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Related Concept Videos

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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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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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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Related Experiment Video

Updated: Sep 5, 2025

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
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Cluster learning-assisted directed evolution.

Yuchi Qiu1, Jian Hu2,3, Guo-Wei Wei1,3,4

  • 1Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.

Nature Computational Science
|July 11, 2022
PubMed
Summary
This summary is machine-generated.

Cluster Learning-Assisted Directed Evolution (CLADE) accelerates protein engineering by intelligently selecting variants for screening. This machine learning approach significantly improves the discovery of high-fitness proteins compared to random methods.

Keywords:
Protein engineeringclusteringdirected evolutionfitnessmachine learning

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Related Experiment Videos

Last Updated: Sep 5, 2025

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Directed evolution is crucial for optimizing protein properties but is limited by costly screening.
  • Machine learning-assisted directed evolution (MLDE) offers a computational approach to accelerate this process.

Purpose of the Study:

  • Introduce Cluster Learning-Assisted Directed Evolution (CLADE), a novel MLDE framework.
  • Enhance the efficiency and success rate of protein engineering via intelligent variant selection.

Main Methods:

  • CLADE integrates hierarchical unsupervised clustering for targeted subspace sampling.
  • Supervised learning models are employed for accurate prediction and outcome improvement.
  • Sequential screening of variants in combinatorial libraries was performed.

Main Results:

  • CLADE achieved maximal fitness hit rates of 91.0% (GB1) and 34.0% (PhoQ).
  • This represents a substantial improvement over random-sampling-based MLDE (18.6% and 7.2%).
  • Efficient screening of 480 sequences out of 160,000 was demonstrated.

Conclusions:

  • CLADE effectively guides protein engineering by selectively screening variants.
  • The framework significantly enhances the discovery of high-fitness proteins.
  • CLADE reduces experimental burden while improving optimization outcomes.