The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Multi-objective optimization for designing structurally similar proteins with dissimilar sequences
Open accessPublished version of a preprint
Abstract
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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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325
Robust deep learning–based protein sequence design using ProteinMPNN
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De novo protein design by deep network hallucination
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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
- Informa UK Limited
- Published
- 2026-02-02
- Volume
- 6
- Issue
- 1
- ISSN
- 2766-0400 (ISSN-L)
- OpenAlex
- W7119542277
- Cited by
- 1
- References
- 6
- 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
- Research Support Project for Life Science and Drug Discovery (Basis for Supporting Innovative Drug Discovery and Life Science Research (JP25ama121012)
- Japan Agency for Medical Research and Development
- JSPS KAKENHI (25H01570, JP25K21333)
- Institute of Science Tokyo 2025
- Multilayered Stress Diseases (JPMXP1323015483)
- 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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