Small-molecule binding and sensing with a designed protein family
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2023) vom: 02. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, Gyu Rie [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 14.11.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2023.11.01.565201 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364529326 |
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| 500 | |a Date Revised 14.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Pellock, Samuel J |e verfasserin |4 aut | |
| 700 | 1 | |a Norn, Christoffer |e verfasserin |4 aut | |
| 700 | 1 | |a Tischer, Doug |e verfasserin |4 aut | |
| 700 | 1 | |a Dauparas, Justas |e verfasserin |4 aut | |
| 700 | 1 | |a Anischenko, Ivan |e verfasserin |4 aut | |
| 700 | 1 | |a Mercer, Jaron A M |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Alex |e verfasserin |4 aut | |
| 700 | 1 | |a Bera, Asim |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Hannah |e verfasserin |4 aut | |
| 700 | 1 | |a Goreshnik, Inna |e verfasserin |4 aut | |
| 700 | 1 | |a Vafeados, Dionne |e verfasserin |4 aut | |
| 700 | 1 | |a Roullier, Nicole |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Hannah L |e verfasserin |4 aut | |
| 700 | 1 | |a Coventry, Brian |e verfasserin |4 aut | |
| 700 | 1 | |a Haddox, Hugh K |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, David R |e verfasserin |4 aut | |
| 700 | 1 | |a Yeh, Andy Hsien-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Baker, David |e verfasserin |4 aut | |
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