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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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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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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment.

Erik S Wright1,2

  • 1Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53715, USA. eswright@wisc.edu.

BMC Bioinformatics
|October 8, 2015
PubMed
Summary
This summary is machine-generated.

Local sequence context improves multiple sequence alignment accuracy. Context-aware predictors enhance protein alignments, especially for large, diverse sequence sets, leading to more accurate results with the DECIPHER program.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Multiple sequence alignment (MSA) quality degrades with increasing sequence set size and diversity.
  • Current MSA algorithms often assume independence between sites, neglecting valuable local sequence context.
  • Error accumulation and ambiguity limit the accuracy of traditional high-throughput sequence alignment methods.

Purpose of the Study:

  • To investigate the utility of local sequence context for enhancing the quality of large-scale multiple sequence alignments.
  • To develop and evaluate context-aware predictors for improving alignment accuracy.
  • To assess the scalability and effectiveness of context-based approaches for diverse biological sequence sets.

Main Methods:

  • Development of two context-based predictors: secondary structure prediction and context-dependent gap cost modulation.
  • Assessment of predictor information content and impact on alignment accuracy using empirical benchmarks.
  • Implementation of context-aware strategies into a novel sequence alignment program, DECIPHER.

Main Results:

  • Context-based predictors provide significant information content, leading to more accurate sequence alignments.
  • The informativeness of local context increases with the number of sequences, improving alignments of large datasets.
  • DECIPHER demonstrated superior performance over existing programs on large and diverse sequence sets.

Conclusions:

  • Leveraging local sequence context, particularly secondary structure, effectively breaks the independence assumption in alignment.
  • Secondary structure, being more conserved than primary sequence, aids in aligning distantly related proteins.
  • Context-aware methods, like DECIPHER, enable scalable, high-accuracy alignments for large and diverse biological sequence data.