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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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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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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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Updated: Nov 20, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Aligning biological sequences by exploiting residue conservation and coevolution.

Anna Paola Muntoni1,2,3, Andrea Pagnani1,4,5, Martin Weigt3

  • 1Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy.

Physical Review. E
|January 20, 2021
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Summary
This summary is machine-generated.

DCAlign is a new algorithm for biological sequence alignment that accounts for coevolution. This method improves upon traditional profile models for DNA, RNA, and protein sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Biophysics

Background:

  • Biological sequence alignment is crucial for understanding evolutionary relationships and predicting biomolecular function.
  • Traditional profile models capture sequence conservation but overlook the coevolution of amino-acid positions, which is vital for protein structure and function.
  • Coevolutionary signals are essential for predicting protein structure, interactions, and mutational landscapes.

Purpose of the Study:

  • To introduce DCAlign, an efficient sequence alignment algorithm that incorporates coevolutionary information.
  • To overcome the limitations of profile models by including coevolution among positions in a general manner.
  • To develop a universally applicable alignment tool for both protein and RNA sequences without requiring structural data.

Main Methods:

  • Developed an efficient alignment algorithm, DCAlign, utilizing an approximate message-passing strategy.
  • Incorporated coevolutionary signals into the alignment process.
  • Validated the algorithm using simulated data and real protein and RNA sequences.

Main Results:

  • DCAlign overcomes limitations of profile models by effectively integrating coevolutionary information.
  • The algorithm demonstrates broad applicability to both protein and RNA sequence alignment.
  • Performance was validated on diverse datasets, showcasing its potential.

Conclusions:

  • DCAlign offers a significant advancement in sequence alignment by incorporating coevolution.
  • The algorithm provides a powerful, structure-free tool for analyzing protein and RNA sequences.
  • DCAlign has broad potential applications in computational biology and bioinformatics.