSA programs.

Main Methods:

  • Development of a Bayesian MCMC sampler (GISMO) utilizing a top-down strategy and position-specific gap penalties.
  • Implementation of a Bayesian statistical measure of alignment quality based on the minimum description length principle.
  • Application to 408 diverse protein sets, benchmarked against manually curated alignments.

Main Results:

  • GISMO demonstrated higher accuracy than MUSCLE, MAFFT, Clustal-Ω, and Kalign on diverse protein sets.
  • The program effectively identifies and aligns conserved domains within large, heterogeneous sequence collections.
  • GISMO's Bayesian approach prevents alignment of statistically unjustified regions, unlike sum-of-pairs methods.

Conclusions:

  • GISMO provides a more accurate and statistically rigorous approach to protein MSA, particularly for conserved domain identification.
  • Its unique features enable better handling of large and diverse sequence datasets.
  • GISMO represents a significant advancement in bioinformatics tools for protein sequence analysis.