Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and com...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1252166/full |
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| author | Subramanian Parthiban Thandarvalli Vijeesh Thashanamoorthi Gayathri Balamurugan Shanmugaraj Ashutosh Sharma Ramalingam Sathishkumar |
| author_facet | Subramanian Parthiban Thandarvalli Vijeesh Thashanamoorthi Gayathri Balamurugan Shanmugaraj Ashutosh Sharma Ramalingam Sathishkumar |
| author_sort | Subramanian Parthiban |
| collection | DOAJ |
| description | Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market. |
| first_indexed | 2024-03-11T07:06:44Z |
| format | Article |
| id | doaj.art-9b240099f9514078a0a53996cb054998 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T07:06:44Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-9b240099f9514078a0a53996cb0549982023-11-17T08:55:53ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-11-011410.3389/fpls.2023.12521661252166Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticalsSubramanian Parthiban0Thandarvalli Vijeesh1Thashanamoorthi Gayathri2Balamurugan Shanmugaraj3Ashutosh Sharma4Ramalingam Sathishkumar5Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, IndiaPlant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, IndiaPlant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, IndiaPlant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, IndiaTecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, MexicoPlant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, IndiaRecombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.https://www.frontiersin.org/articles/10.3389/fpls.2023.1252166/fullartificial intelligencemolecular pharmingsynthetic biologydeep learningmachine learning |
| spellingShingle | Subramanian Parthiban Thandarvalli Vijeesh Thashanamoorthi Gayathri Balamurugan Shanmugaraj Ashutosh Sharma Ramalingam Sathishkumar Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals Frontiers in Plant Science artificial intelligence molecular pharming synthetic biology deep learning machine learning |
| title | Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals |
| title_full | Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals |
| title_fullStr | Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals |
| title_full_unstemmed | Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals |
| title_short | Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals |
| title_sort | artificial intelligence driven systems engineering for next generation plant derived biopharmaceuticals |
| topic | artificial intelligence molecular pharming synthetic biology deep learning machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1252166/full |
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