Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies
Protein design plays a key role in our efforts to work out how genetic coding began. That effort entails urzymes. Urzymes are small, conserved excerpts from full-length aminoacyl-tRNA synthetases that remain active. Urzymes require design to connect disjoint pieces and repair naked nonpolar patches...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC and ACA
2025-03-01
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| Series: | Structural Dynamics |
| Online Access: | http://dx.doi.org/10.1063/4.0000294 |
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| author | Sourav Kumar Patra Nicholas Randolph Brian Kuhlman Henry Dieckhaus Laurie Betts Jordan Douglas Peter R. Wills Charles W. Carter Jr. |
| author_facet | Sourav Kumar Patra Nicholas Randolph Brian Kuhlman Henry Dieckhaus Laurie Betts Jordan Douglas Peter R. Wills Charles W. Carter Jr. |
| author_sort | Sourav Kumar Patra |
| collection | DOAJ |
| description | Protein design plays a key role in our efforts to work out how genetic coding began. That effort entails urzymes. Urzymes are small, conserved excerpts from full-length aminoacyl-tRNA synthetases that remain active. Urzymes require design to connect disjoint pieces and repair naked nonpolar patches created by removing large domains. Rosetta allowed us to create the first urzymes, but those urzymes were only sparingly soluble. We could measure activity, but it was hard to concentrate those samples to levels required for structural biology. Here, we used the deep learning algorithms ProteinMPNN and AlphaFold2 to redesign a set of optimized LeuAC urzymes derived from leucyl-tRNA synthetase. We select a balanced, representative subset of eight variants for testing using principal component analysis. Most tested variants are much more soluble than the original LeuAC. They also span a range of catalytic proficiency and amino acid specificity. The data enable detailed statistical analyses of the sources of both solubility and specificity. In that way, we show how to begin to unwrap the elements of protein chemistry that were hidden within the neural networks. Deep learning networks have thus helped us surmount several vexing obstacles to further investigations into the nature of ancestral proteins. Finally, we discuss how the eight variants might resemble a sample drawn from a population similar to one subject to natural selection. |
| format | Article |
| id | doaj-art-d47d9b2150b448dd8e8240bdabe79d0d |
| institution | OA Journals |
| issn | 2329-7778 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIP Publishing LLC and ACA |
| record_format | Article |
| series | Structural Dynamics |
| spelling | doaj-art-d47d9b2150b448dd8e8240bdabe79d0d2025-08-20T01:48:15ZengAIP Publishing LLC and ACAStructural Dynamics2329-77782025-03-01122024701024701-1510.1063/4.0000294Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispeciesSourav Kumar Patra0Nicholas Randolph1Brian Kuhlman2Henry Dieckhaus3Laurie Betts4Jordan Douglas5Peter R. Wills6Charles W. Carter Jr.7Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USADepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USADepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USADivision of Chemical Biology and Medicinal Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7355, USADepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USADepartment of Physics, University of Auckland, Auckland, New ZealandDepartment of Physics, University of Auckland, Auckland, New ZealandDepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USAProtein design plays a key role in our efforts to work out how genetic coding began. That effort entails urzymes. Urzymes are small, conserved excerpts from full-length aminoacyl-tRNA synthetases that remain active. Urzymes require design to connect disjoint pieces and repair naked nonpolar patches created by removing large domains. Rosetta allowed us to create the first urzymes, but those urzymes were only sparingly soluble. We could measure activity, but it was hard to concentrate those samples to levels required for structural biology. Here, we used the deep learning algorithms ProteinMPNN and AlphaFold2 to redesign a set of optimized LeuAC urzymes derived from leucyl-tRNA synthetase. We select a balanced, representative subset of eight variants for testing using principal component analysis. Most tested variants are much more soluble than the original LeuAC. They also span a range of catalytic proficiency and amino acid specificity. The data enable detailed statistical analyses of the sources of both solubility and specificity. In that way, we show how to begin to unwrap the elements of protein chemistry that were hidden within the neural networks. Deep learning networks have thus helped us surmount several vexing obstacles to further investigations into the nature of ancestral proteins. Finally, we discuss how the eight variants might resemble a sample drawn from a population similar to one subject to natural selection.http://dx.doi.org/10.1063/4.0000294 |
| spellingShingle | Sourav Kumar Patra Nicholas Randolph Brian Kuhlman Henry Dieckhaus Laurie Betts Jordan Douglas Peter R. Wills Charles W. Carter Jr. Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies Structural Dynamics |
| title | Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies |
| title_full | Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies |
| title_fullStr | Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies |
| title_full_unstemmed | Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies |
| title_short | Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies |
| title_sort | aminoacyl trna synthetase urzymes optimized by deep learning behave as a quasispecies |
| url | http://dx.doi.org/10.1063/4.0000294 |
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