Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity
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| Format: | Artículo Open Access |
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2025
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| _version_ | 1867019812319789056 |
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| author | Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore |
| author_facet | Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore |
| collection | Wiley Open Access |
| contents | Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore Protein Science AbstractMesothelin (MSLN) is a cell surface glycoprotein overexpressed in many solid tumors, which is known to interact with cancer antigen CA125/MUC16, promoting cancer cell adhesion and metastasis. MSLN has been used as a target of multiple antibody‐based therapeutic strategies, but their efficacy remains limited, potentially due to inherent pharmacokinetics conferred by the structure of antibodies (~150 kDa). To provide an alternative targeting molecule, we engineered a small scaffold protein derived from the tenth domain of human fibronectin type III (Fn3, 12.8 kDa) to bind MSLN with nanomolar affinity as a theranostic agent for MSLN‐positive cancers. In this study, we explored the Fn3–MSLN interaction site through computational modeling and experimentally validated the model through domain‐level and fine epitope mapping. Fn3–MSLN binding was predicted by a consensus approach, comparing multiple protein–protein docking software, the deep‐learning‐based algorithm AlphaFold3, and performing molecular dynamics (MD) simulations. To validate the prediction, full‐length MSLN, single MSLN domains, or combinations of domains were expressed on the yeast surface, and Fn3 binding to displayed MSLN domains was measured by flow cytometry. The employed algorithms predicted two distinct binding modes for Fn3. Overall, experimental data agreed with our in silico prediction resulting from the AlphaFold3 model, confirming that MSLN domains B and C are predominantly involved in the interaction. 10.1002/pro.70263 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/pro.70263 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_pro_70263 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore Protein Science Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity Margherita Piccardi Valeria Butera Ignazio Sardo Stefano Landi Federica Gemignani Giampaolo Barone Angelo Spinello Sarah J. Moore Protein Science AbstractMesothelin (MSLN) is a cell surface glycoprotein overexpressed in many solid tumors, which is known to interact with cancer antigen CA125/MUC16, promoting cancer cell adhesion and metastasis. MSLN has been used as a target of multiple antibody‐based therapeutic strategies, but their efficacy remains limited, potentially due to inherent pharmacokinetics conferred by the structure of antibodies (~150 kDa). To provide an alternative targeting molecule, we engineered a small scaffold protein derived from the tenth domain of human fibronectin type III (Fn3, 12.8 kDa) to bind MSLN with nanomolar affinity as a theranostic agent for MSLN‐positive cancers. In this study, we explored the Fn3–MSLN interaction site through computational modeling and experimentally validated the model through domain‐level and fine epitope mapping. Fn3–MSLN binding was predicted by a consensus approach, comparing multiple protein–protein docking software, the deep‐learning‐based algorithm AlphaFold3, and performing molecular dynamics (MD) simulations. To validate the prediction, full‐length MSLN, single MSLN domains, or combinations of domains were expressed on the yeast surface, and Fn3 binding to displayed MSLN domains was measured by flow cytometry. The employed algorithms predicted two distinct binding modes for Fn3. Overall, experimental data agreed with our in silico prediction resulting from the AlphaFold3 model, confirming that MSLN domains B and C are predominantly involved in the interaction. 10.1002/pro.70263 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity |
| topic | Protein Science |
| url | https://onlinelibrary.wiley.com/doi/10.1002/pro.70263 |