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ORIGINAL RESEARCH article

Front. Mol. Biosci.
Sec. Nanobiotechnology
Volume 11 - 2024 | doi: 10.3389/fmolb.2024.1439259
This article is part of the Research Topic Microdroplets and the Art of Directed Evolution: Pitfalls and Promising Advances View all articles

Navigating directed evolution efficiently: optimizing selection conditions and selection output analysis

Provisionally accepted
  • Laboratory of Medicinal Chemistry, Department of Pharmaceutical and Pharmacological Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium

The final, formatted version of the article will be published soon.

    Directed evolution is a powerful tool that can bypass gaps in our understanding of the sequencefunction relationship of proteins and still isolate variants with desired activities, properties, and substrate specificities. The rise of directed evolution platforms for polymerase engineering has accelerated the isolation of xenobiotic nucleic acids (XNAs) synthetases and reverse transcriptases (RTs) capable of processing a wide array of unnatural XNAs which have numerous therapeutic and biotechnological applications. Still, the current generation of XNA polymerases functions with significantly lower efficiency than the natural counterparts and retains a significant level of DNA polymerase activity which limits their in vivo applications. Although directed evolution approaches are continuously being developed and implemented to improve XNA polymerase engineering, the field lacks an in-depth analysis of the effect of selection parameters, library construction biases and sampling biases. Focusing on the directed evolution pipeline for DNA and XNA polymerase engineering, this work sets out a method for understanding the impact of selection conditions on selection success and efficiency. We also explore the influence of selection conditions on fidelity at the population and individual mutant level. Additionally, we explore the sequencing coverage requirements in directed evolution experiments, which differ from genome assembly and otheromics approaches. This analysis allowed us to identify the sequencing coverage threshold for the accurate and precise identification of significantly enriched mutants. Overall, this study introduces a robust methodology for optimizing selection protocols, which effectively streamlines selection processes by employing small libraries and cost-effective NGS sequencing. It provides valuable insights into critical considerations, thereby enhancing the overall effectiveness and efficiency of directed evolution strategies applicable to enzymes other than the ones considered here.

    Keywords: Directed Evolution, Design of Experiments, Polymerase engineering, Fitness Landscape, Next-Generation Sequencing (NGS) data analysis

    Received: 27 May 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Handal-Marquez, Nguyen and Pinheiro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Vitor B. Pinheiro, Laboratory of Medicinal Chemistry, Department of Pharmaceutical and Pharmacological Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.