Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs
The unnatural amino acid (UAA) incorporation technique through genetic code expansion has been extensively used in protein engineering for the last two decades. Mutations into UAAs offer more dimensions to tune protein structures and functions. However, the huge library of optional UAAs and various...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702200397X |
| _version_ | 1797978154147512320 |
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| author | Haoran Zhang Zhetao Zheng Liangzhen Dong Ningning Shi Yuelin Yang Hongmin Chen Yuxuan Shen Qing Xia |
| author_facet | Haoran Zhang Zhetao Zheng Liangzhen Dong Ningning Shi Yuelin Yang Hongmin Chen Yuxuan Shen Qing Xia |
| author_sort | Haoran Zhang |
| collection | DOAJ |
| description | The unnatural amino acid (UAA) incorporation technique through genetic code expansion has been extensively used in protein engineering for the last two decades. Mutations into UAAs offer more dimensions to tune protein structures and functions. However, the huge library of optional UAAs and various circumstances of mutation sites on different proteins urge rational UAA incorporations guided by artificial intelligence. Here we collected existing experimental proofs of UAA-incorporated proteins in literature and established a database of known UAA substitution sites. By program designing and machine learning on the database, we showed that UAA incorporations into proteins are predictable by the observed evolutional, steric and physiochemical factors. Based on the predicted probability of successful UAA substitutions, we tested the model performance using literature-reported and freshly-designed experimental proofs, and demonstrated its potential in screening UAA-incorporated proteins. This work expands structure-based computational biology and virtual screening to UAA-incorporated proteins, and offers a useful tool to automate the rational design of proteins with any UAA. |
| first_indexed | 2024-04-11T05:19:33Z |
| format | Article |
| id | doaj.art-707a7d7488aa49618032d0206996aac1 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:33Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-707a7d7488aa49618032d0206996aac12022-12-24T04:54:16ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012049304941Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofsHaoran Zhang0Zhetao Zheng1Liangzhen Dong2Ningning Shi3Yuelin Yang4Hongmin Chen5Yuxuan Shen6Qing Xia7State Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaState Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaCorresponding author.; State Key Laboratory of Natural and Biomimetic Drugs, Department of Chemical Biology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, ChinaThe unnatural amino acid (UAA) incorporation technique through genetic code expansion has been extensively used in protein engineering for the last two decades. Mutations into UAAs offer more dimensions to tune protein structures and functions. However, the huge library of optional UAAs and various circumstances of mutation sites on different proteins urge rational UAA incorporations guided by artificial intelligence. Here we collected existing experimental proofs of UAA-incorporated proteins in literature and established a database of known UAA substitution sites. By program designing and machine learning on the database, we showed that UAA incorporations into proteins are predictable by the observed evolutional, steric and physiochemical factors. Based on the predicted probability of successful UAA substitutions, we tested the model performance using literature-reported and freshly-designed experimental proofs, and demonstrated its potential in screening UAA-incorporated proteins. This work expands structure-based computational biology and virtual screening to UAA-incorporated proteins, and offers a useful tool to automate the rational design of proteins with any UAA.http://www.sciencedirect.com/science/article/pii/S200103702200397XProtein designUnnatural amino acid incorporationGenetic code expansionMachine learningVirtual screening |
| spellingShingle | Haoran Zhang Zhetao Zheng Liangzhen Dong Ningning Shi Yuelin Yang Hongmin Chen Yuxuan Shen Qing Xia Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs Computational and Structural Biotechnology Journal Protein design Unnatural amino acid incorporation Genetic code expansion Machine learning Virtual screening |
| title | Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| title_full | Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| title_fullStr | Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| title_full_unstemmed | Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| title_short | Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| title_sort | rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs |
| topic | Protein design Unnatural amino acid incorporation Genetic code expansion Machine learning Virtual screening |
| url | http://www.sciencedirect.com/science/article/pii/S200103702200397X |
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