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Related Concept Videos

Amino acids03:42

Amino acids

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Amino acids are the monomers that comprise proteins. Each amino acid has the same fundamental structure, which consists of a central carbon atom, or the alpha (α) carbon, bonded to an amino group (NH2), a carboxyl group (COOH), and to a hydrogen atom. Every amino acid also has another atom or group of atoms bonded to the central atom known as the R group. There are 20 common amino acids present in proteins, each with a different R group. Variation in the amino acid sequence is responsible for...
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation07:15

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Amino acid-level signal-to-noise analysis determines the prevalence of genetic variation at a given amino acid position normalized to background genetic variation of a given population. This allows for identification of variant "hotspots" within a protein sequence (signal) that rises above the frequency of rare variants found in a population...
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Here, a procedure to selectively activate a neuronal protein with a short pulse of light by genetically encoding a photo-reactive unnatural amino acid into a target neuronal protein expressed in neurons in culture or in vivo is...
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Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

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Here, we describe a protocol for the optimization and parameterization of amino acid residues modified with reactive carbonyl species, adaptable to protein systems. The protocol steps include structure design and optimization, charge assignments, parameter construction, and preparation of protein...
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The purpose of this protocol is to efficiently generate and curate small-molecule structure libraries using open-source software.
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Updated: Jan 19, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Determining amino acid scores of the genetic code table: Complementarity, structure, function and evolution.

Nikola Štambuk1, Paško Konjevoda2

  • 1Center for Nuclear Magnetic Resonance, Ruđer Bošković Institute, Bijenička cesta 54, HR-10000, Zagreb, Croatia.

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Summary

The Standard Genetic Code

Keywords:
ComplementarityEvolutionFunctionGenetic codeHydrophobicityLipophilicityRNAScoringStructure

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Optical Control of a Neuronal Protein Using a Genetically Encoded Unnatural Amino Acid in Neurons
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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • The Standard Genetic Code (SGC) dictates the translation of nucleotide sequences into amino acids.
  • Understanding the relationship between codon structure and amino acid properties is crucial for deciphering protein function and evolution.

Purpose of the Study:

  • To investigate the three-dimensional arrangement of codons in the SGC table.
  • To determine how hierarchical base partitions correlate with amino acid physicochemical properties.
  • To explore the information content of codon bases regarding protein characteristics.

Main Methods:

  • Analysis of all 24 hierarchical base partitions of the SGC.
  • Determination of amino acid scores based on codon hierarchies and sub-tables (1st, 2nd, and 3rd positions).
  • Correlation of nucleotide hierarchies with amino acid hydrophobicity, lipophilicity, polarity, and accessible surface area.

Main Results:

  • The second base of the codon significantly influences hydrophobicity and lipophilicity.
  • Specific nucleotide hierarchies at the second base strongly correlate with amino acid hydrophobicity, polarity, lipophilicity, and surface area.
  • No significant association was found for the 1st and 3rd codon bases with these physicochemical properties.
  • Amino acid information derived from base scores aligns with physicochemical properties, independent of temperature variations.

Conclusions:

  • The second base of the codon carries the majority of information regarding amino acid physicochemical properties.
  • Codon-based amino acid information can model protein structure, function, and evolution.
  • This approach offers an alternative to using only amino acid physicochemical properties for sequence analysis.