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Related Concept Videos

From DNA to Protein03:06

From DNA to Protein

The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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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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Updated: Jun 9, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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CodonTest: modeling amino acid substitution preferences in coding sequences.

Wayne Delport1, Konrad Scheffler, Gordon Botha

  • 1Department of Pathology, University of California, San Diego, La Jolla, California, United States of America.

Plos Computational Biology
|September 3, 2010
PubMed
Summary
This summary is machine-generated.

Genetic algorithms improve codon models of evolution by classifying amino acid substitutions into rate classes. This approach enhances understanding of protein evolution and aids in evolutionary fingerprinting of genes.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Codon models of evolution interpret genomic selective forces but often assume a single rate for non-synonymous substitutions.
  • Existing models allowing independent substitution rates between amino acid pairs require large alignments for estimation.
  • Subdividing substitution rates into classes based on alignment information offers an alternative but necessitates efficient model search strategies.

Purpose of the Study:

  • To develop a Genetic Algorithm (GA) for efficiently estimating codon models with multiple substitution rate classes.
  • To improve the fit of evolutionary models by incorporating gene and organism-specific amino acid substitution biases.
  • To explore the potential of clustered substitution rates for evolutionary fingerprinting of genes.

Main Methods:

  • Developed a Genetic Algorithm (GA) to assign amino acid substitution pairs into rate classes.
  • Estimated model parameters, including substitution rates, character frequencies, and branch lengths, using maximum likelihood optimization.
  • Applied the GA to empirical alignments to evaluate model performance.

Main Results:

  • The GA-based method demonstrated improved model fit compared to existing codon evolution models.
  • Results indicate that current models are inadequate approximations of protein evolution.
  • Gene and organism-specific multi-rate models incorporating amino acid substitution biases are preferred.

Conclusions:

  • The developed GA provides an efficient strategy for estimating complex codon models.
  • Clustering amino acid substitution rates into classes is biologically informative and useful for evolutionary fingerprinting.
  • This approach advances the accuracy of modeling protein evolution and understanding selective pressures on genomes.