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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Protein design, multi-objective optimization, generative AI

Date published: 2026-12-31

Publisher: Taylor & Francis

Journal:

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

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1080/27660400.2025.2611575

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Updated at: 2026-02-03 15:05:41 +0900

Published on MDR: 2026-01-19 12:21:35 +0900