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Optimizing enzyme inhibition analysis: precise estimation with a single inhibitor concentration

Author

Listed:
  • Hyeong Jun Jang

    (KAIST
    Institute for Basic Science)

  • Yun Min Song

    (Institute for Basic Science
    KAIST)

  • Jang Su Jeon

    (Chungnam National University)

  • Hwi-yeol Yun

    (Chungnam National University
    Chungnam National University)

  • Sang Kyum Kim

    (Chungnam National University)

  • Jae Kyoung Kim

    (Institute for Basic Science
    KAIST
    Korea University)

Abstract

Enzyme inhibition analysis is essential in drug development and food processing, necessitating precise estimation of inhibition constants. Traditionally, these constants are estimated through experiments using multiple substrate and inhibitor concentrations, but inconsistencies across studies highlight a need for a more systematic approach to set experimental designs across all types of enzyme inhibition. Here, we address this by analyzing the error landscape of estimations in various experimental designs. We find that nearly half of the conventional data is dispensable and even introduces bias. Instead, by incorporating the relationship between IC50 and inhibition constants into the fitting process, we find that using a single inhibitor concentration greater than IC50 suffices for precise estimation. This IC50-based optimal approach, which we name 50-BOA, substantially reduces (>75%) the number of experiments required while ensuring precision and accuracy. Additionally, we provide a user-friendly package that implements the 50-BOA.

Suggested Citation

  • Hyeong Jun Jang & Yun Min Song & Jang Su Jeon & Hwi-yeol Yun & Sang Kyum Kim & Jae Kyoung Kim, 2025. "Optimizing enzyme inhibition analysis: precise estimation with a single inhibitor concentration," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60468-z
    DOI: 10.1038/s41467-025-60468-z
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    References listed on IDEAS

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    1. Seolah Shin & Seok Joo Chae & Seunggyu Lee & Jae Kyoung Kim, 2024. "Beyond homogeneity: Assessing the validity of the Michaelis–Menten rate law in spatially heterogeneous environments," PLOS Computational Biology, Public Library of Science, vol. 20(6), pages 1-22, June.
    2. Pedersen, Morten Gram & Bersani, Alberto M. & Bersani, Enrico & Cortese, Giuliana, 2008. "The total quasi-steady-state approximation for complex enzyme reactions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(4), pages 1010-1019.
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