Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants
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Abstract
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardization, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid probabilities inside the Rosetta software framework that allows for the side-by-side comparison of these models. Subsequently, existing protein fitness landscapes were used to benchmark novel ML methods in realistic protein design settings. We focused on the traditional problems of protein design: sampling and scoring. A major finding of our study is that ML approaches are better at purging the sampling space from deleterious mutations. Nevertheless, scoring resulting mutations without model fine-tuning showed no clear improvement over scoring with Rosetta. We conclude that ML now complements, rather than replaces, biophysical methods in protein design.
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Flagged first, then by citations
- Authors
- Ertelt, M., Moretti, R., Meiler, J., & Schoeder, C. T.
- Affiliations
- Center for Scalable Data Analytics and Artificial Intelligence (DE)
- Vanderbilt University (US)
- Corresponding author
- Schoeder, Clara T.
- Venue
- Science Advances
- Published
- 2025-02-14
- Volume
- 11
- Issue
- 7
- Pages
- eadr7338-eadr7338
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC11817935
- OpenAlex
- W4407394592
- Cited by
- 15
- References
- 14
- Citations by year
- 2026: 5 · 2025: 10
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Science Advances (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- computational protein design, protein fitness landscapes, Rosetta, deleterious mutations, benchmarking, fine-tuning
- MeSH terms
- Machine Learning, Supervised Machine Learning, Algorithms, Models, Molecular, Mutation, Proteins, Software, Protein Engineering, Computational Biology
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