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説明:

Recent advances in artificial intelligence technologies have accelerated the development of computational protein design techniques. Although the structure of the designed proteins has been the primary focus, diversity of designed amino acid sequences is another important aspect of protein design. To address the trade-off between reducing sequence similarity and improving structural similarity, simultaneous optimization of these two objectives can be effective. We present a method that integrates ProteinMPNN with the multi-objective optimization algorithm NSGA-II to design proteins that retain high structural similarity to a target while exhibiting low sequence similarity. Using Top7 as a reference protein, we demonstrate that our approach can design proteins with lower sequence similarity to the reference compared to the original ProteinMPNN, while maintaining comparable structural similarity.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Protein design, multi-objective optimization, generative AI

刊行年月日: 2026-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 6 2611575

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6136

公開URL: https://doi.org/10.1080/27660400.2025.2611575

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更新時刻: 2026-02-03 15:05:41 +0900

MDRでの公開時刻: 2026-01-19 12:21:35 +0900