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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Mutations01:35

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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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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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Mutations in Microorganisms

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Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
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Related Experiment Video

Updated: Jan 12, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Unlocking Protein Evolution Insights: Efficient and Interpretable Mutational Effect Predictions with GEMME.

A Carbone1,2, E Laine3,4, G Lombardi3

  • 1Sorbonne Université, CNRS, IBPS, Computational, Quantitative and Synthetic Biology Department - UMR 7238, Paris, France. alessandra.carbone@lip6.fr.

Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2025
PubMed
Summary
This summary is machine-generated.

The Global Epistatic Model for predicting Mutational Effects (GEMME) reconstructs protein landscapes from sequence data. Recent advancements enhance its accuracy and applications, offering deeper insights into protein evolution and function.

Keywords:
EvolutionMutationMutational effectProteinSequence

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Area of Science:

  • Computational biology
  • Protein engineering
  • Evolutionary biology

Background:

  • The Global Epistatic Model for predicting Mutational Effects (GEMME) is a computational tool.
  • GEMME currently reconstructs protein mutational landscapes using only sequence data.

Purpose of the Study:

  • To explore the broader biological questions GEMME can address beyond landscape reconstruction.
  • To guide users in maximizing GEMME's utility.
  • To present recent advancements enhancing GEMME's predictive accuracy and capabilities.

Main Methods:

  • Utilizing sequence data for protein mutational landscape reconstruction.
  • Integrating structural and allele frequency data with sequence data.
  • Providing examples to demonstrate GEMME's applications.

Main Results:

  • GEMME can address a wider range of biological questions than previously recognized.
  • Recent advancements improve GEMME's predictive accuracy.
  • Integration of diverse data types expands GEMME's analytical power.

Conclusions:

  • GEMME offers deeper insights into protein evolution and function.
  • Enhanced GEMME versions can answer novel biological questions.
  • The method is valuable for diverse applications in computational biology and protein engineering.