McKay, C.E., Deans, M., Connor, J. et al. (4 more authors) (2025) Employing deep mutational scanning in the Escherichia coli periplasm to decode the thermodynamic landscape for amyloid formation. Proceedings of the National Academy of Sciences of the United States of America, 122 (38). e2516165122. ISSN: 0027-8424
Abstract
Deep mutational scanning (DMS) assays provide a powerful method to generate large-scale datasets essential for advancing AI-driven predictions in biology. The tripartite β-lactamase assay (TPBLA), in which a protein of interest is inserted between two domains of β-lactamase, has previously been reported as capable of detecting and quantitating the aggregation of proteins and biologics in the oxidizing periplasm of Escherichia coli and used as a platform for identifying small molecule inhibitors of aggregation. Here, we repurpose the TPBLA into a high-throughput DMS platform. We validate this format using a single-site saturation library of the intrinsically disordered peptide Aβ42, linked to Alzheimer’s disease, demonstrating strong agreement between observed variant fitness scores and variant behavior using our previously reported low-throughput TPBLA. The results of DMS revealed variant fitness scores that correlate with known amyloid-promoting regions. An in silico approach using FoldX-derived per-residue thermodynamic stability confirmed that the TPBLA reports on amyloid fibril stability. In vitro experiments support this finding, showing a strong correlation between variant fitness scores and the critical concentration of amyloid formation. Machine learning using the DMS dataset identified β‐sheet propensity and polarity as primary drivers of variant fitness scores. The derived model is also able to predict thermodynamically stabilizing regions in other amyloid systems, underscoring its generalizability. Collectively, our results demonstrate the TPBLA as a versatile platform for generating robust datasets to advance predictive modeling and to inform the design of aggregation‐resistant proteins.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 the Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | deep mutational scanning; amyloid; Aβ42; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) |
Date Deposited: | 07 Oct 2025 11:15 |
Last Modified: | 07 Oct 2025 11:15 |
Status: | Published |
Publisher: | Proceedings of the National Academy of Sciences |
Identification Number: | 10.1073/pnas.2516165122 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:232545 |