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Multi-objective optimization for designing structurally similar proteins with diverse sequences
Open accessPreprint — published as 10.1080/27660400.2025.2611575 · Paper page
Abstract
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 improves sequence diversity compared to the original ProteinMPNN while maintaining comparable structural similarity.
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Metadata
- Authors
- Akiba, R., Moriwaki, Y., Ishitani, R., & Yoshikawa, N.
- Affiliations
- Institute of Science Tokyo (JP)
- Corresponding author
- Ishitani, Ryuichiro
- Yoshikawa, Naruki
- Venue
- Science and Technology of Advanced Materials Methods
- Publisher
- openRxiv
- Published
- 2025-09-17
- Volume
- 6
- Issue
- 1
- ISSN
- 2766-0400 (ISSN-L)
- OpenAlex
- W7119542277
- Cited by
- 1
- References
- 1
- Citations by year
- 2026: 1
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Science and Technology of Advanced Materials Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Materials Data Repository (cc-by)
- Funders
- Japan Agency for Medical Research and Development
- Japan Society for the Promotion of Science (25K21333)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- protein design, multi-objective optimization, structural similarity, sequence similarity, ProteinMPNN, NSGA-II, sequence diversity, protein structure prediction
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