Improving Protein Expression, Stability, and Function with ProteinMPNN
Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:146 |
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| Enthalten in: |
Journal of the American Chemical Society - 146(2024), 3 vom: 24. Jan., Seite 2054-2061 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sumida, Kiera H [VerfasserIn] |
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| Links: |
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| Themen: |
EC 3.4.- |
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| Anmerkungen: |
Date Completed 25.01.2024 Date Revised 06.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1021/jacs.3c10941 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM366849018 |
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| 520 | |a Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins | ||
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| 700 | 1 | |a Pellock, Samuel J |e verfasserin |4 aut | |
| 700 | 1 | |a Wicky, Basile I M |e verfasserin |4 aut | |
| 700 | 1 | |a Milles, Lukas F |e verfasserin |4 aut | |
| 700 | 1 | |a Dauparas, Justas |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jue |e verfasserin |4 aut | |
| 700 | 1 | |a Kipnis, Yakov |e verfasserin |4 aut | |
| 700 | 1 | |a Jameson, Noel |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Alex |e verfasserin |4 aut | |
| 700 | 1 | |a De La Cruz, Joshmyn |e verfasserin |4 aut | |
| 700 | 1 | |a Sankaran, Banumathi |e verfasserin |4 aut | |
| 700 | 1 | |a Bera, Asim K |e verfasserin |4 aut | |
| 700 | 1 | |a Jiménez-Osés, Gonzalo |e verfasserin |4 aut | |
| 700 | 1 | |a Baker, David |e verfasserin |4 aut | |
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