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Peng, Y. Directed Evolution. Encyclopedia. Available online: https://encyclopedia.pub/entry/60825 (accessed on 12 November 2026).
Peng Y. Directed Evolution. Encyclopedia. Available at: https://encyclopedia.pub/entry/60825. Accessed November 12, 2026.
Peng, Yu. "Directed Evolution" Encyclopedia, https://encyclopedia.pub/entry/60825 (accessed November 12, 2026).
Peng, Y. (2026, October 11). Directed Evolution. In Encyclopedia. https://encyclopedia.pub/entry/60825
Peng, Yu. "Directed Evolution." Encyclopedia. Web. 11 October, 2026.

Directed Evolution

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Directed evolution is an iterative, selection-driven process that generates heritable molecular diversity and enriches variants according to a defined functional criterion [1][2]. In protein engineering, a campaign typically begins with a parent gene or a library of genes encoding protein variants, and the encoded proteins are evaluated by screening or selection [2]. Molecular diversity is introduced through mutagenesis and recombination, and sequences associated with the selected phenotype are recovered to seed the next round of diversification [1][2]. This iterative cycle requires a recoverable linkage between the measured or selected phenotype and its encoding nucleic acid sequence, so that functional information can be translated back into sequence information [3]. The outcome of a directed-evolution campaign is therefore determined by the starting sequence population, the accessible variation, the assay and selection conditions, and the number of iterative rounds; the final sequence distribution reflects both the variants generated and the functional constraints imposed during evaluation [4].

genetic diversity selection protein variants iterative evolution
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References

  1. David Medina-Ortiz; Ashkan Khalifeh; Hoda Anvari-Kazemabad; Mehdi D. Davari; Interpretable and Explainable Predictive Machine Learning Models for Data-driven Protein Engineering. Biotechnol. Adv. 2024, 79, 108495. [CrossRef]
  2. Frances H. Arnold; Directed Evolution: Bringing New Chemistry to Life. Angew. Chem. Int. Ed. 2017, 57, 4143-4148. [CrossRef]
  3. David Medina-Ortiz; Ashkan Khalifeh; Hoda Anvari-Kazemabad; Mehdi D. Davari; Interpretable and Explainable Predictive Machine Learning Models for Data-driven Protein Engineering. Biotechnol. Adv. 2024, 79, 108495. [CrossRef]
  4. Alexandre Barrozo; Rok Borstnar; Gaël Marloie; Shina Caroline Lynn Kamerlin; Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution. Int. J. Mol. Sci. 2012, 13, 12428-12460. [CrossRef]
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Update Date: 24 Oct 2026
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