The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Transfer learning to leverage larger datasets for improved prediction of protein stability changes
Open accessPublished version of a preprint
Abstract
Amino acid mutations that lower a protein’s thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein’s amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.
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Cites
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Transfer learning improves the prediction of ligand binding sites at protein–membrane interfaces
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Metadata
- Authors
- Dieckhaus, H., Brocidiacono, M., Randolph, N. Z., & Kuhlman, B.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Kuhlman, Brian
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2024-01-29
- Volume
- 121
- Issue
- 6
- Pages
- e2314853121-e2314853121
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC10861915
- OpenAlex
- W4391308485
- Cited by
- 205
- References
- 47
- Citations by year
- 2026: 104 · 2025: 74 · 2024: 25
- FWCI
- 27.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nd)
- Funders
- HHS | NIH | National Institute of General Medical Sciences (R35GM131923)
- NSF | EDU | Division of Graduate Education (DGE-2040435)
- 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
- protein stability prediction, transfer learning, ProteinMPNN, point mutations, protein thermodynamic stability, deep neural network, protein design, learned features, engineered proteins
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